diff --git a/.gitignore b/.gitignore
index 92b9dd7..69da37c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,7 @@
myenv
-.keras
\ No newline at end of file
+# Abaikan direktori atau file yang mengandung 'keras' di mana saja dalam namanya
+*keras*
+**/*keras*
+
+# Abaikan semua file dengan ekstensi .pkl
+*.pkl
diff --git a/NER/ner_bilstm_model.keras b/NER/ner_bilstm_model.keras
deleted file mode 100644
index 9bcaf2a..0000000
Binary files a/NER/ner_bilstm_model.keras and /dev/null differ
diff --git a/NER/tag2idx.pkl b/NER/tag2idx.pkl
deleted file mode 100644
index 0bba3d3..0000000
Binary files a/NER/tag2idx.pkl and /dev/null differ
diff --git a/NER/word2idx.pkl b/NER/word2idx.pkl
deleted file mode 100644
index 87c0684..0000000
Binary files a/NER/word2idx.pkl and /dev/null differ
diff --git a/QC/normalize_dataset.json b/QC/normalize_dataset.json
index 6ef1564..953e975 100644
--- a/QC/normalize_dataset.json
+++ b/QC/normalize_dataset.json
@@ -12217,5 +12217,1510 @@
"question": ["Dari", "mana", "manusia", "Wajak", "berevolusi", "?"],
"answer": ["mungkin", "tahapan", "Homo", "neanderthalensis"],
"type": "ftb"
+ },
+ {
+ "tokens": [
+ "Zaman",
+ "praaksara",
+ "berakhir",
+ "ketika",
+ "manusia",
+ "mulai",
+ "mengenal",
+ "tulisan",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "O"
+ ],
+ "question": ["Kapan", "zaman", "praaksara", "berakhir", "?"],
+ "answer": ["ketika", "manusia", "mulai", "mengenal", "tulisan"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Kutai",
+ "berada",
+ "di",
+ "Muara",
+ "Kaman",
+ ",",
+ "Kalimantan",
+ "Timur",
+ "."
+ ],
+ "ner": [
+ "B-ORG",
+ "I-ORG",
+ "O",
+ "O",
+ "B-LOC",
+ "I-LOC",
+ "O",
+ "B-LOC",
+ "I-LOC",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Di", "mana", "letak", "Kerajaan", "Kutai", "?"],
+ "answer": ["Muara", "Kaman", ",", "Kalimantan", "Timur"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Ilmu",
+ "sejarah",
+ "mempelajari",
+ "peristiwa",
+ "yang",
+ "terjadi",
+ "di",
+ "masa",
+ "lampau",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "B-TIME", "I-TIME", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Apa", "yang", "dipelajari", "dalam", "ilmu", "sejarah", "?"],
+ "answer": ["peristiwa", "yang", "terjadi", "di", "masa", "lampau"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Saya",
+ "mampu",
+ "menjelaskan",
+ "tentang",
+ "konsep",
+ "ilmu",
+ "sejarah",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "V", "ARG1", "ARG1", "ARG1", "ARG1", "ARG1", "O"],
+ "question": [],
+ "answer": [],
+ "type": "none"
+ },
+ {
+ "tokens": [
+ "Manusia",
+ "purba",
+ "hidup",
+ "berpindah-pindah",
+ "untuk",
+ "mencari",
+ "makanan",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "ARG0", "V", "ARGM-MNR", "ARGM-CAU", "ARG1", "ARG1", "O"],
+ "question": [
+ "Mengapa",
+ "manusia",
+ "purba",
+ "hidup",
+ "berpindah-pindah",
+ "?"
+ ],
+ "answer": ["mencari", "makanan"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Sriwijaya",
+ "berkembang",
+ "sebagai",
+ "pusat",
+ "pembelajaran",
+ "agama",
+ "Buddha",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "O", "O", "O", "O", "O", "B-EVENT", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "O"
+ ],
+ "question": [
+ "Kerajaan",
+ "mana",
+ "yang",
+ "berkembang",
+ "sebagai",
+ "pusat",
+ "pembelajaran",
+ "agama",
+ "Buddha",
+ "?"
+ ],
+ "answer": ["Sriwijaya"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "VOC",
+ "didirikan",
+ "pada",
+ "tahun",
+ "1602",
+ "oleh",
+ "Belanda",
+ "."
+ ],
+ "ner": ["B-ORG", "O", "O", "O", "B-DATE", "O", "B-LOC", "O"],
+ "srl": [
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARG0",
+ "ARG0",
+ "O"
+ ],
+ "question": ["Kapan", "VOC", "didirikan", "?"],
+ "answer": ["1602"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Letusan",
+ "Gunung",
+ "Krakatau",
+ "tahun",
+ "1883",
+ "menyebabkan",
+ "tsunami",
+ "besar",
+ "."
+ ],
+ "ner": ["O", "B-LOC", "I-LOC", "B-DATE", "I-DATE", "O", "O", "O", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "V",
+ "ARG1",
+ "ARG1",
+ "O"
+ ],
+ "question": [
+ "Apa",
+ "akibat",
+ "dari",
+ "letusan",
+ "Gunung",
+ "Krakatau",
+ "tahun",
+ "1883",
+ "?"
+ ],
+ "answer": ["tsunami", "besar"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Candi",
+ "Borobudur",
+ "dibangun",
+ "pada",
+ "abad",
+ "ke-8",
+ "oleh",
+ "dinasti",
+ "Syailendra",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "O",
+ "O",
+ "B-ORG",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "O"
+ ],
+ "question": ["Siapa", "yang", "membangun", "Candi", "Borobudur", "?"],
+ "answer": ["Syailendra"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Sumpah",
+ "Pemuda",
+ "diikrarkan",
+ "pada",
+ "tanggal",
+ "28",
+ "Oktober",
+ "1928",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Kapan", "Sumpah", "Pemuda", "diikrarkan", "?"],
+ "answer": ["28", "Oktober", "1928"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Indonesia",
+ "merdeka",
+ "pada",
+ "tanggal",
+ "17",
+ "Agustus",
+ "1945",
+ "."
+ ],
+ "ner": ["B-LOC", "O", "O", "O", "B-DATE", "I-DATE", "I-DATE", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Kapan", "Indonesia", "merdeka", "?"],
+ "answer": ["17", "Agustus", "1945"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Pangeran",
+ "Diponegoro",
+ "memimpin",
+ "perang",
+ "melawan",
+ "Belanda",
+ "selama",
+ "lima",
+ "tahun",
+ "."
+ ],
+ "ner": [
+ "B-PER",
+ "I-PER",
+ "O",
+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "B-TIME",
+ "I-TIME",
+ "O"
+ ],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Berapa",
+ "lama",
+ "Pangeran",
+ "Diponegoro",
+ "memimpin",
+ "perang",
+ "?"
+ ],
+ "answer": ["lima", "tahun"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Majapahit",
+ "mencapai",
+ "puncak",
+ "kejayaan",
+ "pada",
+ "masa",
+ "Hayam",
+ "Wuruk",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "O", "O", "O", "O", "O", "B-PER", "I-PER", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Siapa",
+ "raja",
+ "pada",
+ "masa",
+ "kejayaan",
+ "Kerajaan",
+ "Majapahit",
+ "?"
+ ],
+ "answer": ["Hayam", "Wuruk"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Tarumanegara",
+ "dikenal",
+ "melalui",
+ "prasasti",
+ "yang",
+ "ditemukan",
+ "di",
+ "Bogor",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "O", "O", "O", "O", "O", "O", "B-LOC", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Dimana",
+ "ditemukan",
+ "prasasti",
+ "Kerajaan",
+ "Tarumanegara",
+ "?"
+ ],
+ "answer": ["Bogor"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Indonesia",
+ "terletak",
+ "di",
+ "antara",
+ "dua",
+ "benua",
+ "dan",
+ "dua",
+ "samudra",
+ "."
+ ],
+ "ner": ["B-LOC", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": [
+ "ARG1",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Indonesia", "terletak", "di", "antara", "apa", "saja", "?"],
+ "answer": ["dua", "benua", "dua", "samudra"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Mataram",
+ "Kuno",
+ "berkembang",
+ "di",
+ "wilayah",
+ "Jawa",
+ "Tengah",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "I-ORG", "O", "O", "O", "B-LOC", "I-LOC", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Di",
+ "mana",
+ "Kerajaan",
+ "Mataram",
+ "Kuno",
+ "berkembang",
+ "?"
+ ],
+ "answer": ["Jawa", "Tengah"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Kediri",
+ "dikenal",
+ "melalui",
+ "kitab",
+ "karya",
+ "Empu",
+ "Sedah",
+ "dan",
+ "Empu",
+ "Panuluh",
+ "."
+ ],
+ "ner": [
+ "B-ORG",
+ "I-ORG",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "O"
+ ],
+ "question": [
+ "Siapa",
+ "penulis",
+ "kitab",
+ "pada",
+ "masa",
+ "Kerajaan",
+ "Kediri",
+ "?"
+ ],
+ "answer": ["Empu", "Sedah", "Empu", "Panuluh"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Singhasari",
+ "berdiri",
+ "sebelum",
+ "kerajaan",
+ "Majapahit",
+ "berkembang",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "O", "O", "O", "B-ORG", "O", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Kerajaan",
+ "mana",
+ "yang",
+ "berdiri",
+ "sebelum",
+ "Majapahit",
+ "?"
+ ],
+ "answer": ["Singhasari"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Candi",
+ "Prambanan",
+ "merupakan",
+ "peninggalan",
+ "agama",
+ "Hindu",
+ "."
+ ],
+ "ner": ["B-EVENT", "I-EVENT", "O", "O", "O", "B-EVENT", "O"],
+ "srl": ["ARG1", "ARG1", "V", "ARG1", "ARGM-CAU", "ARGM-CAU", "O"],
+ "question": [
+ "Candi",
+ "Prambanan",
+ "merupakan",
+ "peninggalan",
+ "agama",
+ "apa",
+ "?"
+ ],
+ "answer": ["Hindu"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Teori",
+ "Out",
+ "of",
+ "Africa",
+ "menjelaskan",
+ "asal",
+ "usul",
+ "manusia",
+ "modern",
+ "dari",
+ "Afrika",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "I-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-LOC",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Menurut",
+ "teori",
+ "Out",
+ "of",
+ "Africa",
+ ",",
+ "manusia",
+ "modern",
+ "berasal",
+ "dari",
+ "mana",
+ "?"
+ ],
+ "answer": ["Afrika"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Buleleng",
+ "berada",
+ "di",
+ "Pulau",
+ "Bali",
+ "dan",
+ "didirikan",
+ "pada",
+ "abad",
+ "ke-17",
+ "."
+ ],
+ "ner": [
+ "B-ORG",
+ "I-ORG",
+ "O",
+ "O",
+ "B-LOC",
+ "I-LOC",
+ "O",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Di", "mana", "letak", "Kerajaan", "Buleleng", "?"],
+ "answer": ["Pulau", "Bali"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kitab",
+ "Negarakertagama",
+ "ditulis",
+ "oleh",
+ "Empu",
+ "Prapanca",
+ "pada",
+ "tahun",
+ "1365",
+ "."
+ ],
+ "ner": [
+ "O",
+ "B-EVENT",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "O",
+ "B-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Siapa", "penulis", "Kitab", "Negarakertagama", "?"],
+ "answer": ["Empu", "Prapanca"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Kalingga",
+ "diperintah",
+ "oleh",
+ "Ratu",
+ "Shima",
+ "yang",
+ "terkenal",
+ "akan",
+ "keadilannya",
+ "."
+ ],
+ "ner": [
+ "B-ORG",
+ "I-ORG",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-CAU",
+ "ARGM-CAU",
+ "O"
+ ],
+ "question": ["Siapa", "yang", "memerintah", "Kerajaan", "Kalingga", "?"],
+ "answer": ["Ratu", "Shima"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kerajaan",
+ "Kutai",
+ "merupakan",
+ "kerajaan",
+ "Hindu",
+ "tertua",
+ "di",
+ "Indonesia",
+ "."
+ ],
+ "ner": ["B-ORG", "I-ORG", "O", "O", "B-EVENT", "O", "O", "B-LOC", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Apa", "kerajaan", "Hindu", "tertua", "di", "Indonesia", "?"],
+ "answer": ["Kutai"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Bangsa",
+ "Indonesia",
+ "pernah",
+ "mengalami",
+ "penjajahan",
+ "selama",
+ "kurang",
+ "lebih",
+ "350",
+ "tahun",
+ "."
+ ],
+ "ner": [
+ "O",
+ "B-LOC",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-TIME",
+ "I-TIME",
+ "O"
+ ],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-MOD",
+ "ARGM-MOD",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Berapa", "lama", "Indonesia", "mengalami", "penjajahan", "?"],
+ "answer": ["350", "tahun"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Perang",
+ "Diponegoro",
+ "terjadi",
+ "antara",
+ "tahun",
+ "1825",
+ "hingga",
+ "1830",
+ "."
+ ],
+ "ner": ["B-EVENT", "I-EVENT", "O", "O", "O", "B-DATE", "O", "B-DATE", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Kapan", "Perang", "Diponegoro", "terjadi", "?"],
+ "answer": ["1825", "hingga", "1830"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Pangeran",
+ "Antasari",
+ "adalah",
+ "pahlawan",
+ "nasional",
+ "dari",
+ "Kalimantan",
+ "Selatan",
+ "."
+ ],
+ "ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC", "I-LOC", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Pangeran", "Antasari", "berasal", "dari", "mana", "?"],
+ "answer": ["Kalimantan", "Selatan"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kaum",
+ "pergerakan",
+ "nasional",
+ "berperan",
+ "dalam",
+ "pendirian",
+ "organisasi",
+ "modern",
+ "seperti",
+ "Budi",
+ "Utomo",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORG", "I-ORG", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARGM-MNR",
+ "ARG1",
+ "ARG1",
+ "O"
+ ],
+ "question": [
+ "Organisasi",
+ "apa",
+ "yang",
+ "didirikan",
+ "oleh",
+ "kaum",
+ "pergerakan",
+ "?"
+ ],
+ "answer": ["Budi", "Utomo"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Kongres",
+ "Perempuan",
+ "Indonesia",
+ "pertama",
+ "diadakan",
+ "pada",
+ "tahun",
+ "1928",
+ "di",
+ "Yogyakarta",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "O",
+ "B-LOC",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Di",
+ "mana",
+ "Kongres",
+ "Perempuan",
+ "Indonesia",
+ "pertama",
+ "diadakan",
+ "?"
+ ],
+ "answer": ["Yogyakarta"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "BPUPKI",
+ "dibentuk",
+ "oleh",
+ "Jepang",
+ "pada",
+ "tanggal",
+ "29",
+ "April",
+ "1945",
+ "."
+ ],
+ "ner": [
+ "B-ORG",
+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Kapan", "BPUPKI", "dibentuk", "?"],
+ "answer": ["29", "April", "1945"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Sidang",
+ "PPKI",
+ "pertama",
+ "dilaksanakan",
+ "pada",
+ "tanggal",
+ "18",
+ "Agustus",
+ "1945",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": ["Kapan", "sidang", "PPKI", "pertama", "dilaksanakan", "?"],
+ "answer": ["18", "Agustus", "1945"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Sumpah",
+ "Palapa",
+ "diucapkan",
+ "oleh",
+ "Gajah",
+ "Mada",
+ "sebagai",
+ "janji",
+ "untuk",
+ "menyatukan",
+ "Nusantara",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-LOC",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Siapa", "yang", "mengucapkan", "Sumpah", "Palapa", "?"],
+ "answer": ["Gajah", "Mada"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Indonesia",
+ "memiliki",
+ "kekayaan",
+ "budaya",
+ "seperti",
+ "batik",
+ ",",
+ "wayang",
+ ",",
+ "dan",
+ "angklung",
+ "."
+ ],
+ "ner": [
+ "B-LOC",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-EVENT",
+ "O",
+ "B-EVENT",
+ "O",
+ "O",
+ "B-EVENT",
+ "O"
+ ],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "O",
+ "ARG1",
+ "O",
+ "ARG1",
+ "ARG1",
+ "O"
+ ],
+ "question": ["Sebutkan", "contoh", "kekayaan", "budaya", "Indonesia", "!"],
+ "answer": ["batik", "wayang", "angklung"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Sistem",
+ "tanam",
+ "paksa",
+ "diperkenalkan",
+ "oleh",
+ "Van",
+ "den",
+ "Bosch",
+ "pada",
+ "tahun",
+ "1830",
+ "."
+ ],
+ "ner": [
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "I-PER",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Siapa",
+ "yang",
+ "memperkenalkan",
+ "sistem",
+ "tanam",
+ "paksa",
+ "?"
+ ],
+ "answer": ["Van", "den", "Bosch"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Globalisasi",
+ "berdampak",
+ "pada",
+ "berubahnya",
+ "gaya",
+ "hidup",
+ "masyarakat",
+ "."
+ ],
+ "ner": ["B-EVENT", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "V", "ARGM-CAU", "ARGM-CAU", "ARG1", "ARG1", "ARG1", "O"],
+ "question": [
+ "Apa",
+ "dampak",
+ "dari",
+ "globalisasi",
+ "terhadap",
+ "masyarakat",
+ "?"
+ ],
+ "answer": ["berubahnya", "gaya", "hidup"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Program",
+ "Wajib",
+ "Belajar",
+ "9",
+ "Tahun",
+ "diluncurkan",
+ "untuk",
+ "meningkatkan",
+ "akses",
+ "pendidikan",
+ "."
+ ],
+ "ner": [
+ "O",
+ "B-EVENT",
+ "I-EVENT",
+ "B-TIME",
+ "I-TIME",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "V",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "ARGM-PRP",
+ "O"
+ ],
+ "question": [
+ "Apa",
+ "tujuan",
+ "dari",
+ "Program",
+ "Wajib",
+ "Belajar",
+ "9",
+ "Tahun",
+ "?"
+ ],
+ "answer": ["meningkatkan", "akses", "pendidikan"],
+ "type": "ftb"
+ },
+ {
+ "tokens": [
+ "Proklamasi",
+ "kemerdekaan",
+ "Indonesia",
+ "dibacakan",
+ "oleh",
+ "Soekarno",
+ "pada",
+ "17",
+ "Agustus",
+ "1945",
+ "."
+ ],
+ "ner": [
+ "B-EVENT",
+ "I-EVENT",
+ "B-LOC",
+ "O",
+ "O",
+ "B-PER",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O"
+ ],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "ARG1",
+ "V",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Proklamasi",
+ "kemerdekaan",
+ "Indonesia",
+ "dibacakan",
+ "oleh",
+ "Soekarno",
+ "pada",
+ "17",
+ "Agustus",
+ "1945",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Candi",
+ "Borobudur",
+ "merupakan",
+ "peninggalan",
+ "agama",
+ "Islam",
+ "."
+ ],
+ "ner": ["B-EVENT", "I-EVENT", "O", "O", "O", "B-EVENT", "O"],
+ "srl": ["ARG1", "ARG1", "V", "ARG1", "ARGM-CAU", "ARGM-CAU", "O"],
+ "question": [
+ "Candi",
+ "Borobudur",
+ "adalah",
+ "peninggalan",
+ "agama",
+ "Islam",
+ "."
+ ],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Soeharto",
+ "menjadi",
+ "Presiden",
+ "Indonesia",
+ "setelah",
+ "Soekarno",
+ "mengundurkan",
+ "diri",
+ "."
+ ],
+ "ner": ["B-PER", "O", "O", "B-LOC", "O", "B-PER", "O", "O", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Soeharto",
+ "menjadi",
+ "Presiden",
+ "Indonesia",
+ "setelah",
+ "Soekarno",
+ "mengundurkan",
+ "diri",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Indonesia", "terletak", "di", "Benua", "Afrika", "."],
+ "ner": ["B-LOC", "O", "O", "O", "B-LOC", "O"],
+ "srl": ["ARG1", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Indonesia", "terletak", "di", "Benua", "Afrika", "."],
+ "answer": ["false"],
+ "type": "tof"
}
]
diff --git a/QC/qg_train.ipynb b/QC/qg_train.ipynb
index b64342b..a6e0a46 100644
--- a/QC/qg_train.ipynb
+++ b/QC/qg_train.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
"id": "9bf2159a",
"metadata": {},
"outputs": [
@@ -10,18 +10,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2025-04-29 19:13:19.968628: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
- "2025-04-29 19:13:19.975821: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
- "2025-04-29 19:13:20.061422: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
- "2025-04-29 19:13:20.109564: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2025-05-02 15:16:40.916818: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2025-05-02 15:16:40.923426: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-02 15:16:40.983217: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-02 15:16:41.024477: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "E0000 00:00:1745928800.155971 272184 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "E0000 00:00:1745928800.168166 272184 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
- "W0000 00:00:1745928800.263286 272184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1745928800.263312 272184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1745928800.263313 272184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1745928800.263314 272184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "2025-04-29 19:13:20.274608: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "E0000 00:00:1746173801.069646 9825 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "E0000 00:00:1746173801.081087 9825 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "W0000 00:00:1746173801.169376 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746173801.169393 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746173801.169395 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746173801.169396 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "2025-05-02 15:16:41.179508: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 5,
"id": "50118278",
"metadata": {},
"outputs": [
@@ -60,9 +60,9 @@
"output_type": "stream",
"text": [
"\n",
- " Jumlah data valid: 287 / 287\n",
+ " Jumlah data valid: 321 / 321\n",
" Jumlah data tidak valid: 0\n",
- "Counter({'ftb': 202, 'tof': 45, 'none': 40})\n"
+ "Counter({'ftb': 235, 'tof': 45, 'none': 41})\n"
]
}
],
@@ -450,7 +450,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "06fd86c7",
"metadata": {},
"outputs": [
@@ -501,9 +501,7 @@
"print(\"\\n=== Akurasi Detail ===\")\n",
"print(f\"Question Accuracy (Token-level): {acc_q:.4f}\")\n",
"print(f\"Answer Accuracy (Token-level) : {acc_a:.4f}\")\n",
- "print(f\"Type Accuracy (Class-level) : {np.mean(y_true_type == y_pred_type):.2f}\")\n",
- "# print(\"\\n=== Classification Report (TYPE) ===\")\n",
- "# print(report_type)"
+ "print(f\"Type Accuracy (Class-level) : {np.mean(y_true_type == y_pred_type):.2f}\")"
]
},
{
diff --git a/dataset/backup_old_dts.tsv b/dataset/backup_old_dts.tsv
new file mode 100644
index 0000000..53efb30
--- /dev/null
+++ b/dataset/backup_old_dts.tsv
@@ -0,0 +1,16762 @@
+Mesin O ARG0
+fotokopi O ARG0
+membutuhkan O V
+450 B-QUANT ARG1
+watt I-QUANT ARG1
+energi O ARG1
+. O O
+
+Komputer O ARG0
+kantor O ARG0
+menggunakan O V
+200 B-QUANT ARG1
+watt I-QUANT ARG1
+daya O ARG1
+listrik O ARG1
+. O O
+
+Lampu O ARG0
+LED O ARG0
+hanya O ARG0
+memerlukan O V
+10 B-QUANT ARG1
+watt I-QUANT ARG1
+tenaga O ARG1
+. O O
+
+Pendingin O ARG0
+ruangan O ARG0
+menghabiskan O V
+1200 B-QUANT ARG1
+watt I-QUANT ARG1
+listrik O ARG1
+. O O
+
+Printer O ARG0
+laser O ARG0
+mengkonsumsi O V
+600 B-QUANT ARG1
+watt I-QUANT ARG1
+daya O ARG1
+. O O
+
+PT B-ORG ARG0
+Garuda I-ORG ARG0
+akan O ARGM-MOD
+menyelenggarakan O V
+dengan O ARGM-MNR
+cepat O ARGM-MNR
+Konferensi B-EVENT ARG1
+Ekonomi I-EVENT ARG1
+pada O O
+19 B-DATE ARGM-TMP
+Juli I-DATE ARGM-TMP
+2001 I-DATE ARGM-TMP
+di O O
+Jakarta B-LOC ARGM-LOC
+. O O
+
+Fajar B-PER ARG0
+Rahma I-PER ARG0
+akan O ARGM-MOD
+tidak O ARGM-NEG
+memimpin O V
+Workshop B-EVENT ARG1
+Fotografi I-EVENT ARG1
+pada O O
+19 B-DATE ARGM-TMP
+Mei I-DATE ARGM-TMP
+2025 I-DATE ARGM-TMP
+di O O
+Manado B-LOC ARGM-LOC
+. O O
+
+Nina B-PER ARG0
+Putri I-PER ARG0
+akan O ARGM-MOD
+membuka O V
+Konferensi B-EVENT ARG1
+Ekonomi I-EVENT ARG1
+pada O O
+12 B-DATE ARGM-TMP
+Juni I-DATE ARGM-TMP
+2019 I-DATE ARGM-TMP
+di O O
+Medan B-LOC ARGM-LOC
+karena O ARGM-CAU
+pandemi O ARGM-CAU
+. O O
+
+PT B-ORG ARG0
+Garuda I-ORG ARG0
+menggelar O V
+Lomba B-EVENT ARG1
+Lari I-EVENT ARG1
+pada O O
+19 B-DATE ARGM-TMP
+April I-DATE ARGM-TMP
+2022 I-DATE ARGM-TMP
+di O O
+Surabaya B-LOC ARGM-LOC
+. O O
+
+Maya B-PER ARG0
+Maulana I-PER ARG0
+menutup O V
+Lomba B-EVENT ARG1
+Lari I-EVENT ARG1
+pada O O
+6 B-DATE ARGM-TMP
+Juni I-DATE ARGM-TMP
+2011 I-DATE ARGM-TMP
+di O O
+Yogyakarta B-LOC ARGM-LOC
+. O O
+
+Universitas B-ORG ARG0
+Indonesia I-ORG ARG0
+menyelenggarakan O V
+dengan O ARGM-MNR
+cepat O ARGM-MNR
+Rapat B-EVENT ARG1
+Umum I-EVENT ARG1
+pada O O
+6 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+2012 I-DATE ARGM-TMP
+di O O
+Medan B-LOC ARGM-LOC
+. O O
+
+Universitas B-ORG ARG0
+Gadjah I-ORG ARG0
+Mada I-ORG ARG0
+menghadiri O V
+dengan O ARGM-MNR
+hati-hati O ARGM-MNR
+Turnamen B-EVENT ARG1
+Catur I-EVENT ARG1
+pada O O
+9 B-DATE ARGM-TMP
+Februari I-DATE ARGM-TMP
+2006 I-DATE ARGM-TMP
+di O O
+Banjarmasin B-LOC ARGM-LOC
+karena O ARGM-CAU
+hujan O ARGM-CAU
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+September I-DATE ARGM-TMP
+1818 I-DATE ARGM-TMP
+di O O
+Samarinda B-LOC ARGM-LOC
+. O O
+
+Hayam B-PER ARG0
+Wuruk I-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+8 B-DATE ARGM-TMP
+April I-DATE ARGM-TMP
+1784 I-DATE ARGM-TMP
+di O O
+Aceh B-LOC ARGM-LOC
+. O O
+
+Hayam B-PER ARG0
+Wuruk I-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+1 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1831 I-DATE ARGM-TMP
+di O O
+Jakarta B-LOC ARGM-LOC
+. O O
+
+Maya B-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+10 B-DATE ARGM-TMP
+Januari I-DATE ARGM-TMP
+1732 I-DATE ARGM-TMP
+di O O
+Bandung B-LOC ARGM-LOC
+. O O
+
+Andi B-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+16 B-DATE ARGM-TMP
+Januari I-DATE ARGM-TMP
+1737 I-DATE ARGM-TMP
+di O O
+Medan B-LOC ARGM-LOC
+. O O
+
+Tuanku B-PER ARG0
+Imam I-PER ARG0
+Bonjol I-PER ARG0
+lahir O V
+pada O O
+4 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1900 I-DATE ARGM-TMP
+di O O
+Makassar B-LOC ARGM-LOC
+. O O
+
+Patimura B-PER ARG0
+lahir O V
+pada O O
+12 B-DATE ARGM-TMP
+Desember I-DATE ARGM-TMP
+1778 I-DATE ARGM-TMP
+di O O
+Jepara B-LOC ARGM-LOC
+. O O
+
+Frans B-PER ARG0
+Kaisiepo I-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+28 B-DATE ARGM-TMP
+Mei I-DATE ARGM-TMP
+1732 I-DATE ARGM-TMP
+di O O
+Padang B-LOC ARGM-LOC
+. O O
+
+Ratu B-PER ARG0
+Kalinyamat I-PER ARG0
+lahir O V
+pada O O
+28 B-DATE ARGM-TMP
+September I-DATE ARGM-TMP
+1739 I-DATE ARGM-TMP
+di O O
+Surabaya B-LOC ARGM-LOC
+. O O
+
+Rudi B-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+22 B-DATE ARGM-TMP
+Maret I-DATE ARGM-TMP
+2018 I-DATE ARGM-TMP
+di O O
+Padang B-LOC ARGM-LOC
+. O O
+
+Gajah B-PER ARG0
+Mada I-PER ARG0
+memimpin O V
+perlawanan O ARG1
+pada O O
+26 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1932 I-DATE ARGM-TMP
+di O O
+Jakarta B-LOC ARGM-LOC
+. O O
+
+Sultan B-PER ARG0
+Agung I-PER ARG0
+lahir O V
+pada O O
+16 B-DATE ARGM-TMP
+April I-DATE ARGM-TMP
+1928 I-DATE ARGM-TMP
+di O O
+Bukittinggi B-LOC ARGM-LOC
+. O O
+
diff --git a/dataset/dataset_ner_srl.tsv b/dataset/dataset_ner_srl.tsv
index fd32ceb..431a47b 100644
--- a/dataset/dataset_ner_srl.tsv
+++ b/dataset/dataset_ner_srl.tsv
@@ -366,7 +366,7 @@ tropis O ARG0
memiliki O V
banyak O ARG1
varietas O ARG1
-yang O R-ARG1
+yang O ARG1
kaya O ARGM-MNR
akan O ARGM-MNR
hidrat O ARG1
@@ -4265,7 +4265,7 @@ purba O ARG2
dari O ARGM-LOC
Kala B-TIME ARGM-TMP
Pleistosen I-TIME ARGM-TMP
-yang O R-ARG2
+yang O ARG2
paling O ARGM-MNR
lengkap O ARGM-MNR
dan O O
@@ -4426,12 +4426,12 @@ lingkungan O ARG1
Beberapa O ARG0
fosil O ARG1
-yang O R-ARG1
+yang O ARG1
ditemukan O V
dalam O ARGM-LOC
seri O ARG1
geologis-stratigrafis O ARG1
-yang O R-ARG1
+yang O ARG1
diendapkan O V
tanpa O ARGM-NEG
terputus O ARGM-MNR
@@ -4473,7 +4473,7 @@ Dunia I-EVENT ARG1
pada B-DATE ARGM-TMP
1996 I-DATE ARGM-TMP
, O O
-yang O R-ARG1
+yang O ARG1
tercantum O V
dalam O ARGM-LOC
nomor O ARG1
@@ -4547,7 +4547,7 @@ ditemukan O V
sebuah O ARG1
fragmen O ARG1
rahang O ARG1
-yang O R-ARG1
+yang O ARG1
pendek O ARG1
dan O O
sangat O ARGM-MNR
@@ -4624,7 +4624,7 @@ pada B-DATE ARGM-TMP
. O O
Ekskavasi O ARG1
-yang O R-ARG1
+yang O ARG1
dilakukan O V
oleh O ARG0
Eugene B-PER ARG0
@@ -4637,7 +4637,7 @@ penemuan O ARG1
sisa-sisa O ARG1
manusia O ARG1
purba O ARG1
-yang O R-ARG1
+yang O ARG1
sangat O ARGM-MNR
berharga O ARG1
bagi O ARGM-PRP
@@ -4674,7 +4674,7 @@ utuh O ARG1
dan O O
fragmen O ARG1
) O O
-yang O R-ARG1
+yang O ARG1
menunjukkan O V
pemiliknya O ARG0
telah O ARGM-MOD
@@ -4707,13 +4707,13 @@ mata O ARGM-LOC
, O O
terdapat O V
penyempitan O ARG1
-yang O R-ARG1
+yang O ARG1
sangat O ARGM-MNR
jelas O ARG1
, O O
menandakan O V
otak O ARG1
-yang O R-ARG1
+yang O ARG1
belum O ARGM-NEG
berkembang O V
. O O
@@ -4724,7 +4724,7 @@ belakang O ARGM-LOC
kepala O ARGM-LOC
terlihat O V
bentuk O ARG1
-yang O R-ARG1
+yang O ARG1
meruncing O ARG1
yang O O
diduga O V
@@ -4795,7 +4795,7 @@ sekitar O ARGM-TMP
tahun I-DATE ARGM-TMP
oleh O ARG0
penduduk O ARG0
-yang O R-ARG0
+yang O ARG0
sedang O ARGM-PRP
membantu O V
penelitian O ARG1
@@ -4812,7 +4812,7 @@ itu O ARG1
menjadi O V
bahan O ARG2
diskusi O ARG2
-yang O R-ARG2
+yang O ARG2
menarik O ARG2
bagi O ARGM-PRP
para O ARGM-PRP
@@ -4823,17 +4823,17 @@ Metode O ARG1
pengujian O ARG1
penanggalan O ARG1
potasium-argon O ARG1
-yang O R-ARG1
+yang O ARG1
digunakan O V
oleh O ARG0
Teuku B-PER ARG0
-Jakob I-PER I-ARG0
+Jakob I-PER ARG0
dan O O
-Curtis B-PER I-ARG0
+Curtis B-PER ARG0
terhadap O ARGM-ADV
batu O ARG1
apung O ARG1
-yang O R-ARG1
+yang O ARG1
terdapat O V
di O ARGM-LOC
sekitar O ARGM-LOC
@@ -4915,7 +4915,7 @@ Adakah O ARG1
di O ARGM-LOC
antara O ARGM-LOC
kamu O ARG1
-yang O R-ARG1
+yang O ARG1
tertarik O V
untuk O ARGM-PRP
melakukan O V
@@ -4959,7 +4959,7 @@ dibandingkan O V
dengan O ARGM-COM
Homo B-EVENT ARG2
erectus I-EVENT ARG2
-yang O R-ARG2
+yang O ARG2
ada O ARG2
di O ARGM-LOC
Sangiran B-LOC ARGM-LOC
@@ -4977,7 +4977,7 @@ tahun I-DATE ARG1
Berdasarkan O ARGM-CAU
beberapa O ARGM-CAU
penelitian O ARGM-CAU
-yang O R-ARGM-CAU
+yang O ARGM-CAU
dilakukan O ARGM-CAU
oleh O ARG0
para O ARG0
@@ -4989,7 +4989,7 @@ beberapa O ARG1
jenis O ARG1
manusia O ARG1
purba O ARG1
-yang O R-ARG1
+yang O ARG1
pernah O ARGM-TMP
hidup O V
di O ARGM-TMP
@@ -5012,7 +5012,7 @@ tahun B-DATE ARGM-TMP
1936 I-DATE ARGM-TMP
dan O O
1941 I-DATE ARGM-TMP
-yang O R-ARG0
+yang O ARG0
menemukan O V
fosil O ARG1
rahang O ARG1
@@ -5051,7 +5051,7 @@ ini O ARG1
memiliki O V
ciri O ARG2
rahang O ARG2
-yang O R-ARG2
+yang O ARG2
kuat O ARG2
dan O O
badannya O ARG2
@@ -5127,7 +5127,7 @@ erectus I-EVENT ARG2
artinya O V
manusia O ARG1
kera O ARG1
-yang O R-ARG1
+yang O ARG1
berjalan O ARG1
tegak O ARG1
. O O
@@ -5148,7 +5148,7 @@ mojokertensis I-EVENT ARG2
Jenis O ARG1
manusia O ARG1
purba O ARG1
-yang O R-ARG1
+yang O ARG1
juga O ARGM-DIS
terkenal O V
sebagai O ARGM-PRD
@@ -5335,7 +5335,7 @@ Homo B-EVENT ARG0
sapiens I-EVENT ARG0
menunjukkan O V
karakter O ARG1
-yang O R-ARG1
+yang O ARG1
lebih O ARGM-MNR
berevolusi O ARG1
dan O O
@@ -5352,7 +5352,7 @@ misal O ARGM-ADV
, O O
karakter O ARG1
evolutif O ARG1
-yang O R-ARG1
+yang O ARG1
paling O ARGM-MNR
signifikan O ARG1
adalah O V
@@ -5369,7 +5369,7 @@ kepurbaannya O ARG0
menunjukkan O V
ada O O
perbedaan O ARG1
-yang O R-ARG1
+yang O ARG1
sangat O ARGM-MNR
nyata O ARG1
antara O ARGM-ADV
@@ -5387,7 +5387,7 @@ akhirnya O ARGM-TMP
tampil O V
sebagai O ARGM-PRD
spesies O ARG2
-yang O R-ARG2
+yang O ARG2
sangat O ARGM-MNR
tangguh O ARG2
dalam O ARGM-LOC
@@ -5446,7 +5446,7 @@ baru O ARG1
di O ARGM-LOC
Kepulauan B-LOC ARGM-LOC
Indonesia I-LOC ARGM-LOC
-yang O R-ARG1
+yang O ARG1
dikenal O V
sebagai O ARGM-PRD
penutur O ARG2
@@ -5492,7 +5492,7 @@ satu-satunya O ARG1
temuan O ARG1
di O ARGM-LOC
Indonesia B-LOC ARGM-LOC
-yang O R-ARG1
+yang O ARG1
untuk O ARGM-TMP
sementara O ARGM-TMP
dapat O ARGM-MOD
@@ -5583,7 +5583,7 @@ tinggi O ARG1
dengan O ARGM-MNR
isi O ARG1
tengkorak O ARG1
-yang O R-ARG1
+yang O ARG1
besar O ARG1
. O O
@@ -5622,7 +5622,7 @@ dilihat O V
dari O ARGM-LOC
ciri O ARG1
tengkoraknya O ARG1
-yang O R-ARG1
+yang O ARG1
sedang O ARG1
atau O O
agak O ARG1
@@ -5670,14 +5670,14 @@ didiami O V
oleh O ARGM-CAU
Homo B-EVENT ARG1
sapiens I-EVENT ARG1
-yang O R-ARG1
+yang O ARG1
rasnya O ARG1
sukar O ARG1
dicocokkan O V
dengan O ARGM-COM
ras-ras O ARG2
pokok O ARG2
-yang O R-ARG2
+yang O ARG2
terdapat O V
sekarang O ARGM-TMP
, O O
@@ -5705,4 +5705,3378 @@ tetapi O ARGM-ADV
mungkin O ARGM-MOD
tahapan O ARG1
Homo B-EVENT ARG1
-neanderthalensis I-EVENT ARG1
\ No newline at end of file
+neanderthalensis I-EVENT ARG1
+
+Manusia O ARG0
+Wajak B-LOC ARG0
+tidak O ARGM-NEG
+langsung O ARGM-MNR
+berevolusi O V
+dari O ARGM-SRC
+Pithecanthropus B-EVENT ARG2
+, O O
+tetapi O ARGM-ADV
+mungkin O ARGM-MOD
+tahapan O ARG1
+Homo B-EVENT ARG1
+neanderthalensis I-EVENT ARG1
+
+Pithecanthropus B-EVENT ARG1
+erectus I-EVENT ARG1
+adalah O O
+peralihan O ARG2
+kera O ARGM-SRC
+ke O O
+manusia O ARG0
+. O O
+
+Manusia O ARG0
+Ngandong B-LOC ARG0
+diperkirakan O ARGM-MOD
+berumur O V
+antara O ARGM-TMP
+300.000 B-TIME ARGM-TMP
+– I-TIME ARGM-TMP
+100.000 I-TIME ARGM-TMP
+tahun I-TIME ARGM-TMP
+. O O
+
+Pompa O ARG1
+hidrolik O ARG1
+mampu O ARGM-MOD
+mengangkat O V
+mobil O ARG2
+yang O O
+sangat O O
+berat O ARG2
+. O O
+
+Jika O O
+ayunan O ARG1
+sederhana O ARG1
+bergetar O V
+sebanyak O ARGM-MNR
+60 O ARGM-MNR
+kali O ARGM-MNR
+dalam O ARGM-TMP
+waktu B-TIME ARGM-TMP
+15 I-TIME ARGM-TMP
+sekon I-TIME ARGM-TMP
+. O O
+
+Sel O ARG1
+darah O ARG1
+merah O ARG1
+hanya O ARGM-MOD
+memiliki O V
+rentang O ARG1
+waktu B-TIME ARG1
+hidup O ARG1
+antara O ARGM-TMP
+100 B-TIME ARGM-TMP
+- I-TIME ARGM-TMP
+120 I-TIME ARGM-TMP
+hari I-TIME ARGM-TMP
+. O O
+
+Bernapaslah O V
+secara O ARGM-MNR
+normal O ARGM-MNR
+atau O O
+seperti O ARGM-MNR
+biasa O ARGM-MNR
+dan O O
+hitung O V
+berapa O O
+jumlah O ARG1
+napasmu O ARG1
+selama O ARGM-TMP
+1 B-TIME ARGM-TMP
+menit I-TIME ARGM-TMP
+. O O
+
+Bandul O ARG1
+digetarkan O V
+selama O ARGM-TMP
+1 B-TIME ARGM-TMP
+menit I-TIME ARGM-TMP
+sehingga O O
+menghasilkan O V
+40 B-QUANT ARG1
+getaran I-QUANT ARG1
+. O O
+
+Jika O O
+waktu B-TIME ARGM-TMP
+yang O O
+dibutuhkan O V
+untuk O O
+menempuh O V
+satu O ARG1
+gelombang O ARG1
+adalah O O
+0,5 B-TIME ARGM-TMP
+sekon I-TIME ARGM-TMP
+. O O
+
+Saat O ARGM-TMP
+di O O
+Aceh B-LOC ARGM-LOC
+pukul O ARGM-TMP
+06.00 B-TIME ARGM-TMP
+di O O
+Bali B-LOC ARGM-LOC
+pukul O ARGM-TMP
+07.00 B-TIME ARGM-TMP
+, O O
+dan O O
+di O O
+Papua B-LOC ARGM-LOC
+pukul O ARGM-TMP
+08.00 B-TIME ARGM-TMP
+. O O
+
+Indonesia B-LOC ARG0
+berperan O V
+aktif O ARGM-MNR
+dalam O O
+KTT B-EVENT ARG1
+G20 I-EVENT ARG1
+, O O
+Juni B-DATE ARGM-TMP
+2019 I-DATE ARGM-TMP
+. O O
+
+Fenomena B-EVENT ARG1
+La B-EVENT ARG1
+Nina I-EVENT ARG1
+sedang O ARGM-MOD
+berkembang O V
+di O O
+Samudra B-LOC ARGM-LOC
+Pasifik I-LOC ARGM-LOC
+. O O
+
+Proklamasi B-EVENT ARG1
+Kemerdekaan I-EVENT ARG1
+Indonesia I-EVENT ARG1
+terjadi O V
+pada O O
+tanggal O O
+17 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+. O O
+
+Perang B-EVENT ARG1
+Diponegoro I-EVENT ARG1
+meletus O V
+pada O O
+tahun O O
+1825 B-DATE ARGM-TMP
+hingga O O
+1830 B-DATE ARGM-TMP
+. O O
+
+Sumpah B-EVENT ARG1
+Pemuda I-EVENT ARG1
+dibacakan O V
+pada O O
+tanggal O O
+28 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1928 I-DATE ARGM-TMP
+. O O
+
+Pertempuran B-EVENT ARG1
+Surabaya I-EVENT ARG1
+meletus O V
+pada O O
+tanggal O O
+10 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+. O O
+
+Perjanjian B-EVENT ARG1
+Linggarjati I-EVENT ARG1
+ditandatangani O V
+antara O O
+Indonesia B-LOC ARG0
+dan O O
+Belanda B-LOC ARG2
+. O O
+
+Kongres B-EVENT ARG1
+Perempuan I-EVENT ARG1
+pertama O ARG1
+berlangsung O V
+pada O O
+tahun O O
+1928 B-DATE ARGM-TMP
+di O O
+Yogyakarta B-LOC ARGM-LOC
+. O O
+
+Kongres B-EVENT ARG1
+Perempuan I-EVENT ARG1
+pertama O ARG1
+berlangsung O V
+pada O O
+tahun O O
+1928 B-DATE ARGM-TMP
+di O O
+Yogyakarta B-LOC ARGM-LOC
+. O O
+
+Agresi B-EVENT ARG1
+Militer I-EVENT ARG1
+Belanda I-EVENT ARG1
+pertama O O
+terjadi O V
+pada O O
+tahun O O
+1947 B-DATE ARGM-TMP
+. O O
+
+Hari B-EVENT ARG1
+Kartini I-EVENT ARG1
+diperingati O V
+setiap O O
+tanggal O O
+21 B-DATE ARGM-TMP
+April I-DATE ARGM-TMP
+. O O
+
+Konferensi B-EVENT ARG1
+Meja I-EVENT ARG1
+Bundar I-EVENT ARG1
+berlangsung O V
+pada O O
+tanggal O O
+2 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1949 I-DATE ARGM-TMP
+. O O
+
+
+Serangan B-EVENT ARG1
+Umum I-EVENT ARG1
+1 B-DATE ARGM-TMP
+Maret I-DATE ARGM-TMP
+dilakukan O V
+untuk O O
+menunjukkan O V
+eksistensi O ARG1
+RI B-LOC ARG1
+. O O
+
+Hari B-EVENT ARG1
+Lahir I-EVENT ARG1
+Pancasila I-EVENT ARG1
+diperingati O V
+setiap O O
+1 B-DATE ARGM-TMP
+Juni I-DATE ARGM-TMP
+. O O
+
+Kongres B-EVENT ARG1
+Pemuda I-EVENT ARG1
+dilaksanakan O V
+pada O O
+tanggal O O
+28 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1928 I-DATE ARGM-TMP
+. O O
+
+Perjanjian B-EVENT ARG1
+Renville I-EVENT ARG1
+dilaksanakan O V
+pada O O
+Januari B-DATE ARGM-TMP
+1948 I-DATE ARGM-TMP
+. O O
+
+Pertempuran B-EVENT ARG1
+Surabaya I-EVENT ARG1
+terjadi O V
+pada O O
+tanggal O O
+10 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+. O O
+
+Peristiwa B-EVENT ARG1
+Rengasdengklok I-EVENT ARG1
+terjadi O V
+pada O O
+tanggal O O
+16 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+. O O
+
+Peristiwa B-EVENT ARG1
+Medan I-EVENT ARG1
+Area I-EVENT ARG1
+merupakan O V
+bentuk O ARG1
+perlawanan O ARG1
+rakyat O ARG0
+Sumatera B-LOC ARG0
+Utara I-LOC ARG0
+. O O
+
+Pertempuran B-EVENT ARG1
+Ambarawa I-EVENT ARG1
+berlangsung O V
+pada O O
+bulan O O
+Desember B-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+. O O
+
+Bandung B-LOC ARGM-LOC
+Lautan B-EVENT ARG1
+Api I-EVENT ARG1
+terjadi O V
+pada O O
+tanggal O O
+24 B-DATE ARGM-TMP
+Maret I-DATE ARGM-TMP
+1946 I-DATE ARGM-TMP
+. O O
+
+Peristiwa B-EVENT ARG1
+G30S I-EVENT ARG1
+mengguncang O V
+stabilitas O ARG1
+politik O ARG1
+nasional O ARG1
+. O O
+
+Pertempuran B-EVENT ARG1
+Margarana I-EVENT ARG1
+terjadi O V
+di O O
+Bali B-LOC ARGM-LOC
+pada O O
+tahun O O
+1946 B-DATE ARGM-TMP
+. O O
+
+Peristiwa B-EVENT ARG1
+Westerling I-EVENT ARG1
+menewaskan O V
+ribuan O ARG1
+penduduk O ARG1
+Sipil O ARG1
+. O O
+
+Hari B-EVENT ARG1
+Kesaktian I-EVENT ARG1
+Pancasila I-EVENT ARG1
+diperingati O V
+setiap O O
+tanggal O O
+1 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+. O O
+
+Serangan B-EVENT ARG1
+Umum I-EVENT ARG1
+Surakarta I-EVENT ARG1
+bertujuan O V
+menunjukkan O V
+kekuatan O ARG1
+RI B-LOC ARG1
+. O O
+
+Pemberontakan B-EVENT ARG1
+PKI I-EVENT ARG1
+Madiun I-LOC ARG1
+terjadi O V
+pada O O
+tahun O O
+1948 B-DATE ARGM-TMP
+. O O
+
+Konferensi B-EVENT ARG1
+Meja I-EVENT ARG1
+Bundar I-EVENT ARG1
+dilaksanakan O V
+pada O O
+tanggal O O
+2 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1949 I-DATE ARGM-TMP
+. O O
+
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+Raja O O
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+termasuk O V
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+Pada O O
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+muslihat O ARG1
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+hingga O O
+wafat O V
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+Pasai I-ORG ARG1
+yang O O
+membawa O V
+pengaruh O ARG1
+Islam O ARG1
+di O O
+Nusantara B-LOC ARGM-LOC
diff --git a/dataset/new_ner_srl.tsv b/dataset/new_ner_srl.tsv
new file mode 100644
index 0000000..7a04a9e
--- /dev/null
+++ b/dataset/new_ner_srl.tsv
@@ -0,0 +1,5758 @@
+Sekitar O O
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+sore B-TIME ARGM-TMP
+Dani B-PER ARG0
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+Rina B-PER ARG0
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+
+Ulos B-EVENT ARG1
+adalah O V
+kain O ARG1
+adat O ARG1
+Suku B-ETH ARG0
+Batak I-ETH ARG0
+Sumatera B-LOC ARGM-LOC
+Utara I-LOC ARGM-LOC
+. O O
+
+Keris B-EVENT ARG1
+menjadi O V
+senjata O ARG1
+tradisional O ARG1
+masyarakat O ARG1
+Jawa B-LOC ARGM-LOC
+. O O
+
+Mandau B-EVENT ARG1
+merupakan O V
+senjata O ARG1
+khas O ARG1
+Suku B-ETH ARG0
+Dayak I-ETH ARG0
+Kalimantan B-LOC ARGM-LOC
+. O O
+
+Rumah B-EVENT ARG1
+Gadang I-EVENT ARG1
+adalah O V
+rumah O ARG1
+adat O ARG1
+Minangkabau B-ETH ARG0
+Sumatera B-LOC ARGM-LOC
+Barat I-LOC ARGM-LOC
+. O O
+
+Joglo B-EVENT ARG1
+merupakan O V
+arsitektur O ARG1
+rumah O ARG1
+Jawa B-LOC ARGM-LOC
+Tengah I-LOC ARGM-LOC
+dan O O
+Yogyakarta B-LOC ARGM-LOC
+. O O
+
+Honai B-EVENT ARG1
+adalah O V
+rumah O ARG1
+tradisional O ARG1
+Suku B-ETH ARG0
+Dani I-ETH ARG0
+Papua B-LOC ARGM-LOC
+. O O
+
+Rendang B-EVENT ARG1
+merupakan O V
+makanan O ARG1
+khas O ARG1
+Minangkabau B-ETH ARG0
+Sumatera B-LOC ARGM-LOC
+Barat I-LOC ARGM-LOC
+. O O
+
+Pempek B-EVENT ARG1
+adalah O V
+kuliner O ARG1
+favorit O ARG1
+Palembang B-LOC ARGM-LOC
+Sumatera B-LOC ARGM-LOC
+Selatan I-LOC ARGM-LOC
+. O O
+
+Gudeg B-EVENT ARG1
+menjadi O V
+ikon O ARG1
+kuliner O ARG1
+Yogyakarta B-LOC ARGM-LOC
+. O O
+
+Papeda B-EVENT ARG1
+adalah O V
+makanan O ARG1
+pokok O ARG1
+masyarakat O ARG1
+Papua B-LOC ARGM-LOC
+. O O
+
+Ayam B-EVENT ARG1
+Betutu I-EVENT ARG1
+adalah O V
+hidangan O ARG1
+tradisional O ARG1
+Suku B-ETH ARG0
+Bali I-ETH ARG0
+. O O
+
+Koteka B-EVENT ARG1
+dipakai O V
+oleh O O
+pria O ARG1
+Papua B-LOC ARGM-LOC
+. O O
+
+Songket B-EVENT ARG1
+Palembang I-EVENT ARG1
+ditenun O V
+dengan O O
+benang O ARG1
+emas O ARG1
+Sumatera B-LOC ARGM-LOC
+Selatan I-LOC ARGM-LOC
+. O O
+
+Rencong B-EVENT ARG1
+menjadi O V
+senjata O ARG1
+tradisional O ARG1
+Aceh B-LOC ARGM-LOC
+. O O
+
+
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+memproklamasikan O V
+kemerdekaan O ARG1
+pada O O
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+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Raden B-PER ARG0
+Ajeng I-PER ARG0
+Kartini I-PER ARG0
+lahir O V
+pada O O
+21 B-DATE ARGM-TMP
+April I-DATE ARGM-TMP
+1879 I-DATE ARGM-TMP
+
+Bung B-PER ARG0
+Hatta I-PER ARG0
+berpidato O V
+pada O O
+tanggal O O
+18 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Sutan B-PER ARG0
+Syahrir I-PER ARG0
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+menjadi O O
+Perdana O ARG1
+Menteri O ARG1
+pada O O
+14 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Pangeran B-PER ARG0
+Diponegoro I-PER ARG0
+memulai O V
+Perang B-EVENT ARG1
+Diponegoro I-EVENT ARG1
+pada O O
+tahun B-DATE ARGM-TMP
+1825 I-DATE ARGM-TMP
+
+Ki B-PER ARG0
+Hajar I-PER ARG0
+Dewantara I-PER ARG0
+memimpin O V
+pendidikan O ARG1
+pada O O
+awal B-TIME ARGM-TMP
+abad O ARGM-TMP
+20 I-TIME ARGM-TMP
+
+Ir. B-PER ARG0
+Soekarno I-PER ARG0
+wafat O V
+pada O O
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+Juni I-DATE ARGM-TMP
+1970 I-DATE ARGM-TMP
+
+Mohammad B-PER ARG0
+Yamin I-PER ARG0
+mengusulkan O V
+sumpah O ARG1
+pemuda O ARG1
+pada O O
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+Oktober I-DATE ARGM-TMP
+1928 I-DATE ARGM-TMP
+
+R.A. B-PER ARG0
+Kartini I-PER ARG0
+menulis O V
+surat O ARG1
+pada O O
+tahun B-DATE ARGM-TMP
+1902 I-DATE ARGM-TMP
+
+Jenderal B-PER ARG0
+Soedirman I-PER ARG0
+memimpin O V
+perang O ARG1
+gerilya O ARG1
+pada O O
+Desember B-DATE ARGM-TMP
+1948 I-DATE ARGM-TMP
+
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+dan O O
+Mohammad B-PER ARG0
+Hatta I-PER ARG0
+membacakan O V
+proklamasi O ARG1
+pada O O
+17 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Jenderal B-PER ARG0
+A.H. I-PER ARG0
+Nasution I-PER ARG0
+selamat O V
+dari O O
+percobaan O ARG1
+pembunuhan O ARG1
+pada O O
+1 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1965 I-DATE ARGM-TMP
+
+Sutomo B-PER ARG0
+atau O O
+Bung B-PER ARG0
+Tommo I-PER ARG0
+memimpin O V
+pertempuran O ARG1
+10 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Jenderal B-PER ARG0
+Soedirman I-PER ARG0
+memimpin O V
+serangan O ARG1
+umum O ARG1
+pada O O
+1 B-DATE ARGM-TMP
+Maret I-DATE ARGM-TMP
+1949 I-DATE ARGM-TMP
+
+KH B-PER ARG0
+Hasyim I-PER ARG0
+Asy'ari I-PER ARG0
+mengeluarkan O V
+resolusi O ARG1
+jihad O ARG1
+pada O O
+22 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Presiden B-PER ARG0
+Habibie I-PER ARG0
+mengundurkan O V
+diri O ARG1
+pada O O
+20 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1999 I-DATE ARGM-TMP
+
+Jenderal B-PER ARG0
+Ahmad I-PER ARG0
+Yani I-PER ARG0
+gugur O V
+pada O O
+1 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1965 I-DATE ARGM-TMP
+
+Presiden B-PER ARG0
+Abdurrahman I-PER ARG0
+Wahid I-PER ARG0
+resmi O V
+dilantik O ARG1
+pada O O
+20 B-DATE ARGM-TMP
+Oktober I-DATE ARGM-TMP
+1999 I-DATE ARGM-TMP
+
+Drs. B-PER ARG0
+Mohammad I-PER ARG0
+Hatta I-PER ARG0
+menjadi O V
+Wakil O ARG1
+Presiden O ARG1
+pertama O ARG1
+pada O O
+18 B-DATE ARGM-TMP
+Agustus I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+memberikan O V
+amanat O ARG1
+pada O O
+1 B-DATE ARGM-TMP
+Juni I-DATE ARGM-TMP
+1945 I-DATE ARGM-TMP
+
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+menetapkan O V
+tanggal O O
+10 B-DATE ARGM-TMP
+November I-DATE ARGM-TMP
+sebagai O O
+Hari B-EVENT ARG1
+Pahlawan I-EVENT ARG1
+
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+sedang O ARGM-TMP
+membacakan O V
+Dekrit B-EVENT ARG1
+Presiden B-EVENT ARG1
+5 B-DATE ARGM-TMP
+Juli I-DATE ARGM-TMP
+1959 I-DATE ARGM-TMP
+di O O
+Istana B-LOC ARGM-LOC
+Merdeka I-LOC ARGM-LOC
+
+Bergulirnya O O
+gerakan B-EVENT ARG1
+reformasi I-EVENT ARG1
+menyadarkan O V
+bangsa O ARG1
+Indonesia B-LOC ARG1
+mengenai O O
+demokrasi O ARG1
+dan O O
+menghargai O V
+kedaulatan O ARG1
+rakyat O ARG1
+
+Dalam O O
+Gerakan B-EVENT ARGM-LOC
+Non-Blok I-EVENT ARGM-LOC
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+menjadi O V
+salah O O
+satu O O
+tokoh O ARG1
+pendiri O ARG1
+bersama O O
+PM O O
+India B-LOC ARGM-LOC
+Jawaharlal B-PER ARG0
+Nehru I-PER ARG0
+dan O O
+Presiden B-PER ARG0
+Mesir B-LOC ARGM-LOC
+Gamal B-PER ARG0
+Abdul I-PER ARG0
+Nasser I-PER ARG0
+
+Para O ARG0
+pemuda O ARG0
+termasuk O O
+Sultan B-PER ARG0
+Syahrir I-PER ARG0
+menganggap O V
+PPKI B-ORG ARG1
+adalah O O
+bentukan O ARG1
+Jepang B-ETH ARG1
+sehingga O O
+Proklamasi B-EVENT ARG1
+kemerdekaan I-EVENT ARG1
+harus O ARGM-MOD
+dilakukan O V
+dengan O O
+kekuatan O ARG1
+sendiri O ARG1
+
+Pada O O
+masa O ARGM-TMP
+Perang B-EVENT ARGM-TMP
+Puputan I-EVENT ARGM-TMP
+Margarana I-EVENT ARGM-TMP
+Letkol O O
+I B-PER ARG0
+Gusti I-PER ARG0
+Ngurah I-PER ARG0
+Rai I-PER ARG0
+memimpin O V
+pertempuran O ARG1
+habis-habisan O ARGM-MNR
+melawan O O
+pasukan O ARG1
+Belanda B-ETH ARG1
+
+Raja B-PER ARG0
+Kertanegara I-PER ARG0
+bercita-cita O V
+menjadikan O V
+Singhasari B-ORG ARG1
+sebagai O O
+kerajaan O ARG1
+besar O ARG1
+yang O O
+menguasai O V
+seluruh O O
+Nusantara B-LOC ARG1
+
+Pangeran B-PER ARG0
+Antasari I-PER ARG0
+mendapat O V
+dukungan O ARG1
+dari O O
+berbagai O O
+pihak O ARG1
+seperti O O
+Sultan B-PER ARG1
+Pasir I-PER ARG1
+dan O O
+Tumenggung B-PER ARG1
+Surapati I-PER ARG1
+untuk O O
+melawan O V
+kekuasaan O ARG1
+Belanda B-ETH ARG1
+
+Sultan B-PER ARG0
+Hamengkubuwana I-PER ARG0
+II I-PER ARG0
+berhasil O ARGM-MOD
+diturunkan O V
+dari O O
+tahta O ARG1
+dan O O
+digantikan O V
+oleh O O
+Sultan B-PER ARG0
+Hamengkubuwana I-PER ARG0
+III I-PER ARG0
+setelah O ARGM-TMP
+peristiwa B-EVENT ARGM-TMP
+penyerangan I-EVENT ARGM-TMP
+Keraton B-LOC ARGM-LOC
+Yogyakarta I-LOC ARGM-LOC
+oleh O O
+pasukan O ARG1
+Inggris B-ETH ARG1
+
+Pangeran B-PER ARG0
+Diponegoro I-PER ARG0
+adalah O O
+pemimpin O ARG1
+dan O O
+pejuang O ARG1
+yang O O
+sangat O ARGM-MNR
+menghargai O V
+kerja O ARG1
+sama O ARG1
+dalam O O
+menghadapi O V
+kekuasaan O ARG1
+Belanda B-ETH ARG1
+
+Sultan B-PER ARG0
+Malik I-PER ARG0
+as-Shaleh I-PER ARG0
+merupakan O V
+sultan O ARG1
+pertama O ARG1
+Kesultanan B-ORG ARG1
+Samudra I-ORG ARG1
+Pasai I-ORG ARG1
+yang O O
+membawa O V
+pengaruh O ARG1
+Islam O ARG1
+di O O
+Nusantara B-LOC ARGM-LOC
+
+Sultan B-PER ARG0
+Mansyur I-PER ARG0
+Syah I-PER ARG0
+menempatkan O V
+raja-raja O ARG1
+sebagai O O
+wakil O ARG1
+Kemaharajaan B-ORG ARG1
+Melayu I-ORG ARG1
+di O O
+daerah O ARGM-LOC
+Siak B-LOC ARGM-LOC
+dan O O
+sekitarnya O ARGM-LOC
+
+Letjen O O
+Soeharto B-PER ARG0
+memegang O V
+kendali O ARG1
+pemerintahan O ARG1
+setelah O ARGM-TMP
+menerima O V
+Surat B-EVENT ARG1
+Perintah I-EVENT ARG1
+11 B-DATE ARGM-TMP
+Maret I-DATE ARGM-TMP
+Supersemar B-EVENT ARG1
+dari O O
+Presiden B-PER ARG0
+Soekarno I-PER ARG0
+
+Pangeran B-PER ARG0
+Nuku I-PER ARG0
+diangkat O V
+sebagai O O
+sultan O ARG1
+dengan O O
+gelar O ARG1
+Tuan B-PER ARG1
+Sultan B-PER ARG1
+Amir I-PER ARG1
+Muhammad I-PER ARG1
+Syafiudin I-PER ARG1
+Syah I-PER ARG1
+dan O O
+memimpin O V
+perlawanan O ARG1
+terhadap O O
+VOC B-ORG ARG1
+
+Sultan B-PER ARG0
+Agung I-PER ARG0
+adalah O O
+raja O ARG1
+yang O O
+paling O ARGM-MNR
+terkenal O ARG1
+dari O O
+Kerajaan B-ORG ARGM-LOC
+Mataram I-ORG ARGM-LOC
+dan O O
+bercita-cita O V
+mempersatukan O V
+seluruh O O
+tanah O ARG1
+Jawa B-LOC ARGM-LOC
+serta O O
+mengusir O V
+kekuasaan O ARG1
+asing O ARG1
+dari O O
+Nusantara B-LOC ARGM-LOC
+
+Nyi B-PER ARG0
+Ageng I-PER ARG0
+Serang I-PER ARG0
+yang O O
+sudah O ARGM-MOD
+berusia O V
+73 B-DATE ARGM-TMP
+tahun I-DATE ARGM-TMP
+bersama O O
+cucunya O ARG1
+R.M. B-PER ARG1
+Papak I-PER ARG1
+bergabung O V
+bersama O O
+pasukan O ARG1
+Pangeran B-PER ARG1
+Diponegoro I-PER ARG1
+
+Imam B-PER ARG0
+Bonjol I-PER ARG0
+bersama O O
+pejuang O ARG0
+lainnya O ARG0
+terus O ARGM-MNR
+berjuang O V
+untuk O O
+mempertahankan O V
+setiap O O
+jengkal O ARG1
+tanah O ARG1
+Minangkabau B-LOC ARGM-LOC
+
+Pangeran B-PER ARG0
+Antasari I-PER ARG0
+juga O O
+mendapat O V
+dukungan O ARG1
+dari O O
+berbagai O O
+pihak O ARG1
+seperti O O
+Sultan B-PER ARG1
+Pasir I-PER ARG1
+dan O O
+Tumenggung B-PER ARG1
+Surapati I-PER ARG1
+untuk O O
+melawan O V
+kekuasaan O ARG1
+Belanda B-ETH ARG1
+
+Sultan B-PER ARG0
+Ageng I-PER ARG0
+Tirtayasa I-PER ARG0
+memimpin O V
+rakyatnya O ARG1
+untuk O O
+menyerang O V
+VOC B-ORG ARG1
+demi O O
+menjaga O V
+kedaulatan O ARG1
+dan O O
+kemakmuran O ARG1
+Banten B-LOC ARGM-LOC
+
+Sultan B-PER ARG0
+Rahmatullah I-PER ARG0
+memerintah O V
+Kerajaan B-ORG ARG1
+Banjarmasin I-ORG ARG1
+dan O O
+masih O ARGM-MOD
+mengirimkan O V
+upeti O ARG1
+ke O O
+Demak B-LOC ARGM-LOC
+yang O O
+saat O O
+itu O O
+menjadi O V
+Kerajaan B-ORG ARG1
+Pajang I-ORG ARG1
+
+Raja O O
+Baharuddin B-PER ARG0
+termasuk O V
+raja O ARG1
+yang O O
+banyak O ARGM-MNR
+jasanya O ARG1
+terhadap O O
+Raffles B-PER ARG1
+dalam O O
+menyenangkan O V
+Belanda B-ETH ARG1
+dari O O
+Nusantara B-LOC ARGM-LOC
+
+Pada O O
+tahun O O
+1683 B-DATE ARGM-TMP
+Sultan B-PER ARG0
+Ageng I-PER ARG0
+Tirtayasa I-PER ARG0
+berhasil O ARGM-MOD
+ditangkap O V
+oleh O O
+VOC B-ORG ARG1
+dengan O O
+tipu O ARG1
+muslihat O ARG1
+dan O O
+ditawan O V
+di O O
+Batavia B-LOC ARGM-LOC
+hingga O O
+wafat O V
+
+Sultan B-PER ARG0
+Malik I-PER ARG0
+as-Shaleh I-PER ARG0
+merupakan O V
+sultan O ARG1
+pertama O ARG1
+Kesultanan B-ORG ARG1
+Samudra I-ORG ARG1
+Pasai I-ORG ARG1
+yang O O
+membawa O V
+pengaruh O ARG1
+Islam O ARG1
+di O O
+Nusantara B-LOC ARGM-LOC
+
diff --git a/lstm_multi_output_model.keras b/lstm_multi_output_model.keras
deleted file mode 100644
index f9f13fa..0000000
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diff --git a/lstm_multi_output_ner_model.keras b/lstm_multi_output_ner_model.keras
deleted file mode 100644
index 5d1ef4c..0000000
Binary files a/lstm_multi_output_ner_model.keras and /dev/null differ
diff --git a/NER/lstm_ner_qc.py b/old/NER/lstm_ner_qc.py
similarity index 100%
rename from NER/lstm_ner_qc.py
rename to old/NER/lstm_ner_qc.py
diff --git a/NER/test_ner.py b/old/NER/test_ner.py
similarity index 100%
rename from NER/test_ner.py
rename to old/NER/test_ner.py
diff --git a/old/accuracy_plot.png b/old/accuracy_plot.png
new file mode 100644
index 0000000..d17ff61
Binary files /dev/null and b/old/accuracy_plot.png differ
diff --git a/old/loss_plot.png b/old/loss_plot.png
new file mode 100644
index 0000000..23c4287
Binary files /dev/null and b/old/loss_plot.png differ
diff --git a/old/lses.py b/old/lses.py
new file mode 100644
index 0000000..8e032b9
--- /dev/null
+++ b/old/lses.py
@@ -0,0 +1,152 @@
+# ner_srl_multitask.py
+# ----------------------------------------------------------
+# Train a multi‑task (Bi)LSTM that predicts NER + SRL tags
+# ----------------------------------------------------------
+import json, numpy as np, tensorflow as tf
+from tensorflow.keras.layers import (Input, Embedding, LSTM, Bidirectional,
+ TimeDistributed, Dense)
+from tensorflow.keras.models import Model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from seqeval.metrics import classification_report
+# ----------------------------------------------------------
+# 1. Load and prepare data
+# ----------------------------------------------------------
+DATA = json.load(open("../dataset/dataset_ner_srl.json", "r", encoding="utf8"))
+
+# --- token vocabulary -------------------------------------------------
+vocab = {"PAD": 0, "UNK": 1}
+for sample in DATA:
+ for tok in sample["tokens"]:
+ vocab.setdefault(tok.lower(), len(vocab))
+
+# --- label maps -------------------------------------------------------
+def build_label_map(key):
+ tags = {"PAD": 0} # keep 0 for padding
+ for s in DATA:
+ for t in s[key]:
+ tags.setdefault(t, len(tags))
+ return tags
+
+ner2idx = build_label_map("labels_ner")
+srl2idx = build_label_map("labels_srl")
+idx2ner = {i: t for t, i in ner2idx.items()}
+idx2srl = {i: t for t, i in srl2idx.items()}
+
+# --- sequences --------------------------------------------------------
+MAXLEN = max(len(x["tokens"]) for x in DATA)
+
+X = [[vocab.get(tok.lower(), vocab["UNK"]) for tok in s["tokens"]]
+ for s in DATA]
+y_ner = [[ner2idx[t] for t in s["labels_ner"]]
+ for s in DATA]
+y_srl = [[srl2idx[t] for t in s["labels_srl"]]
+ for s in DATA]
+
+X = pad_sequences(X, maxlen=MAXLEN, padding="post", value=vocab["PAD"])
+y_ner = pad_sequences(y_ner, maxlen=MAXLEN, padding="post", value=ner2idx["PAD"])
+y_srl = pad_sequences(y_srl, maxlen=MAXLEN, padding="post", value=srl2idx["PAD"])
+
+# --- one‑hot for softmax ---------------------------------------------
+y_ner = to_categorical(y_ner, num_classes=len(ner2idx))
+y_srl = to_categorical(y_srl, num_classes=len(srl2idx))
+
+# ----------------------------------------------------------
+# 2. Train / validation split
+# ----------------------------------------------------------
+# *All* arrays must be passed to train_test_split in one call so they
+# stay aligned. Order‑of‑return = train,test for each array.
+X_tr, X_val, y_tr_ner, y_val_ner, y_tr_srl, y_val_srl = train_test_split(
+ X, y_ner, y_srl, test_size=0.15, random_state=42
+)
+
+# ----------------------------------------------------------
+# 3. Model definition
+# ----------------------------------------------------------
+EMB_DIM = 128
+LSTM_UNITS = 128
+
+inp = Input(shape=(MAXLEN,))
+emb = Embedding(len(vocab), EMB_DIM, mask_zero=True)(inp)
+bilstm= Bidirectional(LSTM(LSTM_UNITS, return_sequences=True))(emb)
+
+ner_out = TimeDistributed(
+ Dense(len(ner2idx), activation="softmax"), name="ner")(bilstm)
+srl_out = TimeDistributed(
+ Dense(len(srl2idx), activation="softmax"), name="srl")(bilstm)
+
+model = Model(inp, [ner_out, srl_out])
+model.compile(
+ optimizer="adam",
+ loss ={"ner": "categorical_crossentropy",
+ "srl": "categorical_crossentropy"},
+ metrics={"ner": "accuracy",
+ "srl": "accuracy"}
+)
+model.summary()
+
+# ----------------------------------------------------------
+# 4. Train
+# ----------------------------------------------------------
+history = model.fit(
+ X_tr,
+ {"ner": y_tr_ner, "srl": y_tr_srl},
+ validation_data=(X_val, {"ner": y_val_ner, "srl": y_val_srl}),
+ epochs=15,
+ batch_size=32,
+ verbose=2,
+)
+
+# ----------------------------------------------------------
+# 5. Helper: decode with a mask (so lens always match)
+# ----------------------------------------------------------
+def decode(pred, idx2tag, mask):
+ """
+ pred : [n, MAXLEN, n_tags] (one‑hot or probabilities)
+ mask : [n, MAXLEN] (True for real tokens, False for PAD)
+ """
+ out = []
+ for seq, m in zip(pred, mask):
+ tags = [idx2tag[np.argmax(tok)] for tok, keep in zip(seq, m) if keep]
+ out.append(tags)
+ return out
+
+# ----------------------------------------------------------
+# 6. Evaluation
+# ----------------------------------------------------------
+y_pred_ner, y_pred_srl = model.predict(X_val, verbose=0)
+
+mask_val = (X_val != vocab["PAD"]) # True for real tokens
+
+true_ner = decode(y_val_ner , idx2ner, mask_val)
+pred_ner = decode(y_pred_ner, idx2ner, mask_val)
+true_srl = decode(y_val_srl , idx2srl, mask_val)
+pred_srl = decode(y_pred_srl, idx2srl, mask_val)
+
+print("\n📊 NER report")
+print(classification_report(true_ner, pred_ner))
+
+print("\n📊 SRL report")
+print(classification_report(true_srl, pred_srl))
+
+# # ----------------------------------------------------------
+# # 7. Quick inference function
+# # ----------------------------------------------------------
+# def predict_sentence(sentence: str):
+# tokens = sentence.strip().split()
+# ids = [vocab.get(w.lower(), vocab["UNK"]) for w in tokens]
+# ids = pad_sequences([ids], maxlen=MAXLEN, padding="post",
+# value=vocab["PAD"])
+# mask = (ids != vocab["PAD"])
+# p_ner, p_srl = model.predict(ids, verbose=0)
+# ner_tags = decode(p_ner , idx2ner , mask)[0]
+# srl_tags = decode(p_srl , idx2srl , mask)[0]
+# return list(zip(tokens, ner_tags, srl_tags))
+
+# # ---- demo ------------------------------------------------
+# if __name__ == "__main__":
+# print("\n🔍 Demo:")
+# for tok, ner, srl in predict_sentence(
+# "Keanekaragaman hayati Indonesia sangat dipengaruhi faktor iklim."):
+# print(f"{tok:15} {ner:10} {srl}")
diff --git a/old/lstm_ner_srl.ipynb b/old/lstm_ner_srl.ipynb
new file mode 100644
index 0000000..be3d073
--- /dev/null
+++ b/old/lstm_ner_srl.ipynb
@@ -0,0 +1,2791 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fcdce269",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-08 14:34:09.368546: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2025-05-08 14:34:09.369289: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-08 14:34:09.371530: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-08 14:34:09.377960: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "E0000 00:00:1746689649.388328 77963 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "E0000 00:00:1746689649.391591 77963 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "W0000 00:00:1746689649.399815 77963 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746689649.399831 77963 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746689649.399832 77963 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746689649.399833 77963 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "2025-05-08 14:34:09.402412: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import Model\n",
+ "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
+ "from keras.utils import to_categorical\n",
+ "from keras.preprocessing.sequence import pad_sequences\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from seqeval.metrics import classification_report\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import nltk\n",
+ "from nltk.corpus import stopwords\n",
+ "from nltk.tokenize import word_tokenize\n",
+ "\n",
+ "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
+ "\n",
+ "from collections import Counter\n",
+ "import re\n",
+ "import string\n",
+ "import pickle\n",
+ "import json\n",
+ "import numpy as np\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "92b6b57f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package stopwords to /home/akeon/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n",
+ "[nltk_data] Downloading package punkt to /home/akeon/nltk_data...\n",
+ "[nltk_data] Package punkt is already up-to-date!\n",
+ "[nltk_data] Downloading package punkt_tab to /home/akeon/nltk_data...\n",
+ "[nltk_data] Package punkt_tab is already up-to-date!\n",
+ "[nltk_data] Downloading package wordnet to /home/akeon/nltk_data...\n",
+ "[nltk_data] Package wordnet is already up-to-date!\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nltk.download(\"stopwords\")\n",
+ "nltk.download(\"punkt\")\n",
+ "nltk.download(\"punkt_tab\")\n",
+ "nltk.download(\"wordnet\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d568e8f2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "158 sentences\n",
+ "=== NER LABEL COUNTS ===\n",
+ "O -> 1495 labels\n",
+ "B-LOC -> 100 labels\n",
+ "B-MISC -> 6 labels\n",
+ "B-TIME -> 46 labels\n",
+ "I-TIME -> 37 labels\n",
+ "I-LOC -> 19 labels\n",
+ "B-QUANT -> 4 labels\n",
+ "I-QUANT -> 5 labels\n",
+ "B-DATE -> 42 labels\n",
+ "B-REL -> 2 labels\n",
+ "B-ETH -> 2 labels\n",
+ "I-ETH -> 2 labels\n",
+ "B-ORG -> 9 labels\n",
+ "I-ORG -> 5 labels\n",
+ "B-MIN -> 6 labels\n",
+ "B-TERM -> 2 labels\n",
+ "I-TERM -> 3 labels\n",
+ "B-RES -> 8 labels\n",
+ "I-RES -> 2 labels\n",
+ "B-PER -> 13 labels\n",
+ "I-PER -> 16 labels\n",
+ "I-DATE -> 34 labels\n",
+ "I-MISC -> 4 labels\n",
+ "B-EVENT -> 4 labels\n",
+ "I-EVENT -> 4 labels\n",
+ "\n",
+ "=== SRL LABEL COUNTS ===\n",
+ "ARG1 -> 421 labels\n",
+ "ARGM-LOC -> 65 labels\n",
+ "AM-NEG -> 2 labels\n",
+ "V -> 196 labels\n",
+ "ARGM-SRC -> 13 labels\n",
+ "O -> 320 labels\n",
+ "AM-QUE -> 5 labels\n",
+ "ARGM-BNF -> 6 labels\n",
+ "ARG2 -> 184 labels\n",
+ "ARGM-MNR -> 9 labels\n",
+ "ARG0 -> 129 labels\n",
+ "AM-TMP -> 279 labels\n",
+ "AM-PRP -> 1 labels\n",
+ "AM-MOD -> 5 labels\n",
+ "AM-ADV -> 1 labels\n",
+ "AM-CAU -> 14 labels\n",
+ "AM-EXT -> 6 labels\n",
+ "AM-MNR -> 22 labels\n",
+ "AM-DIS -> 2 labels\n",
+ "AM-FRQ -> 2 labels\n",
+ "ARGM-PNC -> 4 labels\n",
+ "R-ARG1 -> 3 labels\n",
+ "AM-LOC -> 78 labels\n",
+ "AM-DIR -> 4 labels\n",
+ "ARGM-CAU -> 17 labels\n",
+ "ARGM-MOD -> 11 labels\n",
+ "ARGM-EXT -> 2 labels\n",
+ "ARGM-TMP -> 12 labels\n",
+ "ARGM-DIS -> 9 labels\n",
+ "ARG3 -> 12 labels\n",
+ "ARGM-NEG -> 2 labels\n",
+ "ARGM-COM -> 3 labels\n",
+ "ARGM-PRP -> 10 labels\n",
+ "ARGM-EX -> 4 labels\n",
+ "ARGM-PRD -> 4 labels\n",
+ "AM-COM -> 9 labels\n",
+ "I-AM-LOC -> 1 labels\n",
+ "AM-PNC -> 5 labels\n"
+ ]
+ }
+ ],
+ "source": [
+ "# === LOAD DATA ===\n",
+ "with open(\"../dataset/dataset_ner_srl.json\", \"r\", encoding=\"utf-8\") as f:\n",
+ " data = json.load(f)\n",
+ "\n",
+ "sentences = [[token.lower() for token in item[\"tokens\"]] for item in data]\n",
+ "ner_labels = [item[\"labels_ner\"] for item in data]\n",
+ "srl_labels = [item[\"labels_srl\"] for item in data]\n",
+ "\n",
+ "print(len(sentences), \"sentences\")\n",
+ "\n",
+ "# === COUNTERS ===\n",
+ "ner_counter = Counter()\n",
+ "srl_counter = Counter()\n",
+ "\n",
+ "for ner_seq in ner_labels:\n",
+ " ner_counter.update(ner_seq)\n",
+ "\n",
+ "for srl_seq in srl_labels:\n",
+ " srl_counter.update(srl_seq)\n",
+ "\n",
+ "# === PRINT RESULT ===\n",
+ "print(\"=== NER LABEL COUNTS ===\")\n",
+ "for label, count in ner_counter.items():\n",
+ " print(f\"{label} -> {count} labels\")\n",
+ "\n",
+ "print(\"\\n=== SRL LABEL COUNTS ===\")\n",
+ "for label, count in srl_counter.items():\n",
+ " print(f\"{label} -> {count} labels\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "95f16969",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "old [['keberagaman', 'potensi', 'sumber', 'daya', 'alam', 'indonesia', 'tidak', 'lepas', 'dari', 'proses', 'geografis', 'yang', 'terjadi', '.'], ['bagaimana', 'proses', 'geografis', 'di', 'indonesia', '?'], ['bagaimana', 'pengaruh', 'proses', 'geografis', 'bagi', 'keragaman', 'alam', 'dan', 'keragaman', 'sosial', 'masyarakat', 'indonesia', '?'], ['bagaimana', 'mengoptimalkan', 'peranan', 'sumber', 'daya', 'manusia', 'dalam', 'mengelola', 'sumber', 'daya', 'alam', 'indonesia', '?'], ['apakah', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'sudah', 'memenuhi', 'syarat', 'untuk', 'mengolah', 'pariwisata', 'yang', 'dimilikinya', '?'], ['bagaimana', 'lembaga', 'sosial', 'yang', 'akan', 'mewadahi', 'untuk', 'mengolah', 'sumber', 'daya', 'alam', 'dan', 'sumber', 'daya', 'manusianya', '?'], ['kalian', 'juga', 'perlu', 'memahami', ',', 'bahwa', 'keragaman', 'sosial', 'dan', 'budaya', 'telah', 'menarik', 'kedatangan', 'bangsa-bangsa', 'asing', 'sejak', 'ribuan', 'tahun', 'yang', 'lalu', '.'], ['perkembangan', 'hindu-buddha', 'di', 'indonesia', 'tidak', 'lepas', 'dari', 'perkembangan', 'perdagangan', 'dan', 'pelayaran', 'pada', 'awal', 'abad', 'masehi', '.'], ['bangsa', 'indonesia', 'patut', 'bersyukur', 'karena', 'proses', 'geografis', 'dan', 'keragaman', 'alam', 'yang', 'dimiliki', '.'], ['indonesia', 'merupakan', 'negara', 'terluas', 'di', 'asia', 'tenggara', '.'], ['luas', 'daratan', 'indonesia', 'sebesar', '1.910.932,37', 'km2', '.'], ['dan', 'lautan', 'indonesia', 'mencapai', '5,8', 'juta', 'km2', '.'], ['letak', 'indonesia', 'sangat', 'menguntungkan', 'bagi', 'kehidupan', 'masyarakat', '.'], ['selain', 'memiliki', 'letak', 'geografis', 'yang', 'sangat', 'menguntungkan', ',', 'indonesia', 'juga', 'memiliki', 'letak', 'geologis', ',', 'iklim', ',', 'dan', 'cuaca', 'yang', 'sangat', 'menguntungkan', '.'], ['kalian', 'tentu', 'sering', 'membincangkan', 'tentang', 'musim', 'dan', 'hubungannya', 'dengan', 'aktivitas', 'sehari-hari', '.'], ['masyarakat', 'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober', '-', 'maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april', '-', 'september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman'], ['penyinaran', 'matahari', 'memengaruhi', 'fotosintesis', 'tanaman', ',', 'dapat', 'meningkatkan', 'suhu', 'udara', '.'], ['suhu', 'mengurangi', 'kadar', 'air', 'sehingga', 'cenderung', 'menjadi', 'kering', '.'], ['kelembaban', 'membatasi', 'hilangnya', 'air', '.'], ['angin', 'membantu', 'proses', 'penyerbukan', 'secara', 'alami', ',', 'mengurangi', 'kadar', 'air', '.'], ['hujan', 'meningkatkan', 'kadar', 'air', ',', 'mengikis', 'tanah', '.'], ['kalian', 'menemukan', 'berbagai', 'perbedaan', 'sosial', 'budaya', 'masyarakat', 'di', 'sekitar', 'tempat', 'tinggalmu', '.'], ['apabila', 'kalian', 'tinggal', 'di', 'perkotaan', ',', 'perbedaan', 'sosial', 'budaya', 'akan', 'semakin', 'banyak', '.'], ['perbedaan', 'sosial', 'budaya', 'meliputi', 'perbedaan', 'nilai-nilai', ',', 'norma', ',', 'dan', 'karakteristik', 'dari', 'suatu', 'kelompok', '.'], ['keragaman', 'sosial', 'budaya', 'di', 'masyarakat', 'dapat', 'terjadi', 'saat', 'berbagai', 'jenis', 'suku', 'dan', 'agama', 'yang', 'ada', 'di', 'suatu', 'ruang', 'bertemu', 'dan', 'berinteraksi', 'setiap', 'harinya', '.'], ['ruang', 'tersebut', 'adalah', 'ruang', 'yang', 'ada', 'pada', 'masyarakat', '.'], ['budaya', 'dapat', 'berupa', 'cara', 'hidup', 'masyarakat', ',', 'cara', 'berpakaian', ',', 'adat', 'istiadat', ',', 'jenis', 'mata', 'pecaharian', ',', 'dan', 'tata', 'upacara', 'keagamaan', '.'], ['keragaman', 'budaya', 'juga', 'mencakup', 'barang-barang', 'yang', 'dihasilkan', 'oleh', 'masyarakat', ',', 'seperti', 'senjata', ',', 'alat', 'bajak', 'sawah', ',', 'kitab', 'hukum', 'adat', ',', 'dan', 'tempat', 'tinggal', '.'], ['budaya', 'dapat', 'dianggap', 'sebagai', 'serangkaian', 'rancangan', 'untuk', 'bertahan', 'hidup', 'atau', 'alat', 'dari', 'praktik', ',', 'pengetahuan', ',', 'dan', 'simbol', 'yang', 'diperoleh', 'melalui', 'pembelajaran', ',', 'bukan', 'oleh', 'naluri', ',', 'yang', 'memungkinkan', 'orang', 'untuk', 'hidup', 'dalam', 'masyarakat', '.'], ['masyarakat', 'terdiri', 'dari', 'orang-orang', 'yang', 'berinteraksi', 'dan', 'berbagi', 'budaya', 'yang', 'sama', '.'], ['perbedaan', 'budaya', 'dapat', 'disebabkan', 'oleh', 'berbagai', 'hal', 'seperti', 'sejarah', ',', 'keturunan', ',', 'keyakinan', ',', 'dan', 'faktor', 'geografis', '.'], ['salah', 'satu', 'penyebab', 'perbedaan', 'budaya', 'adalah', 'faktor', 'geografis', '.'], ['faktor', 'geografis', 'yang', 'memengaruhi', 'keragaman', 'budaya', 'yang', 'akan', 'dibahas', 'berikut', 'ini'], ['dari', 'teks', 'tersebut', 'dapat', 'kita', 'pelajari', 'bahwa', 'budaya', 'yang', 'ada', 'di', 'masyarakat', 'dapat', 'dipengaruhi', 'oleh', 'lingkungan', 'yang', 'ada', 'di', 'sekitarnya', ','], ['misalnya', 'suku', 'lawu', 'dan', 'suku', 'bugis', 'yang', 'bermata', 'pencaharian', 'sebagai', 'nelayan', 'dengan', 'kapal', 'pinisinya', ','], ['sehingga', 'menjadi', 'sebuah', 'simbol', 'bahwa', 'indonesia', 'merupakan', 'negara', 'maritim', 'yang', 'kuat', 'dan', 'disegani', 'di', 'lautan', '.'], ['keragaman', 'budaya', 'dipengaruhi', 'oleh', 'lingkungan', 'fisik', '.'], ['manusia', 'sebagai', 'individu', 'adalah', 'kesatuan', 'jiwa', ',', 'raga', 'dan', 'kegiatan', 'atau', 'perilaku', 'pribadi', 'itu', 'sendiri', '.'], ['sebagai', 'individu', ',', 'dalam', 'pribadi', 'manusia', 'terdapat', 'tiga', 'unsur', ',', 'yaitu', 'nafsu', ',', 'semangat', ',', 'dan', 'intelegensi', '.'], ['kombinasi', 'dari', 'unsur', 'tersebut', 'menghasilkan', 'tingkah', 'laku', 'seseorang', 'yang', 'mencerminkan', 'karakter', 'atau', 'budayaanya', '.'], ['kesatuan', 'dari', 'kepribadian-kepribadian', 'seseorang', 'pada', 'suatu', 'daerah', 'yang', 'mempunyai', 'pola', 'yang', 'sama', 'dapat', 'membentuk', 'budaya', 'daerah', 'tersebut', 'yang', 'membedakan', 'dengan', 'tempat', 'lain', '.'], ['indonesia', 'memiliki', 'kebudayaan', 'yang', 'beragam', '.'], ['indonesia', 'memiliki', 'kekayaan', 'yang', 'begitu', 'besar', '.'], ['bukan', 'hanya', 'pemandangan', 'alam', 'budaya', ',', 'jauh', 'di', 'kedalaman', 'tanahnya', 'begitu', 'banyak', 'kandungan', 'mineral', 'berharga', '.'], ['selama', 'puluhan', 'tahun', ',', 'freeport', 'mengelola', 'tambang', 'mineral', 'di', 'tanah', 'papua', ',', 'indonesia', '.'], ['berdasarkan', 'laporan', 'keuangan', 'freeport', 'mcmorran', 'inc', 'periode', '2017', ',', 'freeport', 'indonesia', 'di', 'papua', 'tercatat', 'memiliki', '6', 'tambang', ',', 'yakni', 'grasberg', 'block', 'cave', ',', 'dmlz', ',', 'tambang', 'kucing', 'liar', ',', 'doz', ',', 'big', 'gossan', ',', 'dan', 'grasberg', 'open', 'pit', '.'], ['tambang', 'freeport', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'yaitu', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['sumber', 'daya', 'alam', 'yang', 'terdapat', 'pada', 'pertambangan', 'freeport', 'di', 'atas', 'merupakan', 'salah', 'satu', 'contoh', 'dari', 'berbagai', 'sumber', 'daya', 'yang', 'ada', 'di', 'indonesia', 'yang', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'seperti', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['kemudian', 'apa', 'sih', 'sumber', 'daya', 'alam', 'itu', '?'], ['apakah', 'ada', 'manfaatnya', 'untuk', 'kita', '?'], ['yuk', 'silahkan', 'simak', 'penjelasan', 'di', 'bawah', 'ini', '.'], ['sumber', 'daya', 'alam', 'merupakan', 'segala', 'sesuatu', 'yang', 'ada', 'di', 'permukaan', 'bumi', 'dan', 'dapat', 'dimanfaatkan', 'untuk', 'memenuhi', 'kebutuhan', 'manusia', '.'], ['potensi', 'sumber', 'daya', 'ini', 'mencakup', 'hal', 'yang', 'ada', 'di', 'udara', ',', 'daratan', ',', 'dan', 'perairan', '.'], ['berdasarkan', 'kelestariannya', ',', 'sumber', 'daya', 'alam', 'dapat', 'dibedakan', 'menjadi', 'dua', 'yaitu', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', '(', 'renewable', 'resources', ')', 'dan', 'tidak', 'dapat', 'diperbarui', '(', 'non', 'renewable', 'resource', ')', '.'], ['contoh', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', 'yaitu', 'seperti', 'air', ',', 'tanah', ',', 'dan', 'hutan', '.'], ['sedangkan', 'sumber', 'daya', 'alam', 'yang', 'tidak', 'dapat', 'diperbarui', 'seperti', 'minyak', 'bumi', 'dan', 'batu', 'bara', '.'], ['berikut', 'ini', 'merupakan', 'potensi', 'sumber', 'daya', 'alam', 'di', 'indonesia', 'yang', 'dirinci', 'menjadi', 'tiga', 'yaitu', 'sumber', 'daya', 'alam', 'hutan', ',', 'sumber', 'daya', 'alam', 'tambang', ',', 'dan', 'sumber', 'daya', 'alam', 'kemaritiman', '.'], ['indonesia', 'termasuk', 'negara', 'yang', 'memiliki', 'kekayaan', 'alam', 'yang', 'berlimpah', 'dibandingkan', 'negara-negara', 'yang', 'lain', '.'], ['potensi', 'sumber', 'daya', 'alam', 'indonesia', 'sangat', 'beraneka', 'ragam', '.'], ['bangsa', 'indonesia', 'memiliki', 'modal', 'penting', 'dalam', 'pembangunan', '.'], ['jumlah', 'penduduk', 'indonesia', 'yang', 'lebih', 'dari', '270', 'juta', 'merupakan', 'potensi', 'penting', 'dalam', 'pembangunan', '.'], ['pada', 'tahun', '2016', 'badan', 'pusat', 'statistik', 'mencatat', 'bahwa', 'di', 'indonesia', 'terdapat', 'angkatan', 'kerja', '127,67', 'juta', 'jiwa', '.'], ['di', 'antara', 'negara', 'asean', ',', 'kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'masih', 'berada', 'di', 'peringkat', 'bawah', '.'], ['kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'menempati', 'urutan', 'kelima', '.'], ['peringkat', 'ini', 'masih', 'kalah', 'jika', 'dibandingkan', 'singapura', ',', 'brunei', 'darussalam', ',', 'malaysia', ',', 'dan', 'thailand', '.'], ['kualitas', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'memengaruhi', 'terhadap', 'kemajuan', 'sebuah', 'bangsa', '.'], ['peristiwa', 'itu', 'dilatarbelakangi', 'oleh', 'peristiwa', 'yang', 'jauh', 'dari', 'indonesia', ',', 'misalnya', 'peristiwa', 'jatuhnya', 'konstantinopel', 'di', 'kawasan', 'laut', 'tengah', 'pada', 'tahun', '1453', '.'], ['kehidupan', 'global', 'semakin', 'berkembang', 'dengan', 'maraknya', 'penjelajahan', 'samudera', 'orang-orang', 'eropa', 'ke', 'dunia', 'timur', '.'], ['begitu', 'juga', 'peristiwa', 'kedatangan', 'bangsa', 'eropa', 'ke', 'indonesia', ',', 'telah', 'ikut', 'meningkatkan', 'kehidupan', 'global', '.'], ['pada', 'tahun', '1488', 'karena', 'serangan', 'ombak', 'besar', 'terpaksa', 'bartholomeus', 'diaz', 'mendarat', 'di', 'suatu', 'ujung', 'selatan', 'benua', 'afrika', '.'], ['pada', 'juli', '1497', 'vasco', 'da', 'gama', 'berangkat', 'dari', 'pelabuhan', 'lisabon', 'untuk', 'memulai', 'penjelajahan', 'samudra', '.'], ['berdasarkan', 'pengalaman', 'bartholomeus', 'diaz', 'tersebut', ',', 'vasco', 'da', 'gama', 'juga', 'berlayar', 'mengambil', 'rute', 'yang', 'pernah', 'dilayari', 'bartholomeus', 'diaz', '.'], ['rombongan', 'vasco', 'da', 'gama', 'juga', 'singgah', 'di', 'tanjung', 'harapan', '.'], ['atas', 'petunjuk', 'dari', 'pelaut', 'bangsa', 'moor', 'yang', 'telah', 'disewanya', ',', 'rombongan', 'vasco', 'da', 'gama', 'melanjutkan', 'penjelajahan', ',', 'berlayar', 'menelusuri', 'pantai', 'timur', 'afrika', 'kemudian', 'berbelok', 'ke', 'kanan', 'untuk', 'mengarungi', 'lautan', 'hindia', '(', 'samudra', 'indonesia', ')', '.'], ['pada', 'tahun', '1498', 'rombongan', 'vasco', 'da', 'gama', 'mendarat', 'sampai', 'di', 'kalikut', 'dan', 'juga', 'goa', 'di', 'pantai', 'barat', 'india', '.'], ['pada', 'tahun', '1511', 'armada', 'portugis', 'berhasil', 'menguasai', 'malaka', '.'], ['proklamasi', 'kemerdekaan', 'indonesia', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', 'di', 'hawaii', '.'], ['reformasi', 'indonesia', 'dimulai', 'tahun', '1998', 'setelah', 'soeharto', 'mundur', '.'], ['perang', 'dunia', 'ii', 'berakhir', 'pada', '2', 'september', '1945', '.'], ['indonesia', 'menjadi', 'anggota', 'pbb', 'sejak', '28', 'september', '1950', '.'], ['banjir', 'bandang', 'terjadi', 'pada', '5', 'januari', '2021', 'di', 'bandung', '.'], ['hari', 'pahlawan', 'diperingati', 'setiap', '10', 'november', '.'], ['pada', 'tahun', '1511', 'portugis', 'menguasai', 'malaka', '.'], ['konferensi', 'asia-afrika', 'diselenggarakan', 'tahun', '1955', 'di', 'bandung', '.'], ['musim', 'kemarau', 'diperkirakan', 'mulai', 'april', '2025', '.'], ['rapat', 'dimulai', 'pukul', '09.00', 'pagi', '.'], ['kereta', 'akan', 'tiba', 'sekitar', 'jam', '3', 'sore', '.'], ['pertandingan', 'akan', 'dimulai', 'pada', 'pukul', '19.30', '.'], ['matahari', 'terbit', 'sekitar', '05.45', 'pagi', 'di', 'jakarta', '.'], ['makan', 'siang', 'biasanya', 'dilakukan', 'sekitar', 'jam', '12', 'siang', '.'], ['penerbangan', 'dijadwalkan', 'lepas', 'landas', 'pukul', '23.15', '.'], ['film', 'tayang', 'mulai', 'jam', '8', 'malam', 'nanti', '.'], ['pesawat', 'mendarat', 'tepat', 'pada', '00.30', 'dinihari', '.'], ['siaran', 'langsung', 'dimulai', 'pukul', '18.00', '.'], ['jam', 'kerja', 'dimulai', 'pukul', '08.00', 'dan', 'berakhir', 'pukul', '17.00', '.'], ['alarm', 'berbunyi', 'pada', 'pukul', '06.00', 'pagi', '.'], ['saya', 'bangun', 'sekitar', 'jam', '5', 'pagi', 'setiap', 'hari', '.'], ['konser', 'dimulai', 'sekitar', '20.00', 'malam', 'di', 'stadion', '.'], ['wawancara', 'dijadwalkan', 'pada', 'jam', '11', 'pagi', '.'], ['kami', 'tiba', 'di', 'bandara', 'sekitar', 'jam', '2', 'dinihari', '.'], ['dia', 'mengajar', 'kelas', 'pada', 'pukul', '13.00', '.'], ['peserta', 'diminta', 'hadir', 'sebelum', 'jam', '7', 'pagi', '.'], ['televisi', 'menayangkan', 'berita', 'malam', 'pada', '22.00', '.'], ['kami', 'akan', 'bertemu', 'jam', '10', 'malam', 'di', 'kafe', '.'], ['toko', 'buka', 'hingga', 'pukul', '21.00', '.'], ['dia', 'biasanya', 'berolahraga', 'pada', 'pagi', 'hari', '.'], ['kami', 'bertemu', 'lagi', 'pada', 'malam', 'hari', 'itu', '.'], ['upacara', 'dilaksanakan', 'pada', 'sore', 'hari', 'di', 'lapangan', '.'], ['ia', 'pulang', 'setiap', 'malam', 'sekitar', 'jam', '9', '.'], ['kami', 'berangkat', 'di', 'pagi', 'hari', 'menggunakan', 'mobil', '.'], ['acara', 'berlangsung', 'hingga', 'malam', 'hari', '.'], ['kami', 'tiba', 'di', 'bandara', 'pada', 'dinihari', '.'], ['pintu', 'gerbang', 'dibuka', 'setiap', 'pagi', '.'], ['ia', 'selalu', 'belajar', 'di', 'malam', '.'], ['waktu', 'bermain', 'dimulai', 'sore', 'hari', '.'], ['pelajaran', 'kedua', 'dimulai', 'sekitar', 'jam', 'tujuh', 'lebih', 'sepuluh', 'menit', '.'], ['bus', 'berangkat', 'kurang', 'lebih', 'jam', 'delapan', 'malam', '.'], ['pertemuan', 'terakhir', 'dilaksanakan', 'sebelum', 'matahari', 'terbenam', '.'], ['kereta', 'berangkat', 'sekitar', 'tengah', 'malam', 'dari', 'stasiun', 'gambir', '.'], ['jadwal', 'sholat', 'dimulai', 'pukul', 'empat', 'lebih', 'lima', 'menit', '.'], ['pemadaman', 'listrik', 'akan', 'dimulai', 'menjelang', 'malam', '.'], ['layanan', 'pelanggan', 'dibuka', 'setiap', 'hari', 'kerja', 'jam', 'sembilan', '.'], ['ia', 'terjaga', 'di', 'tengah', 'malam', 'karena', 'petir', '.'], ['kelas', 'selesai', 'sekitar', 'jam', 'dua', 'kurang', 'seperempat', '.'], ['waktu', 'sarapan', 'dimulai', 'pukul', '6.30', 'hingga', '7.30', '.'], ['proklamasi', 'kemerdekaan', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['indonesia', 'merdeka', 'pada', 'tahun', '1945', '.'], ['pemilu', 'diadakan', 'pada', '14', 'februari', '2024', '.'], ['tanggal', '1', 'januari', '2023', 'merupakan', 'hari', 'libur', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', '.'], ['hari', 'bumi', 'diperingati', 'setiap', '22', 'april', '.'], ['musim', 'kemarau', 'terjadi', 'antara', 'bulan', 'april', 'hingga', 'oktober', '.'], ['reformasi', '1998', 'mengubah', 'sistem', 'politik', 'indonesia', '.'], ['konferensi', 'asia-afrika', 'digelar', 'pada', 'tahun', '1955', 'di', 'bandung', '.'], ['perang', 'dunia', 'kedua', 'berakhir', 'tahun', '1945', '.'], ['sumpah', 'pemuda', 'diperingati', 'setiap', '28', 'oktober', '.'], ['habibie', 'dilantik', 'menjadi', 'presiden', 'pada', '21', 'mei', '1998', '.'], ['hari', 'kemerdekaan', 'indonesia', 'dirayakan', 'setiap', '17', 'agustus', '.'], ['pada', 'tahun', '1949', ',', 'belanda', 'mengakui', 'kemerdekaan', 'indonesia', '.'], ['tsunami', 'aceh', 'terjadi', 'pada', '26', 'desember', '2004', '.'], ['bung', 'karno', 'meninggal', 'pada', '21', 'juni', '1970', '.'], ['jakarta', 'ditetapkan', 'sebagai', 'ibu', 'kota', 'negara', 'pada', 'tahun', '1961', '.'], ['pada', '1955', ',', 'indonesia', 'menjadi', 'tuan', 'rumah', 'konferensi', 'asia-afrika', '.'], ['pemerintah', 'mengumumkan', 'kebijakan', 'psbb', 'pada', 'april', '2020', 'di', 'jakarta', '.'], ['undang-undang', 'dasar', '1945', 'disahkan', 'pada', 'tanggal', '18', 'agustus', '1945', '.']]\n",
+ "new [['keberagaman', 'potensi', 'sumber', 'daya', 'alam', 'indonesia', 'tidak', 'lepas', 'dari', 'proses', 'geografis', 'yang', 'terjadi', '.'], ['bagaimana', 'proses', 'geografis', 'di', 'indonesia', '?'], ['bagaimana', 'pengaruh', 'proses', 'geografis', 'bagi', 'keragaman', 'alam', 'dan', 'keragaman', 'sosial', 'masyarakat', 'indonesia', '?'], ['bagaimana', 'mengoptimalkan', 'peranan', 'sumber', 'daya', 'manusia', 'dalam', 'mengelola', 'sumber', 'daya', 'alam', 'indonesia', '?'], ['apakah', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'sudah', 'memenuhi', 'syarat', 'untuk', 'mengolah', 'pariwisata', 'yang', 'dimilikinya', '?'], ['bagaimana', 'lembaga', 'sosial', 'yang', 'akan', 'mewadahi', 'untuk', 'mengolah', 'sumber', 'daya', 'alam', 'dan', 'sumber', 'daya', 'manusianya', '?'], ['kalian', 'juga', 'perlu', 'memahami', ',', 'bahwa', 'keragaman', 'sosial', 'dan', 'budaya', 'telah', 'menarik', 'kedatangan', 'bangsa-bangsa', 'asing', 'sejak', 'ribuan', 'tahun', 'yang', 'lalu', '.'], ['perkembangan', 'hindu-buddha', 'di', 'indonesia', 'tidak', 'lepas', 'dari', 'perkembangan', 'perdagangan', 'dan', 'pelayaran', 'pada', 'awal', 'abad', 'masehi', '.'], ['bangsa', 'indonesia', 'patut', 'bersyukur', 'karena', 'proses', 'geografis', 'dan', 'keragaman', 'alam', 'yang', 'dimiliki', '.'], ['indonesia', 'merupakan', 'negara', 'terluas', 'di', 'asia', 'tenggara', '.'], ['luas', 'daratan', 'indonesia', 'sebesar', '1.910.932,37', 'km2', '.'], ['dan', 'lautan', 'indonesia', 'mencapai', '5,8', 'juta', 'km2', '.'], ['letak', 'indonesia', 'sangat', 'menguntungkan', 'bagi', 'kehidupan', 'masyarakat', '.'], ['selain', 'memiliki', 'letak', 'geografis', 'yang', 'sangat', 'menguntungkan', ',', 'indonesia', 'juga', 'memiliki', 'letak', 'geologis', ',', 'iklim', ',', 'dan', 'cuaca', 'yang', 'sangat', 'menguntungkan', '.'], ['kalian', 'tentu', 'sering', 'membincangkan', 'tentang', 'musim', 'dan', 'hubungannya', 'dengan', 'aktivitas', 'sehari-hari', '.'], ['masyarakat', 'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober', '-', 'maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april', '-', 'september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman'], ['penyinaran', 'matahari', 'memengaruhi', 'fotosintesis', 'tanaman', ',', 'dapat', 'meningkatkan', 'suhu', 'udara', '.'], ['suhu', 'mengurangi', 'kadar', 'air', 'sehingga', 'cenderung', 'menjadi', 'kering', '.'], ['kelembaban', 'membatasi', 'hilangnya', 'air', '.'], ['angin', 'membantu', 'proses', 'penyerbukan', 'secara', 'alami', ',', 'mengurangi', 'kadar', 'air', '.'], ['hujan', 'meningkatkan', 'kadar', 'air', ',', 'mengikis', 'tanah', '.'], ['kalian', 'menemukan', 'berbagai', 'perbedaan', 'sosial', 'budaya', 'masyarakat', 'di', 'sekitar', 'tempat', 'tinggalmu', '.'], ['apabila', 'kalian', 'tinggal', 'di', 'perkotaan', ',', 'perbedaan', 'sosial', 'budaya', 'akan', 'semakin', 'banyak', '.'], ['perbedaan', 'sosial', 'budaya', 'meliputi', 'perbedaan', 'nilai-nilai', ',', 'norma', ',', 'dan', 'karakteristik', 'dari', 'suatu', 'kelompok', '.'], ['keragaman', 'sosial', 'budaya', 'di', 'masyarakat', 'dapat', 'terjadi', 'saat', 'berbagai', 'jenis', 'suku', 'dan', 'agama', 'yang', 'ada', 'di', 'suatu', 'ruang', 'bertemu', 'dan', 'berinteraksi', 'setiap', 'harinya', '.'], ['ruang', 'tersebut', 'adalah', 'ruang', 'yang', 'ada', 'pada', 'masyarakat', '.'], ['budaya', 'dapat', 'berupa', 'cara', 'hidup', 'masyarakat', ',', 'cara', 'berpakaian', ',', 'adat', 'istiadat', ',', 'jenis', 'mata', 'pecaharian', ',', 'dan', 'tata', 'upacara', 'keagamaan', '.'], ['keragaman', 'budaya', 'juga', 'mencakup', 'barang-barang', 'yang', 'dihasilkan', 'oleh', 'masyarakat', ',', 'seperti', 'senjata', ',', 'alat', 'bajak', 'sawah', ',', 'kitab', 'hukum', 'adat', ',', 'dan', 'tempat', 'tinggal', '.'], ['budaya', 'dapat', 'dianggap', 'sebagai', 'serangkaian', 'rancangan', 'untuk', 'bertahan', 'hidup', 'atau', 'alat', 'dari', 'praktik', ',', 'pengetahuan', ',', 'dan', 'simbol', 'yang', 'diperoleh', 'melalui', 'pembelajaran', ',', 'bukan', 'oleh', 'naluri', ',', 'yang', 'memungkinkan', 'orang', 'untuk', 'hidup', 'dalam', 'masyarakat', '.'], ['masyarakat', 'terdiri', 'dari', 'orang-orang', 'yang', 'berinteraksi', 'dan', 'berbagi', 'budaya', 'yang', 'sama', '.'], ['perbedaan', 'budaya', 'dapat', 'disebabkan', 'oleh', 'berbagai', 'hal', 'seperti', 'sejarah', ',', 'keturunan', ',', 'keyakinan', ',', 'dan', 'faktor', 'geografis', '.'], ['salah', 'satu', 'penyebab', 'perbedaan', 'budaya', 'adalah', 'faktor', 'geografis', '.'], ['faktor', 'geografis', 'yang', 'memengaruhi', 'keragaman', 'budaya', 'yang', 'akan', 'dibahas', 'berikut', 'ini'], ['dari', 'teks', 'tersebut', 'dapat', 'kita', 'pelajari', 'bahwa', 'budaya', 'yang', 'ada', 'di', 'masyarakat', 'dapat', 'dipengaruhi', 'oleh', 'lingkungan', 'yang', 'ada', 'di', 'sekitarnya', ','], ['misalnya', 'suku', 'lawu', 'dan', 'suku', 'bugis', 'yang', 'bermata', 'pencaharian', 'sebagai', 'nelayan', 'dengan', 'kapal', 'pinisinya', ','], ['sehingga', 'menjadi', 'sebuah', 'simbol', 'bahwa', 'indonesia', 'merupakan', 'negara', 'maritim', 'yang', 'kuat', 'dan', 'disegani', 'di', 'lautan', '.'], ['keragaman', 'budaya', 'dipengaruhi', 'oleh', 'lingkungan', 'fisik', '.'], ['manusia', 'sebagai', 'individu', 'adalah', 'kesatuan', 'jiwa', ',', 'raga', 'dan', 'kegiatan', 'atau', 'perilaku', 'pribadi', 'itu', 'sendiri', '.'], ['sebagai', 'individu', ',', 'dalam', 'pribadi', 'manusia', 'terdapat', 'tiga', 'unsur', ',', 'yaitu', 'nafsu', ',', 'semangat', ',', 'dan', 'intelegensi', '.'], ['kombinasi', 'dari', 'unsur', 'tersebut', 'menghasilkan', 'tingkah', 'laku', 'seseorang', 'yang', 'mencerminkan', 'karakter', 'atau', 'budayaanya', '.'], ['kesatuan', 'dari', 'kepribadian-kepribadian', 'seseorang', 'pada', 'suatu', 'daerah', 'yang', 'mempunyai', 'pola', 'yang', 'sama', 'dapat', 'membentuk', 'budaya', 'daerah', 'tersebut', 'yang', 'membedakan', 'dengan', 'tempat', 'lain', '.'], ['indonesia', 'memiliki', 'kebudayaan', 'yang', 'beragam', '.'], ['indonesia', 'memiliki', 'kekayaan', 'yang', 'begitu', 'besar', '.'], ['bukan', 'hanya', 'pemandangan', 'alam', 'budaya', ',', 'jauh', 'di', 'kedalaman', 'tanahnya', 'begitu', 'banyak', 'kandungan', 'mineral', 'berharga', '.'], ['selama', 'puluhan', 'tahun', ',', 'freeport', 'mengelola', 'tambang', 'mineral', 'di', 'tanah', 'papua', ',', 'indonesia', '.'], ['berdasarkan', 'laporan', 'keuangan', 'freeport', 'mcmorran', 'inc', 'periode', '2017', ',', 'freeport', 'indonesia', 'di', 'papua', 'tercatat', 'memiliki', '6', 'tambang', ',', 'yakni', 'grasberg', 'block', 'cave', ',', 'dmlz', ',', 'tambang', 'kucing', 'liar', ',', 'doz', ',', 'big', 'gossan', ',', 'dan', 'grasberg', 'open', 'pit', '.'], ['tambang', 'freeport', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'yaitu', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['sumber', 'daya', 'alam', 'yang', 'terdapat', 'pada', 'pertambangan', 'freeport', 'di', 'atas', 'merupakan', 'salah', 'satu', 'contoh', 'dari', 'berbagai', 'sumber', 'daya', 'yang', 'ada', 'di', 'indonesia', 'yang', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'seperti', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['kemudian', 'apa', 'sih', 'sumber', 'daya', 'alam', 'itu', '?'], ['apakah', 'ada', 'manfaatnya', 'untuk', 'kita', '?'], ['yuk', 'silahkan', 'simak', 'penjelasan', 'di', 'bawah', 'ini', '.'], ['sumber', 'daya', 'alam', 'merupakan', 'segala', 'sesuatu', 'yang', 'ada', 'di', 'permukaan', 'bumi', 'dan', 'dapat', 'dimanfaatkan', 'untuk', 'memenuhi', 'kebutuhan', 'manusia', '.'], ['potensi', 'sumber', 'daya', 'ini', 'mencakup', 'hal', 'yang', 'ada', 'di', 'udara', ',', 'daratan', ',', 'dan', 'perairan', '.'], ['berdasarkan', 'kelestariannya', ',', 'sumber', 'daya', 'alam', 'dapat', 'dibedakan', 'menjadi', 'dua', 'yaitu', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', '(', 'renewable', 'resources', ')', 'dan', 'tidak', 'dapat', 'diperbarui', '(', 'non', 'renewable', 'resource', ')', '.'], ['contoh', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', 'yaitu', 'seperti', 'air', ',', 'tanah', ',', 'dan', 'hutan', '.'], ['sedangkan', 'sumber', 'daya', 'alam', 'yang', 'tidak', 'dapat', 'diperbarui', 'seperti', 'minyak', 'bumi', 'dan', 'batu', 'bara', '.'], ['berikut', 'ini', 'merupakan', 'potensi', 'sumber', 'daya', 'alam', 'di', 'indonesia', 'yang', 'dirinci', 'menjadi', 'tiga', 'yaitu', 'sumber', 'daya', 'alam', 'hutan', ',', 'sumber', 'daya', 'alam', 'tambang', ',', 'dan', 'sumber', 'daya', 'alam', 'kemaritiman', '.'], ['indonesia', 'termasuk', 'negara', 'yang', 'memiliki', 'kekayaan', 'alam', 'yang', 'berlimpah', 'dibandingkan', 'negara-negara', 'yang', 'lain', '.'], ['potensi', 'sumber', 'daya', 'alam', 'indonesia', 'sangat', 'beraneka', 'ragam', '.'], ['bangsa', 'indonesia', 'memiliki', 'modal', 'penting', 'dalam', 'pembangunan', '.'], ['jumlah', 'penduduk', 'indonesia', 'yang', 'lebih', 'dari', '270', 'juta', 'merupakan', 'potensi', 'penting', 'dalam', 'pembangunan', '.'], ['pada', 'tahun', '2016', 'badan', 'pusat', 'statistik', 'mencatat', 'bahwa', 'di', 'indonesia', 'terdapat', 'angkatan', 'kerja', '127,67', 'juta', 'jiwa', '.'], ['di', 'antara', 'negara', 'asean', ',', 'kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'masih', 'berada', 'di', 'peringkat', 'bawah', '.'], ['kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'menempati', 'urutan', 'kelima', '.'], ['peringkat', 'ini', 'masih', 'kalah', 'jika', 'dibandingkan', 'singapura', ',', 'brunei', 'darussalam', ',', 'malaysia', ',', 'dan', 'thailand', '.'], ['kualitas', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'memengaruhi', 'terhadap', 'kemajuan', 'sebuah', 'bangsa', '.'], ['peristiwa', 'itu', 'dilatarbelakangi', 'oleh', 'peristiwa', 'yang', 'jauh', 'dari', 'indonesia', ',', 'misalnya', 'peristiwa', 'jatuhnya', 'konstantinopel', 'di', 'kawasan', 'laut', 'tengah', 'pada', 'tahun', '1453', '.'], ['kehidupan', 'global', 'semakin', 'berkembang', 'dengan', 'maraknya', 'penjelajahan', 'samudera', 'orang-orang', 'eropa', 'ke', 'dunia', 'timur', '.'], ['begitu', 'juga', 'peristiwa', 'kedatangan', 'bangsa', 'eropa', 'ke', 'indonesia', ',', 'telah', 'ikut', 'meningkatkan', 'kehidupan', 'global', '.'], ['pada', 'tahun', '1488', 'karena', 'serangan', 'ombak', 'besar', 'terpaksa', 'bartholomeus', 'diaz', 'mendarat', 'di', 'suatu', 'ujung', 'selatan', 'benua', 'afrika', '.'], ['pada', 'juli', '1497', 'vasco', 'da', 'gama', 'berangkat', 'dari', 'pelabuhan', 'lisabon', 'untuk', 'memulai', 'penjelajahan', 'samudra', '.'], ['berdasarkan', 'pengalaman', 'bartholomeus', 'diaz', 'tersebut', ',', 'vasco', 'da', 'gama', 'juga', 'berlayar', 'mengambil', 'rute', 'yang', 'pernah', 'dilayari', 'bartholomeus', 'diaz', '.'], ['rombongan', 'vasco', 'da', 'gama', 'juga', 'singgah', 'di', 'tanjung', 'harapan', '.'], ['atas', 'petunjuk', 'dari', 'pelaut', 'bangsa', 'moor', 'yang', 'telah', 'disewanya', ',', 'rombongan', 'vasco', 'da', 'gama', 'melanjutkan', 'penjelajahan', ',', 'berlayar', 'menelusuri', 'pantai', 'timur', 'afrika', 'kemudian', 'berbelok', 'ke', 'kanan', 'untuk', 'mengarungi', 'lautan', 'hindia', '(', 'samudra', 'indonesia', ')', '.'], ['pada', 'tahun', '1498', 'rombongan', 'vasco', 'da', 'gama', 'mendarat', 'sampai', 'di', 'kalikut', 'dan', 'juga', 'goa', 'di', 'pantai', 'barat', 'india', '.'], ['pada', 'tahun', '1511', 'armada', 'portugis', 'berhasil', 'menguasai', 'malaka', '.'], ['proklamasi', 'kemerdekaan', 'indonesia', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', 'di', 'hawaii', '.'], ['reformasi', 'indonesia', 'dimulai', 'tahun', '1998', 'setelah', 'soeharto', 'mundur', '.'], ['perang', 'dunia', 'ii', 'berakhir', 'pada', '2', 'september', '1945', '.'], ['indonesia', 'menjadi', 'anggota', 'pbb', 'sejak', '28', 'september', '1950', '.'], ['banjir', 'bandang', 'terjadi', 'pada', '5', 'januari', '2021', 'di', 'bandung', '.'], ['hari', 'pahlawan', 'diperingati', 'setiap', '10', 'november', '.'], ['pada', 'tahun', '1511', 'portugis', 'menguasai', 'malaka', '.'], ['konferensi', 'asia-afrika', 'diselenggarakan', 'tahun', '1955', 'di', 'bandung', '.'], ['musim', 'kemarau', 'diperkirakan', 'mulai', 'april', '2025', '.'], ['rapat', 'dimulai', 'pukul', '09.00', 'pagi', '.'], ['kereta', 'akan', 'tiba', 'sekitar', 'jam', '3', 'sore', '.'], ['pertandingan', 'akan', 'dimulai', 'pada', 'pukul', '19.30', '.'], ['matahari', 'terbit', 'sekitar', '05.45', 'pagi', 'di', 'jakarta', '.'], ['makan', 'siang', 'biasanya', 'dilakukan', 'sekitar', 'jam', '12', 'siang', '.'], ['penerbangan', 'dijadwalkan', 'lepas', 'landas', 'pukul', '23.15', '.'], ['film', 'tayang', 'mulai', 'jam', '8', 'malam', 'nanti', '.'], ['pesawat', 'mendarat', 'tepat', 'pada', '00.30', 'dinihari', '.'], ['siaran', 'langsung', 'dimulai', 'pukul', '18.00', '.'], ['jam', 'kerja', 'dimulai', 'pukul', '08.00', 'dan', 'berakhir', 'pukul', '17.00', '.'], ['alarm', 'berbunyi', 'pada', 'pukul', '06.00', 'pagi', '.'], ['saya', 'bangun', 'sekitar', 'jam', '5', 'pagi', 'setiap', 'hari', '.'], ['konser', 'dimulai', 'sekitar', '20.00', 'malam', 'di', 'stadion', '.'], ['wawancara', 'dijadwalkan', 'pada', 'jam', '11', 'pagi', '.'], ['kami', 'tiba', 'di', 'bandara', 'sekitar', 'jam', '2', 'dinihari', '.'], ['dia', 'mengajar', 'kelas', 'pada', 'pukul', '13.00', '.'], ['peserta', 'diminta', 'hadir', 'sebelum', 'jam', '7', 'pagi', '.'], ['televisi', 'menayangkan', 'berita', 'malam', 'pada', '22.00', '.'], ['kami', 'akan', 'bertemu', 'jam', '10', 'malam', 'di', 'kafe', '.'], ['toko', 'buka', 'hingga', 'pukul', '21.00', '.'], ['dia', 'biasanya', 'berolahraga', 'pada', 'pagi', 'hari', '.'], ['kami', 'bertemu', 'lagi', 'pada', 'malam', 'hari', 'itu', '.'], ['upacara', 'dilaksanakan', 'pada', 'sore', 'hari', 'di', 'lapangan', '.'], ['ia', 'pulang', 'setiap', 'malam', 'sekitar', 'jam', '9', '.'], ['kami', 'berangkat', 'di', 'pagi', 'hari', 'menggunakan', 'mobil', '.'], ['acara', 'berlangsung', 'hingga', 'malam', 'hari', '.'], ['kami', 'tiba', 'di', 'bandara', 'pada', 'dinihari', '.'], ['pintu', 'gerbang', 'dibuka', 'setiap', 'pagi', '.'], ['ia', 'selalu', 'belajar', 'di', 'malam', '.'], ['waktu', 'bermain', 'dimulai', 'sore', 'hari', '.'], ['pelajaran', 'kedua', 'dimulai', 'sekitar', 'jam', 'tujuh', 'lebih', 'sepuluh', 'menit', '.'], ['bus', 'berangkat', 'kurang', 'lebih', 'jam', 'delapan', 'malam', '.'], ['pertemuan', 'terakhir', 'dilaksanakan', 'sebelum', 'matahari', 'terbenam', '.'], ['kereta', 'berangkat', 'sekitar', 'tengah', 'malam', 'dari', 'stasiun', 'gambir', '.'], ['jadwal', 'sholat', 'dimulai', 'pukul', 'empat', 'lebih', 'lima', 'menit', '.'], ['pemadaman', 'listrik', 'akan', 'dimulai', 'menjelang', 'malam', '.'], ['layanan', 'pelanggan', 'dibuka', 'setiap', 'hari', 'kerja', 'jam', 'sembilan', '.'], ['ia', 'terjaga', 'di', 'tengah', 'malam', 'karena', 'petir', '.'], ['kelas', 'selesai', 'sekitar', 'jam', 'dua', 'kurang', 'seperempat', '.'], ['waktu', 'sarapan', 'dimulai', 'pukul', '6.30', 'hingga', '7.30', '.'], ['proklamasi', 'kemerdekaan', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['indonesia', 'merdeka', 'pada', 'tahun', '1945', '.'], ['pemilu', 'diadakan', 'pada', '14', 'februari', '2024', '.'], ['tanggal', '1', 'januari', '2023', 'merupakan', 'hari', 'libur', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', '.'], ['hari', 'bumi', 'diperingati', 'setiap', '22', 'april', '.'], ['musim', 'kemarau', 'terjadi', 'antara', 'bulan', 'april', 'hingga', 'oktober', '.'], ['reformasi', '1998', 'mengubah', 'sistem', 'politik', 'indonesia', '.'], ['konferensi', 'asia-afrika', 'digelar', 'pada', 'tahun', '1955', 'di', 'bandung', '.'], ['perang', 'dunia', 'kedua', 'berakhir', 'tahun', '1945', '.'], ['sumpah', 'pemuda', 'diperingati', 'setiap', '28', 'oktober', '.'], ['habibie', 'dilantik', 'menjadi', 'presiden', 'pada', '21', 'mei', '1998', '.'], ['hari', 'kemerdekaan', 'indonesia', 'dirayakan', 'setiap', '17', 'agustus', '.'], ['pada', 'tahun', '1949', ',', 'belanda', 'mengakui', 'kemerdekaan', 'indonesia', '.'], ['tsunami', 'aceh', 'terjadi', 'pada', '26', 'desember', '2004', '.'], ['bung', 'karno', 'meninggal', 'pada', '21', 'juni', '1970', '.'], ['jakarta', 'ditetapkan', 'sebagai', 'ibu', 'kota', 'negara', 'pada', 'tahun', '1961', '.'], ['pada', '1955', ',', 'indonesia', 'menjadi', 'tuan', 'rumah', 'konferensi', 'asia-afrika', '.'], ['pemerintah', 'mengumumkan', 'kebijakan', 'psbb', 'pada', 'april', '2020', 'di', 'jakarta', '.'], ['undang-undang', 'dasar', '1945', 'disahkan', 'pada', 'tanggal', '18', 'agustus', '1945', '.']] \n",
+ " 158\n"
+ ]
+ }
+ ],
+ "source": [
+ "# text preprocessing\n",
+ "stop_words = set(stopwords.words(\"indonesian\")) \n",
+ "factory = StemmerFactory()\n",
+ "stemmer = factory.create_stemmer()\n",
+ "\n",
+ "with open(\"../normalize_text/normalize.json\", \"r\", encoding=\"utf-8\") as file:\n",
+ " normalization_dict = json.load(file)\n",
+ " \n",
+ "def text_preprocessing(text):\n",
+ " \n",
+ " # if(text == \"?\" or text == \".\" or text == \"!\"): return text\n",
+ " # lowercase\n",
+ " text = text.lower()\n",
+ " \n",
+ " # remove punctuation\n",
+ " # text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
+ " \n",
+ " # remove extra spaces\n",
+ " text = re.sub(r\"\\s+\", \" \", text).strip()\n",
+ " \n",
+ " # tokenize\n",
+ " # tokens = word_tokenize(text)\n",
+ " \n",
+ " # normalization\n",
+ " # tokens = normalization_dict.get(text, text) \n",
+ " \n",
+ " \n",
+ " # stemming\n",
+ " # tokens = stemmer.stem(tokens)\n",
+ " \n",
+ " \n",
+ " # remove stopwords\n",
+ " # tokens = [word for word in tokens if word not in stop_words]\n",
+ " \n",
+ " # print(f\"Original: {text}\")\n",
+ " # print(f\"Normalized: {tokens}\")\n",
+ " \n",
+ " return text\n",
+ "\n",
+ "# sentences = [text_preprocessing(\" \".join(sentence)) for sentence in sentences]\n",
+ "print(\"old\", sentences)\n",
+ "preprocessing_sentences = []\n",
+ "\n",
+ "for text in sentences:\n",
+ " result = []\n",
+ " for i in range(len(text)):\n",
+ " text[i] = text_preprocessing(text[i])\n",
+ " result.append(text[i])\n",
+ " preprocessing_sentences.append(result)\n",
+ "\n",
+ "print(\"new\", preprocessing_sentences, \"\\n\", len(preprocessing_sentences))\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e9653d99",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['alarm', 'sempit', 'sore', '05.45', 'pecaharian', 'alat', 'langsung', 'pantai', 'kebijakan', 'dipengaruhi', 'hidrat', 'perak', '11', 'membatasi', 'wilayah', 'perairan', 'kedatangan', 'diselenggarakan', 'jauh', 'penyerbukan', 'barang-barang', 'tiba', 'sama', 'diaz', 'ombak', 'terluas', 'negara-negara', 'menarik', 'kering', 'mewadahi', 'semakin', '1998', 'sendiri', 'keturunan', 'simbol', 'diminta', 'sekitarnya', 'kemarau', 'bartholomeus', 'februari', 'keberagaman', '?', 'kanan', 'pertambangan', 'pbb', 'harian', '1949', 'jatuhnya', 'curah', 'faktor', 'sangat', 'sistem', 'tropis', 'terdiri', 'anggota', 'perkembangan', 'ada', 'pengalaman', 'kapal', 'beraneka', 'resources', 'perang', 'penduduk', 'alami', 'geografis', 'disebabkan', 'nelayan', 'manusia', 'mengolah', 'luas', 'toko', 'lembaga', 'menjadi', 'waktu', 'suatu', '2004', 'ini', 'setelah', 'pit', '3', 'buka', 'agama', 'mengoptimalkan', 'rata-rata', 'terjadi', 'tentang', 'pemuda', 'permukaan', 'disegani', 'kelembaban', '1950', 'makanan', 'kandungan', 'politik', 'bagaimana', 'rapat', 'perilaku', 'sumpah', 'tentu', 'mineral', '2016', 'meningkatkan', 'budayaanya', '2021', 'konser', 'mempunyai', 'bukan', 'bara', 'terjaga', '2025', 'mengarungi', 'hilangnya', 'tanah', 'gerbang', '1497', 'menjelang', '127,67', 'bahan', 'sepuluh', 'rombongan', 'sesuatu', 'kebutuhan', 'hobi', 'pembangunan', 'terpaksa', 'lima', 'alam', 'ikut', 'geologis', 'puluhan', 'dinihari', 'bagian', 'tercatat', 'menayangkan', 'kondisi', 'unsur', 'kesenangan', 'tanggal', 'siang', 'kurang', 'ruang', 'jam', 'laku', 'memahami', 'diperoleh', 'kebiasaan', 'liar', 'bung', 'diamati', 'dasar', 'meninggal', 'maraknya', 'dimilikinya', 'sebuah', 'bermain', '(', 'berkembang', 'selama', 'bertemu', 'pelajaran', 'intelegensi', 'oktober', 'ketenagakerjaan', 'budaya', 'modal', 'keuangan', 'sdm', 'ujung', 'angin', 'lahir', 'kualitas', 'malaka', 'mobil', 'kekayaan', 'daratan', 'urutan', 'dilatarbelakangi', 'listrik', 'kehidupan', 'ke', 'lepas', 'asing', 'berhasil', 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+ "{'alarm': 2, 'sempit': 3, 'sore': 4, '05.45': 5, 'pecaharian': 6, 'alat': 7, 'langsung': 8, 'pantai': 9, 'kebijakan': 10, 'dipengaruhi': 11, 'hidrat': 12, 'perak': 13, '11': 14, 'membatasi': 15, 'wilayah': 16, 'perairan': 17, 'kedatangan': 18, 'diselenggarakan': 19, 'jauh': 20, 'penyerbukan': 21, 'barang-barang': 22, 'tiba': 23, 'sama': 24, 'diaz': 25, 'ombak': 26, 'terluas': 27, 'negara-negara': 28, 'menarik': 29, 'kering': 30, 'mewadahi': 31, 'semakin': 32, '1998': 33, 'sendiri': 34, 'keturunan': 35, 'simbol': 36, 'diminta': 37, 'sekitarnya': 38, 'kemarau': 39, 'bartholomeus': 40, 'februari': 41, 'keberagaman': 42, '?': 43, 'kanan': 44, 'pertambangan': 45, 'pbb': 46, 'harian': 47, '1949': 48, 'jatuhnya': 49, 'curah': 50, 'faktor': 51, 'sangat': 52, 'sistem': 53, 'tropis': 54, 'terdiri': 55, 'anggota': 56, 'perkembangan': 57, 'ada': 58, 'pengalaman': 59, 'kapal': 60, 'beraneka': 61, 'resources': 62, 'perang': 63, 'penduduk': 64, 'alami': 65, 'geografis': 66, 'disebabkan': 67, 'nelayan': 68, 'manusia': 69, 'mengolah': 70, 'luas': 71, 'toko': 72, 'lembaga': 73, 'menjadi': 74, 'waktu': 75, 'suatu': 76, '2004': 77, 'ini': 78, 'setelah': 79, 'pit': 80, '3': 81, 'buka': 82, 'agama': 83, 'mengoptimalkan': 84, 'rata-rata': 85, 'terjadi': 86, 'tentang': 87, 'pemuda': 88, 'permukaan': 89, 'disegani': 90, 'kelembaban': 91, '1950': 92, 'makanan': 93, 'kandungan': 94, 'politik': 95, 'bagaimana': 96, 'rapat': 97, 'perilaku': 98, 'sumpah': 99, 'tentu': 100, 'mineral': 101, '2016': 102, 'meningkatkan': 103, 'budayaanya': 104, '2021': 105, 'konser': 106, 'mempunyai': 107, 'bukan': 108, 'bara': 109, 'terjaga': 110, '2025': 111, 'mengarungi': 112, 'hilangnya': 113, 'tanah': 114, 'gerbang': 115, '1497': 116, 'menjelang': 117, '127,67': 118, 'bahan': 119, 'sepuluh': 120, 'rombongan': 121, 'sesuatu': 122, 'kebutuhan': 123, 'hobi': 124, 'pembangunan': 125, 'terpaksa': 126, 'lima': 127, 'alam': 128, 'ikut': 129, 'geologis': 130, 'puluhan': 131, 'dinihari': 132, 'bagian': 133, 'tercatat': 134, 'menayangkan': 135, 'kondisi': 136, 'unsur': 137, 'kesenangan': 138, 'tanggal': 139, 'siang': 140, 'kurang': 141, 'ruang': 142, 'jam': 143, 'laku': 144, 'memahami': 145, 'diperoleh': 146, 'kebiasaan': 147, 'liar': 148, 'bung': 149, 'diamati': 150, 'dasar': 151, 'meninggal': 152, 'maraknya': 153, 'dimilikinya': 154, 'sebuah': 155, 'bermain': 156, '(': 157, 'berkembang': 158, 'selama': 159, 'bertemu': 160, 'pelajaran': 161, 'intelegensi': 162, 'oktober': 163, 'ketenagakerjaan': 164, 'budaya': 165, 'modal': 166, 'keuangan': 167, 'sdm': 168, 'ujung': 169, 'angin': 170, 'lahir': 171, 'kualitas': 172, 'malaka': 173, 'mobil': 174, 'kekayaan': 175, 'daratan': 176, 'urutan': 177, 'dilatarbelakangi': 178, 'listrik': 179, 'kehidupan': 180, 'ke': 181, 'lepas': 182, 'asing': 183, 'berhasil': 184, 'bandara': 185, 'afrika': 186, 'renewable': 187, 'dijadwalkan': 188, 'pelabuhan': 189, 'berupa': 190, 'kota': 191, '09.00': 192, 'portugis': 193, 'hadir': 194, 'singgah': 195, 'dalam': 196, '10': 197, 'naluri': 198, 'selain': 199, 'kedua': 200, 'kelestariannya': 201, 'km2': 202, '1945': 203, 'eropa': 204, 'belanda': 205, 'inc': 206, 'bersyukur': 207, 'brunei': 208, 'mundur': 209, 'kereta': 210, 'kerja': 211, 'jika': 212, 'bus': 213, 'berolahraga': 214, 'terakhir': 215, 'libur': 216, 'dua': 217, 'hindia': 218, 'bangsa-bangsa': 219, 'pertandingan': 220, '23.15': 221, 'belajar': 222, 'memperhatikan': 223, 'sejarah': 224, 'tujuh': 225, 'pariwisata': 226, 'air': 227, 'pemadaman': 228, 'selesai': 229, 'tayang': 230, 'negara': 231, 'angkatan': 232, 'pemilu': 233, 'petir': 234, 'psbb': 235, 'membentuk': 236, 'gama': 237, 'pelajari': 238, 'matahari': 239, 'samudera': 240, '-': 241, 'varietas': 242, 'secara': 243, 'hutan': 244, '18.00': 245, 'saya': 246, 'kafe': 247, 'pusat': 248, 'tengah': 249, 'mengelola': 250, 'teks': 251, 'stadion': 252, 'januari': 253, 'raga': 254, 'landas': 255, 'cara': 256, 'mulai': 257, 'diperbarui': 258, 'asia-afrika': 259, 'mendarat': 260, 'inilah': 261, 'darussalam': 262, 'patut': 263, '2': 264, 'goa': 265, 'diperingati': 266, 'tidak': 267, 'akan': 268, 'hidup': 269, 'tata': 270, 'badan': 271, 'bandang': 272, 'saat': 273, 'barat': 274, 'hawaii': 275, 'dimiliki': 276, 'dilaksanakan': 277, 'selatan': 278, 'rumah': 279, 'jenis': 280, '2024': 281, 'mata': 282, 'baik': 283, 'mei': 284, 'film': 285, 'berakhir': 286, 'kombinasi': 287, 'tanaman': 288, 'sarapan': 289, 'fisik': 290, 'bugis': 291, 'praktik': 292, 'jiwa': 293, 'biasanya': 294, 'kelas': 295, 'ibu': 296, 'penyinaran': 297, 'juga': 298, 'memenuhi': 299, 'melanjutkan': 300, 'sudah': 301, 'merupakan': 302, 'kebudayaan': 303, 'wawancara': 304, 'asean': 305, 'daerah': 306, 'big': 307, 'nanti': 308, 'abad': 309, 'norma': 310, 'satu': 311, 'prakiraan': 312, 'presiden': 313, 'laporan': 314, 'udara': 315, 'tsunami': 316, 'besar': 317, '1.910.932,37': 318, 'bumi': 319, 'acara': 320, '.': 321, 'kita': 322, 'dilantik': 323, '1511': 324, 'bagi': 325, 'bangun': 326, 'pernah': 327, '14': 328, '6': 329, 'dibuka': 330, '7.30': 331, 'harapan': 332, 'berlangsung': 333, 'jadwal': 334, 'kelima': 335, 'layanan': 336, 'yang': 337, 'masyarakat': 338, 'sawah': 339, 'ditetapkan': 340, 'penjelajahan': 341, 'tersebut': 342, 'kedalaman': 343, 'non': 344, 'dibahas': 345, 'terhadap': 346, '9': 347, 'hujan': 348, 'meliputi': 349, 'yaitu': 350, 'dan': 351, 'suhu': 352, 'musim': 353, 'harinya': 354, '5,8': 355, '00.30': 356, 'dapat': 357, 'pinisinya': 358, 'salah': 359, 'misalnya': 360, 'bawah': 361, 'oleh': 362, 'statistik': 363, 'adat': 364, 'block': 365, 'peristiwa': 366, 'berpakaian': 367, 'hingga': 368, 'reformasi': 369, 'sebagai': 370, 'disahkan': 371, 'perbedaan': 372, 'seseorang': 373, 'sebesar': 374, 'berdasarkan': 375, 'emas': 376, 'perkotaan': 377, 'dunia': 378, 'dengan': 379, 'mengikis': 380, 'merancang': 381, 'tingkah': 382, 'maritim': 383, 'berbelok': 384, 'keadaan': 385, '12': 386, 'kemaritiman': 387, 'aceh': 388, 'awal': 389, 'periode': 390, 'tenggara': 391, 'digelar': 392, 'keagamaan': 393, 'kalikut': 394, 'bangsa': 395, 'sampai': 396, 'unsur-unsur': 397, '13.00': 398, '18': 399, 'da': 400, '6.30': 401, 'batu': 402, 'masih': 403, 'apakah': 404, 'karena': 405, 'contoh': 406, 'memungkinkan': 407, 'daya': 408, 'lebih': 409, 'menggunakan': 410, ')': 411, 'lapangan': 412, 'individu': 413, 'mengakui': 414, 'delapan': 415, 'atau': 416, 'atas': 417, 'membantu': 418, 'berada': 419, '4': 420, 'armada': 421, '22': 422, 'adalah': 423, 'dirinci': 424, 'sumber': 425, 'dari': 426, 'cenderung': 427, '21.00': 428, ',': 429, '17.00': 430, 'kawasan': 431, 'semangat': 432, 'proses': 433, 'menelusuri': 434, 'berita': 435, 'sholat': 436, 'tinggal': 437, 'serangkaian': 438, 'jatuh': 439, 'tambang': 440, 'laut': 441, 'moor': 442, 'berinteraksi': 443, 'rancangan': 444, 'tanjung': 445, '8': 446, 'telah': 447, '06.00': 448, 'memulai': 449, 'freeport': 450, 'arang': 451, 'manfaatnya': 452, 'berbunyi': 453, 'obama': 454, 'proklamasi': 455, 'tahunan': 456, 'samudra': 457, 'makan': 458, 'setiap': 459, 'penyebab': 460, 'lawu': 461, 'karakter': 462, 'sebelum': 463, 'april': 464, 'kami': 465, 'konstantinopel': 466, 'pribadi': 467, 'tiga': 468, 'iklim': 469, 'global': 470, 'dihasilkan': 471, 'bahwa': 472, 'letak': 473, 'mencakup': 474, 'desember': 475, 'hal': 476, 'beberapa': 477, 'november': 478, 'mencapai': 479, 'mencatat': 480, '08.00': 481, 'upacara': 482, 'tuan': 483, 'peringkat': 484, 'mengurangi': 485, 'mengumumkan': 486, 'karakteristik': 487, 'begitu': 488, '270': 489, 'dimanfaatkan': 490, 'peserta': 491, 'singkat': 492, 'penjelasan': 493, '2023': 494, '21': 495, '1955': 496, 'sejak': 497, 'grasberg': 498, 'menguasai': 499, 'orang': 500, 'pemandangan': 501, 'kemudian': 502, 'ii': 503, 'malam': 504, 'kesatuan': 505, 'lalu': 506, 'nilai-nilai': 507, 'perdagangan': 508, 'pelaut': 509, 'jumlah': 510, 'berlayar': 511, 'dianggap': 512, 'tahun': 513, 'cadangan': 514, 'kitab': 515, 'melalui': 516, 'soeharto': 517, 'menguntungkan': 518, 'disewanya': 519, '26': 520, 'serangan': 521, 'pencaharian': 522, 'singapura': 523, '2017': 524, 'pagi': 525, 'seperti': 526, 'lingkungan': 527, 'itu': 528, 'nafsu': 529, 'tepat': 530, '22.00': 531, 'benua': 532, 'membincangkan': 533, 'terutama': 534, 'berhubungan': 535, 'penerbangan': 536, 'timur': 537, 'dibedakan': 538, 'sekitar': 539, 'open': 540, 'lisabon': 541, 'kalah': 542, 'tertentu': 543, '1970': 544, '1488': 545, 'syarat': 546, '28': 547, 'tanahnya': 548, 'juta': 549, 'hanya': 550, 'potensi': 551, 'sehari-hari': 552, 'minyak': 553, 'menit': 554, 'bajak': 555, 'pola': 556, 'pelayaran': 557, 'sehingga': 558, 'segala': 559, 'mengajar': 560, 'berbagi': 561, 'bertahan': 562, 'berbagai': 563, 'berikut': 564, 'sembilan': 565, 'suku': 566, 'dmlz': 567, 'yuk': 568, 'pulang': 569, 'untuk': 570, 'istiadat': 571, '20.00': 572, 'berharga': 573, 'terdapat': 574, 'pengaruh': 575, 'terbit': 576, 'karno': 577, 'menghasilkan': 578, 'keragaman': 579, 'cave': 580, 'ribuan': 581, 'menemukan': 582, 'bulan': 583, 'apabila': 584, 'memengaruhi': 585, 'mencerminkan': 586, 'pintu': 587, 'dirayakan': 588, 'pengetahuan': 589, 'juli': 590, 'lain': 591, 'ragam': 592, 'undang-undang': 593, 'jakarta': 594, '19.30': 595, 'tempat': 596, 'termasuk': 597, 'kemajuan': 598, 'asia': 599, 'mengambil': 600, '1453': 601, 'masehi': 602, 'pukul': 603, 'dilakukan': 604, 'sosial': 605, 'kemerdekaan': 606, 'pahlawan': 607, 'vasco': 608, 'penting': 609, 'kuat': 610, 'ia': 611, '2020': 612, 'simak': 613, 'beragam': 614, 'peranan': 615, 'cuaca': 616, 'maret': 617, '1498': 618, 'menempati': 619, 'sih': 620, 'memiliki': 621, 'konferensi': 622, 'hubungannya': 623, '17': 624, 'gambir': 625, 'yakni': 626, 'doz': 627, 'sedangkan': 628, 'mcmorran': 629, 'berlimpah': 630, 'juni': 631, 'barack': 632, '1961': 633, 'apa': 634, 'merdeka': 635, 'habibie': 636, 'hukum': 637, 'aktivitas': 638, 'keyakinan': 639, 'terbenam': 640, 'pada': 641, 'malaysia': 642, 'kucing': 643, 'mengubah': 644, 'empat': 645, 'manusianya': 646, 'menguap': 647, 'tembaga': 648, 'selalu': 649, 'perlu': 650, 'berangkat': 651, 'fotosintesis': 652, 'kepribadian-kepribadian': 653, 'dilayari': 654, 'agustus': 655, 'kelompok': 656, '5': 657, '7': 658, 'senjata': 659, 'antara': 660, 'orang-orang': 661, 'siaran': 662, 'dibandingkan': 663, 'pertemuan': 664, 'september': 665, 'india': 666, 'tinggalmu': 667, 'di': 668, 'sedikit': 669, 'thailand': 670, '1': 671, 'pemerintah': 672, 'pesawat': 673, 'pembelajaran': 674, 'membedakan': 675, 'gossan': 676, 'televisi': 677, 'stasiun': 678, 'kegiatan': 679, 'banyak': 680, 'kadar': 681, 'sering': 682, 'rute': 683, 'dimulai': 684, 'bandung': 685, 'diadakan': 686, 'pelanggan': 687, 'kalian': 688, 'diperkirakan': 689, 'pokok': 690, 'seperempat': 691, 'dia': 692, 'hindu-buddha': 693, 'bermata': 694, 'lautan': 695, 'banjir': 696, 'indonesia': 697, 'kaya': 698, 'hari': 699, 'resource': 700, 'lagi': 701, 'papua': 702, 'petunjuk': 703, 'relatif': 704, 'silahkan': 705, 'PAD': 0, 'UNK': 1}\n",
+ "['B-DATE', 'B-ETH', 'B-EVENT', 'B-LOC', 'B-MIN', 'B-MISC', 'B-ORG', 'B-PER', 'B-QUANT', 'B-REL', 'B-RES', 'B-TERM', 'B-TIME', 'I-DATE', 'I-ETH', 'I-EVENT', 'I-LOC', 'I-MISC', 'I-ORG', 'I-PER', 'I-QUANT', 'I-RES', 'I-TERM', 'I-TIME', 'O']\n",
+ "['AM-ADV', 'AM-CAU', 'AM-COM', 'AM-DIR', 'AM-DIS', 'AM-EXT', 'AM-FRQ', 'AM-LOC', 'AM-MNR', 'AM-MOD', 'AM-NEG', 'AM-PNC', 'AM-PRP', 'AM-QUE', 'AM-TMP', 'ARG0', 'ARG1', 'ARG2', 'ARG3', 'ARGM-BNF', 'ARGM-CAU', 'ARGM-COM', 'ARGM-DIS', 'ARGM-EX', 'ARGM-EXT', 'ARGM-LOC', 'ARGM-MNR', 'ARGM-MOD', 'ARGM-NEG', 'ARGM-PNC', 'ARGM-PRD', 'ARGM-PRP', 'ARGM-SRC', 'ARGM-TMP', 'I-AM-LOC', 'O', 'R-ARG1', 'V']\n",
+ "{'B-DATE': 0, 'B-ETH': 1, 'B-EVENT': 2, 'B-LOC': 3, 'B-MIN': 4, 'B-MISC': 5, 'B-ORG': 6, 'B-PER': 7, 'B-QUANT': 8, 'B-REL': 9, 'B-RES': 10, 'B-TERM': 11, 'B-TIME': 12, 'I-DATE': 13, 'I-ETH': 14, 'I-EVENT': 15, 'I-LOC': 16, 'I-MISC': 17, 'I-ORG': 18, 'I-PER': 19, 'I-QUANT': 20, 'I-RES': 21, 'I-TERM': 22, 'I-TIME': 23, 'O': 24}\n",
+ "{'AM-ADV': 0, 'AM-CAU': 1, 'AM-COM': 2, 'AM-DIR': 3, 'AM-DIS': 4, 'AM-EXT': 5, 'AM-FRQ': 6, 'AM-LOC': 7, 'AM-MNR': 8, 'AM-MOD': 9, 'AM-NEG': 10, 'AM-PNC': 11, 'AM-PRP': 12, 'AM-QUE': 13, 'AM-TMP': 14, 'ARG0': 15, 'ARG1': 16, 'ARG2': 17, 'ARG3': 18, 'ARGM-BNF': 19, 'ARGM-CAU': 20, 'ARGM-COM': 21, 'ARGM-DIS': 22, 'ARGM-EX': 23, 'ARGM-EXT': 24, 'ARGM-LOC': 25, 'ARGM-MNR': 26, 'ARGM-MOD': 27, 'ARGM-NEG': 28, 'ARGM-PNC': 29, 'ARGM-PRD': 30, 'ARGM-PRP': 31, 'ARGM-SRC': 32, 'ARGM-TMP': 33, 'I-AM-LOC': 34, 'O': 35, 'R-ARG1': 36, 'V': 37}\n",
+ "{0: 'B-DATE', 1: 'B-ETH', 2: 'B-EVENT', 3: 'B-LOC', 4: 'B-MIN', 5: 'B-MISC', 6: 'B-ORG', 7: 'B-PER', 8: 'B-QUANT', 9: 'B-REL', 10: 'B-RES', 11: 'B-TERM', 12: 'B-TIME', 13: 'I-DATE', 14: 'I-ETH', 15: 'I-EVENT', 16: 'I-LOC', 17: 'I-MISC', 18: 'I-ORG', 19: 'I-PER', 20: 'I-QUANT', 21: 'I-RES', 22: 'I-TERM', 23: 'I-TIME', 24: 'O'}\n",
+ "{0: 'AM-ADV', 1: 'AM-CAU', 2: 'AM-COM', 3: 'AM-DIR', 4: 'AM-DIS', 5: 'AM-EXT', 6: 'AM-FRQ', 7: 'AM-LOC', 8: 'AM-MNR', 9: 'AM-MOD', 10: 'AM-NEG', 11: 'AM-PNC', 12: 'AM-PRP', 13: 'AM-QUE', 14: 'AM-TMP', 15: 'ARG0', 16: 'ARG1', 17: 'ARG2', 18: 'ARG3', 19: 'ARGM-BNF', 20: 'ARGM-CAU', 21: 'ARGM-COM', 22: 'ARGM-DIS', 23: 'ARGM-EX', 24: 'ARGM-EXT', 25: 'ARGM-LOC', 26: 'ARGM-MNR', 27: 'ARGM-MOD', 28: 'ARGM-NEG', 29: 'ARGM-PNC', 30: 'ARGM-PRD', 31: 'ARGM-PRP', 32: 'ARGM-SRC', 33: 'ARGM-TMP', 34: 'I-AM-LOC', 35: 'O', 36: 'R-ARG1', 37: 'V'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "words = list(set(word for sentence in preprocessing_sentences for word in sentence))\n",
+ "word2idx = {word: idx + 2 for idx, word in enumerate(words)}\n",
+ "word2idx[\"PAD\"] = 0\n",
+ "word2idx[\"UNK\"] = 1\n",
+ "\n",
+ "all_ner_tags = sorted(set(tag for seq in ner_labels for tag in seq))\n",
+ "all_srl_tags = sorted(set(tag for seq in srl_labels for tag in seq))\n",
+ "tag2idx_ner = {tag: idx for idx, tag in enumerate(all_ner_tags)}\n",
+ "tag2idx_srl = {tag: idx for idx, tag in enumerate(all_srl_tags)}\n",
+ "idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
+ "idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}\n",
+ "\n",
+ "print(words)\n",
+ "print(word2idx)\n",
+ "print(all_ner_tags)\n",
+ "print(all_srl_tags)\n",
+ "print(tag2idx_ner)\n",
+ "print(tag2idx_srl)\n",
+ "print(idx2tag_ner)\n",
+ "print(idx2tag_srl)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9d3a37b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 42 551 425 ... 0 0 0]\n",
+ " [ 96 433 66 ... 0 0 0]\n",
+ " [ 96 575 433 ... 0 0 0]\n",
+ " ...\n",
+ " [641 496 429 ... 0 0 0]\n",
+ " [672 486 10 ... 0 0 0]\n",
+ " [593 151 203 ... 0 0 0]]\n",
+ "y_ner \n",
+ " \n",
+ "[[24 24 24 ... 24 24 24]\n",
+ " [24 24 24 ... 24 24 24]\n",
+ " [24 24 24 ... 24 24 24]\n",
+ " ...\n",
+ " [24 0 24 ... 24 24 24]\n",
+ " [24 24 24 ... 24 24 24]\n",
+ " [24 24 0 ... 24 24 24]]\n",
+ "y_srl \n",
+ " \n",
+ "[[16 16 16 ... 35 35 35]\n",
+ " [13 16 16 ... 35 35 35]\n",
+ " [13 16 16 ... 35 35 35]\n",
+ " ...\n",
+ " [14 14 35 ... 35 35 35]\n",
+ " [15 37 16 ... 35 35 35]\n",
+ " [16 16 14 ... 35 35 35]]\n",
+ "y_ner cat \n",
+ " \n",
+ "[array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 1., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 1., 0.],\n",
+ " [0., 0., 0., ..., 0., 1., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 1., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 1., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 1., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.]])]\n",
+ "y_srl cat \n",
+ " \n",
+ "[array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " [0., 1., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 1.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.],\n",
+ " [0., 0., 0., ..., 1., 0., 0.]])]\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# === ENCODING ===\n",
+ "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
+ "y_ner = [[tag2idx_ner[t] for t in ts] for ts in ner_labels]\n",
+ "y_srl = [[tag2idx_srl[t] for t in ts] for ts in srl_labels]\n",
+ "\n",
+ "maxlen = 50\n",
+ "\n",
+ "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
+ "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
+ "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
+ "\n",
+ "y_ner_cat = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
+ "y_srl_cat = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
+ "\n",
+ "print(X)\n",
+ "print(\"y_ner \\n \")\n",
+ "print(y_ner)\n",
+ "print(\"y_srl \\n \")\n",
+ "print(y_srl)\n",
+ "print(\"y_ner cat \\n \")\n",
+ "print(y_ner_cat)\n",
+ "print(\"y_srl cat \\n \")\n",
+ "print(y_srl_cat)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a5c264df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# split dataset \n",
+ "X_temp, X_test, y_ner_temp, y_ner_test, y_srl_temp, y_srl_test = train_test_split(\n",
+ " X, y_ner_cat, y_srl_cat, test_size=0.1, random_state=42\n",
+ ")\n",
+ "X_train, X_val, y_ner_train, y_ner_val, y_srl_train, y_srl_val = train_test_split(\n",
+ " X_temp, y_ner_temp, y_srl_temp, test_size=0.1111, random_state=42 # ~10% of total\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "712c1789",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-08 14:34:12.231050: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ input_layer │ (None, 50) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding │ (None, 50, 64) │ 45,184 │ input_layer[0][0] │\n",
+ "│ (Embedding) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ bidirectional │ (None, 50, 128) │ 66,048 │ embedding[0][0] │\n",
+ "│ (Bidirectional) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ner_output │ (None, 50, 25) │ 3,225 │ bidirectional[0]… │\n",
+ "│ (TimeDistributed) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ srl_output │ (None, 50, 38) │ 4,902 │ bidirectional[0]… │\n",
+ "│ (TimeDistributed) │ │ │ │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
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+ ],
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+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
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+ "│ input_layer │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m45,184\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ bidirectional │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ner_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m3,225\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ srl_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m38\u001b[0m) │ \u001b[38;5;34m4,902\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 119,359 (466.25 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m119,359\u001b[0m (466.25 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 119,359 (466.25 KB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m119,359\u001b[0m (466.25 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 0 (0.00 B)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#training model\n",
+ "input_layer = Input(shape=(maxlen,))\n",
+ "embedding = Embedding(input_dim=len(word2idx), output_dim=64)(input_layer)\n",
+ "bilstm = Bidirectional(LSTM(units=64, return_sequences=True))(embedding)\n",
+ "out_ner = TimeDistributed(Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\")(bilstm)\n",
+ "out_srl = TimeDistributed(Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\")(bilstm)\n",
+ "\n",
+ "model = Model(inputs=input_layer, outputs=[out_ner, out_srl])\n",
+ "model.compile(\n",
+ " optimizer=\"adam\",\n",
+ " loss={\"ner_output\": \"categorical_crossentropy\", \"srl_output\": \"categorical_crossentropy\"},\n",
+ " metrics={\"ner_output\": \"accuracy\", \"srl_output\": \"accuracy\"}\n",
+ ")\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "98feee87",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - loss: 3.6158 - ner_output_accuracy: 0.9415 - ner_output_loss: 1.4945 - srl_output_accuracy: 0.7447 - srl_output_loss: 2.1213 - val_loss: 0.7665 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2785 - val_srl_output_accuracy: 0.8550 - val_srl_output_loss: 0.4881\n",
+ "Epoch 2/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.8915 - ner_output_accuracy: 0.9478 - ner_output_loss: 0.2724 - srl_output_accuracy: 0.8253 - srl_output_loss: 0.6190 - val_loss: 0.6997 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2667 - val_srl_output_accuracy: 0.8538 - val_srl_output_loss: 0.4330\n",
+ "Epoch 3/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.7365 - ner_output_accuracy: 0.9564 - ner_output_loss: 0.2132 - srl_output_accuracy: 0.8416 - srl_output_loss: 0.5233 - val_loss: 0.6682 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2577 - val_srl_output_accuracy: 0.8575 - val_srl_output_loss: 0.4105\n",
+ "Epoch 4/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.7311 - ner_output_accuracy: 0.9505 - ner_output_loss: 0.2344 - srl_output_accuracy: 0.8466 - srl_output_loss: 0.4967 - val_loss: 0.6193 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2365 - val_srl_output_accuracy: 0.8813 - val_srl_output_loss: 0.3828\n",
+ "Epoch 5/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.7166 - ner_output_accuracy: 0.9486 - ner_output_loss: 0.2280 - srl_output_accuracy: 0.8665 - srl_output_loss: 0.4886 - val_loss: 0.5963 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2299 - val_srl_output_accuracy: 0.8875 - val_srl_output_loss: 0.3664\n",
+ "Epoch 6/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.6772 - ner_output_accuracy: 0.9565 - ner_output_loss: 0.1832 - srl_output_accuracy: 0.8551 - srl_output_loss: 0.4940 - val_loss: 0.5593 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2167 - val_srl_output_accuracy: 0.8950 - val_srl_output_loss: 0.3426\n",
+ "Epoch 7/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.6840 - ner_output_accuracy: 0.9439 - ner_output_loss: 0.2195 - srl_output_accuracy: 0.8772 - srl_output_loss: 0.4646 - val_loss: 0.5333 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2071 - val_srl_output_accuracy: 0.8975 - val_srl_output_loss: 0.3262\n",
+ "Epoch 8/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.5664 - ner_output_accuracy: 0.9525 - ner_output_loss: 0.1749 - srl_output_accuracy: 0.8891 - srl_output_loss: 0.3915 - val_loss: 0.5044 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.1980 - val_srl_output_accuracy: 0.9162 - val_srl_output_loss: 0.3064\n",
+ "Epoch 9/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.5864 - ner_output_accuracy: 0.9497 - ner_output_loss: 0.1924 - srl_output_accuracy: 0.8918 - srl_output_loss: 0.3941 - val_loss: 0.4887 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.1913 - val_srl_output_accuracy: 0.9200 - val_srl_output_loss: 0.2974\n",
+ "Epoch 10/10\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.5106 - ner_output_accuracy: 0.9608 - ner_output_loss: 0.1370 - srl_output_accuracy: 0.8946 - srl_output_loss: 0.3736 - val_loss: 0.4705 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.1820 - val_srl_output_accuracy: 0.9187 - val_srl_output_loss: 0.2885\n"
+ ]
+ },
+ {
+ "data": {
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VUgAAAAAAAChzJKUAAAAAAABQ5hxsHUB5ZDKZdOrUKXl6esowDFuHAwAAAAAAUGGYzWYlJSUpODhYdnYFr4ciKXUFp06dUkhIiK3DAAAAAAAAqLCio6NVs2bNAj8nKXUFnp6eUu7N8/LysnU4xZaVlaU1a9aoR48ecnR0tHU4QJXDMwjYDs8fYDs8f4Dt8PyhvEhMTFRISIglv1IQklJXkFey5+XlVeGTUm5ubvLy8uIvJMAGeAYB2+H5A2yH5w+wHZ4/lDdXa4lEo3MAAAAAAACUOZJSAAAAAAAAKHMkpQAAAAAAAFDm6CkFAAAAAEAFkpOTo6ysrHzjWVlZcnBwUHp6unJycmwSG6oGR0dH2dvbX/dxSEoBAAAAAFABmM1mxcbGKj4+vsDPAwMDFR0dfdUG08D18vHxUWBg4HX91khKAQAAAABQAeQlpAICAuTm5pYvGWAymZScnCwPDw/Z2dGtB6XDbDYrNTVVZ86ckSQFBQUV+1gkpQAAAAAAKOdycnIsCSk/P78rzjGZTMrMzJSLiwtJKZQqV1dXSdKZM2cUEBBQ7FI+fqUAAAAAAJRzeT2k3NzcbB0KIF3yW7xSf7OiIikFAAAAAEAFQa8olBcl8VskKQUAAAAAAIAyR1IKAAAAAABUGoZhaNmyZbYOo8g+++wz9ejRo9SO36VLFz355JNFnn/u3DkFBAToxIkTpRZTHpJSAAAAAACgxBmGUeg2efLkAr979OhRGYahqKioEo9rxIgR6tu3b4kftzjS09P14osv6qWXXrKMlXR83377rV555ZUiz69WrZruu+8+q5hKC2/fAwAAAAAAJS4mJsbyzwsXLtSkSZN04MABy5iHh4eNIis/lixZIi8vL3Xs2PGav5uVlSVHR8erzvP19b3mYz/wwANq3bq13n777WJ9v6hYKQUAAAAAAEpcYGCgZfP29pZhGJb9gIAAvffee6pZs6acnZ3VsmVLrVq1yvLd0NBQSVJ4eLgMw1CXLl0kSZGRkerevbuqVasmb29v3Xrrrfrtt99KNO5ffvlF7dq1k7Ozs4KCgvTcc88pOzvb8vmSJUvUvHlzubq6ys/PT926dVNKSookacOGDWrXrp3c3d3l4+Ojjh076tixYwWea8GCBerdu7dlf/Lkyfriiy/0/fffW1aUbdiwwbJybOHChbr11lvl4uKiefPmKS4uTkOHDlWNGjXk5uam5s2b65tvvrE6x+Xle3Xq1NHrr7+uBx98UJ6enqpVq5Y+/vhjq+80bdpUwcHB+u6770rknhaEpBQAAAAAABWQ2WxWama21ZaWmZNvrKQ3s9l83bFPnz5d7777rt555x3t2bNHERER6tOnjw4ePChJ2rFjhyRp3bp1iomJ0bfffitJSkpK0v3336/Nmzfr119/Vf369dWrVy8lJSVdd0ySdPLkSfXq1Utt27bV77//rlmzZumzzz7Tq6++KuWu/ho6dKgefPBB7du3Txs2bFD//v1lNpuVnZ2tvn376tZbb9WePXu0bds2jR49utC31G3evFlt2rSx7D/99NMaNGiQ7rjjDsXExCgmJkY333yz5fPnnntOTzzxhPbt26eIiAilp6erdevW+vHHH7V3716NHj1a9957r+X+FeTdd99VmzZttHv3bj366KN65JFHrFaxSVK7du20adOm67ibV0f5HgAAAAAAFVBaVo6aTFpd5uf96+UIuTldXzrhnXfe0fjx4zVkyBBJ0tSpU7V+/XpNmzZNH374ofz9/SVJfn5+CgwMtHzvtttuszrOxx9/LB8fH/3yyy+66667rismSfroo48UEhKimTNnyjAMNWrUSKdOndL48eM1adIkxcTEKDs7W/3791ft2rUlSc2bN5cknT9/XgkJCbrrrrtUt25dSVLjxo0LPFd8fLwSEhIUHBxsGfPw8JCrq6syMjKsrjvPk08+qf79+1uNPf3005Z/fvzxx7V69WotWrRI7dq1K/DcvXr10qOPPipJGj9+vN5//32tX79eDRs2tMwJDg7W7t27i3TfiouVUgAAAAAAoMwkJibq1KlT+foodezYUfv27Sv0u6dPn9aoUaNUv359eXt7y8vLS8nJyTp+/HiJxLZv3z516NDBanVTx44dlZycrBMnTigsLEy33367mjdvroEDB+qTTz7RhQsXpNzeTSNGjFBERIR69+6t6dOnW/XVulxaWpokycXFpcjxXbqqSpJycnL0yiuvqHnz5vL19ZWHh4dWr1591fvRokULyz/nlVWeOXPGao6rq6tSU1OLHFtxsFIKAAAAAIAKyNXRXn+9HGHZN5lMSkpMkqeXp+zsSm8Niqujfakd+2ruv/9+xcXFafr06apdu7acnZ3VoUMHZWZmlsn57e3ttXbtWm3dulVr1qzRBx98oBdeeEHbt29XaGio5syZo7Fjx2rVqlVauHChJk6cqLVr1+qmm27Kdyw/Pz8ZhmFJahWFu7u71f7bb7+t6dOna9q0aWrevLnc3d315JNPXvV+XN4g3TAMmUwmq7Hz589bVqyVFlZKVXIms2QyXX+9LwAAAACgfDEMQ25ODlabq5N9vrGS3grrkVQUXl5eCg4O1pYtW6zGt2zZoiZNmkiSnJycpNyVQJfPGTt2rHr16qWmTZvK2dlZ586du654LtW4cWNt27bNqm/Wli1b5OnpqZo1a0q5971jx46aMmWKdu/eLScnJ6uG4OHh4ZowYYK2bt2qZs2aaf78+Vc8l5OTk5o0aaK//vor3/jl112QLVu26O6779a//vUvhYWF6cYbb9Tff/9dzKu3tnfvXoWHh5fIsQpCUqoSm7f9uF7dba/1f5+1dSgAAAAAAFg888wzmjp1qhYuXKgDBw7oueeeU1RUlJ544glJUkBAgFxdXbVq1SqdPn1aCQkJkqT69evrq6++0r59+7R9+3YNHz5crq6u13z+hIQERUVFWW3R0dF69NFHFR0drccff1z79+/X999/r5deeknjxo2TnZ2dtm/frtdff107d+7U8ePH9e233+rs2bNq3Lixjhw5ogkTJmjbtm06duyY1qxZo4MHDxbaVyoiIkKbN2+2GqtTp4727NmjAwcO6Ny5c8rKyirw+/Xr17es3Nq3b58efvhhnT59+prvx+VSU1O1a9cu9ejR47qPVRjK9yqx6AtpisswtHjXSd3RvIatwwEAAAAAQJI0duxYJSQk6KmnntKZM2fUpEkTLV++XPXr15ckOTg4aMaMGXr55Zc1adIkderUSRs2bNBnn32m0aNHq1WrVgoJCdHrr79u1ei7qDZs2JBvFdDIkSP16aefasWKFXrmmWcUFhYmX19fjRw5UhMnTpRyV3lt3LhR06ZNU2JiomrXrq13331XPXv21OnTp7V//3598cUXiouLU1BQkB577DE9/PDDBcYxcuRItWnTRgkJCfL29pYkjRo1Shs2bFCbNm2UnJys9evXq06dOlf8/sSJE/XPP/8oIiJCbm5uGj16tPr27WtJ4hXX999/r1q1aqlTp07XdZyrMcwl8S7HYnrjjTf07bffav/+/XJ1ddXNN9+sqVOnWnV7v5LFixfrxRdf1NGjR1W/fn1NnTpVvXr1snxuNpv10ksv6ZNPPlF8fLw6duyoWbNmWX7cV5OYmChvb28lJCTIy8vruq/TVvadvKCeH2yVvZ2hbc/dpgCvojdPA3D9srKytGLFCvXq1StfzTaA0sXzB9gOzx9QOtLT03XkyBGFhoYW2BjbZDIpMTFRXl5epdpTCiVr4MCBatWqlSZMmGDrUCxuuukmjR07VsOGDStwTmG/yaLmVWz6K/3ll1/02GOP6ddff9XatWuVlZWlHj16KCUlpcDvbN26VUOHDtXIkSO1e/du9e3bV3379tXevXstc9566y3NmDFDs2fP1vbt2+Xu7q6IiAilp6eX0ZWVD/UCPBTqaVaOyawlv52wdTgAAAAAAOAyb7/9tjw8PGwdhsW5c+fUv39/DR06tNTPZdOk1KpVqzRixAg1bdpUYWFhmjt3ro4fP65du3YV+J3p06frjjvu0DPPPKPGjRvrlVdeUatWrTRz5kwpd5XUtGnTNHHiRN19991q0aKFvvzyS506dUrLli0rw6srH24KuNg9f2FktGy4KA4AAAAAAFxBnTp19Pjjj9s6DItq1arp2Wefve6G9kVRrnpK5dU8+vr6Fjhn27ZtGjdunNVYRESEJeF05MgRxcbGqlu3bpbPvb291b59e23btk1DhgzJd8yMjAxlZGRY9hMTE6XcpceFNRQr77KyshTuZ9byaHsdi0vVloNn1D604HsLoGTl/f1Rkf8eASoqnj/Adnj+gNKRlZUls9ksk8kkk8l0xTl5CxHy5gGlyWQyyWw2KysrS/b29lafFfXfAeUmKWUymfTkk0+qY8eOatasWYHzYmNjVb16daux6tWrKzY21vJ53lhBcy73xhtvaMqUKfnG16xZIzc3t2JdT3nhbC+F+WRp6xk7TVu+Q/fW5y8moKytXbvW1iEAVRbPH2A7PH9AyXJwcFBgYKCSk5OVmZlZ6NykpKQyiwtVV2ZmptLS0rRx40ZlZ2dbfZaamlqkY5SbpNRjjz2mvXv35nsVYlmYMGGC1eqrxMREhYSEqEePHhW60XlWVpbWrl2rsb3bautnu/RHvIM6dr1V3q40nATKQt4z2L17dxq9AmWM5w+wHZ4/oHSkp6crOjpaHh4eBTY6N5vNSkpKkqenZ5mUXqFqS09Pl6urqzp37nzFRudFUS6SUmPGjNEPP/ygjRs3qmbNmoXODQwM1OnTp63GTp8+rcDAQMvneWNBQUFWc1q2bHnFYzo7O8vZ2TnfuKOjY6X4F2mr2r5qFOip/bFJWvHnGd3X4cqvkgRQOirL3yVARcTzB9gOzx9QsnJycmQYhuzs7Ap8s15eyV7ePKA02dnZyTCMK/59X9S//236KzWbzRozZoy+++47/fzzzwoNDb3qdzp06KCffvrJamzt2rXq0KGDJCk0NFSBgYFWcxITE7V9+3bLnKrGMAwNbhsiSVqwI9rW4QAAAAAAANg2KfXYY4/p66+/1vz58+Xp6anY2FjFxsYqLS3NMue+++7ThAkTLPtPPPGEVq1apXfffVf79+/X5MmTtXPnTo0ZM0bKTcA8+eSTevXVV7V8+XL98ccfuu+++xQcHKy+ffva5DrLg74ta8jJ3k5/xSRq78kEW4cDAAAAAACqOJsmpWbNmqWEhAR16dJFQUFBlm3hwoWWOcePH1dMTIxl/+abb9b8+fP18ccfKywsTEuWLNGyZcusmqM/++yzevzxxzV69Gi1bdtWycnJWrVqVYF1t1XBDe5Oimh2sbRxQeRxW4cDAAAAAACqOJuX711pGzFihGXOhg0bNHfuXKvvDRw4UAcOHFBGRob27t2rXr16WX1uGIZefvllxcbGKj09XevWrVODBg3K7LrKqyG5JXzf7z6ltMwcW4cDAAAAAECJMwxDy5Yts3UYRfbZZ5+pR48eJXrMLl266Mknn7Ts33TTTVq6dGmJnqMk0PmsCulwo59CfF2VlJGtlXtjivANAAAAAACKxzCMQrfJkycX+N2jR4/KMAxFRUWVeFwjRowoN+190tPT9eKLL+qll14q1fNMnDhRzz33nKUZfnlBUqoKsbMzNKh1bsPzSBqeAwAAAABKT0xMjGWbNm2avLy8rMaefvppW4doc0uWLJGXl5c6duxYqufp2bOnkpKStHLlylI9z7UiKVXFDGhTU3aGtOPIef1zNtnW4QAAAAAAKqnAwEDL5u3tLcMwLPsBAQF67733VLNmTTk7O6tly5ZatWqV5buhoaGSpPDwcBmGoS5dukiSIiMj1b17d1WrVk3e3t669dZb9dtvv5Vo3L/88ovatWsnZ2dnBQUF6bnnnlN2drbl8yVLlqh58+ZydXWVn5+funXrppSUFCm3BVG7du3k7u4uHx8fdezYUceOHSvwXAsWLFDv3r0t+2vWrJGLi4vi4+Ot5j3xxBO67bbbJElxcXEaOnSoatSoITc3NzVv3lzffPNNoddkb2+vXr16acGCBcW+L6WBpFQVE+Ttqlsb+EuSFu08YetwAAAAAADFZTZLmSnWW1Zq/rGS3szm6w59+vTpevfdd/XOO+9oz549ioiIUJ8+fXTw4EFJ0o4dOyRJ69atU0xMjL799ltJUlJSku6//35t3rxZv/76q+rXr69evXopKSnpumOSpJMnT6pXr15q27atfv/9d82aNUufffaZXn31VSl39dfQoUP14IMPat++fdqwYYP69+8vs9ms7Oxs9e3bV7feeqv27Nmjbdu2afTo0TIMo8Dzbd68WW3atLHs33777fLx8bHq/5STk6OFCxdq+PDhUm7JX+vWrfXjjz9q7969Gj16tO69917LPStIu3bttGnTphK4SyXHwdYBoOwNbltL6w+c1ZJdJ/RUjwZytCc3CQAAAAAVTlaq9HqwZddOkk9ZnPf5U5KT+3Ud4p133tH48eM1ZMgQSdLUqVO1fv16TZs2TR9++KH8/S8upvDz81NgYKDle3mrhfJ8/PHH8vHx0S+//KK77rrrumKSpI8++kghISGaOXOmDMNQo0aNdOrUKY0fP16TJk1STEyMsrOz1b9/f9WuXVuS1Lx5c0nS+fPnlZCQoLvuukt169aVJDVu3LjAc8XHxyshIUHBwf/739De3l5DhgzR/PnzNXLkSEnSTz/9pPj4eN1zzz2SpBo1aliVPj7++ONavXq1Fi1apHbt2hV4vuDgYEVHR8tkMsnOrnzkAcpHFChTtzcOUDUPJ51LztDP+8/YOhwAAAAAQBWSmJioU6dO5euj1LFjR+3bt6/Q754+fVqjRo1S/fr15e3tLS8vLyUnJ+v48eMlEtu+ffvUoUMHq9VNHTt2VHJysk6cOKGwsDDdfvvtat68uQYOHKhPPvlEFy5ckCT5+vpqxIgRioiIUO/evTV9+nTFxBT8krG0tDRJkouLi9X48OHDtWHDBp06dUqSNG/ePN15553y8bmYcszJydErr7yi5s2by9fXVx4eHlq9evVV74Grq6tMJpMyMjKu4w6VLFZKVUGO9na6p1VN/d/Gf7QoMloRTQOL8C0AAAAAQLni6HZx1VIuk8mkxKQkeXl6lu5KGEe30jv2Vdx///2Ki4vT9OnTVbt2bTk7O6tDhw7KzMwsk/Pb29tr7dq12rp1q9asWaMPPvhAL7zwgrZv367Q0FDNmTNHY8eO1apVq7Rw4UJNnDhRa9eu1U033ZTvWH5+fjIMw5LUytO2bVvVrVtXCxYs0COPPKLvvvtOc+fOtXz+9ttva/r06Zo2bZqaN28ud3d3Pfnkk1e9B+fPn5e7u7tcXV1L8I5cH1ZKVVGD2l58C9/6A2cUm5Bu63AAAAAAANfKMC6W0V26ObrlHyvprZAeSUXh5eWl4OBgbdmyxWp8y5YtatKkiSTJyclJyl0VdPmcsWPHqlevXmratKmcnZ117ty564rnUo0bN9a2bdtkvqRv1pYtW+Tp6amaNWtKkgzDUMeOHTVlyhTt3r1bTk5O+u677yzzw8PDNWHCBG3dulXNmjXT/Pnzr3guJycnNWnSRH/99Ve+z4YPH6558+bpv//9r+zs7HTnnXdaxXP33XfrX//6l8LCwnTjjTfq77//vuq17d27V+Hh4dd8T0oTSakqqq6/h9rV8ZXJLC3ZFW3rcAAAAAAAVcgzzzyjqVOnauHChTpw4ICee+45RUVF6YknnpAkBQQEyNXVVatWrdLp06eVkJAgSapfv76++uor7du3T9u3b9fw4cOLtfInISFBUVFRVlt0dLQeffRRRUdH6/HHH9f+/fv1/fff66WXXtK4ceNkZ2en7du36/XXX9fOnTt1/Phxffvttzp79qwaN26sI0eOaMKECdq2bZuOHTumNWvW6ODBg4X2lYqIiNDmzZvzjQ8fPly//fabXnvtNQ0YMEDOzs6Wz+rXr29ZrbVv3z49/PDDOn369FWvedOmTerRo8c136vSRFKqCstbLbVo5wmZTNf/9gQAAAAAAIpi7NixGjdunJ566ik1b95cq1at0vLly1W/fn1JkoODg2bMmKH/+7//U3BwsO6++25J0meffaYLFy6oVatWuvfeezV27FgFBARc8/k3bNig8PBwq23KlCmqUaOGVqxYoR07digsLEz//ve/NXLkSE2cOFHKXeW1ceNG9erVSw0aNNDEiRP17rvvqmfPnnJzc9P+/ft1zz33qEGDBho9erQee+wxPfzwwwXGMXLkSK1YscKSdMtTr149tWvXTnv27LG8dS/PxIkT1apVK0VERKhLly4KDAxU3759C73ekydPauvWrXrggQeu+V6VJsNsLoF3OVYyiYmJ8vb2VkJCgry8vGwdTrFlZWVpxYoV6tWrlxwdHfN9npqZrfav/aSkjGzNf6i9bq5XzSZxApXV1Z5BAKWH5w+wHZ4/oHSkp6fryJEjCg0NzdcYO4/JZFJiYqK8vLzKzdvVcHUDBw5Uq1atNGHChFI7x/jx43XhwgV9/PHHJXbMwn6TRc2r8CutwtycHNSn5cVXTy6IpIQPAAAAAICy9vbbb8vDw6NUzxEQEKBXXnmlVM9RHCSlqrjBuSV8q/6MVXxq2bytAAAAAAAAXFSnTh09/vjjpXqOp556StWrVy/VcxQHSakqrnkNbzUO8lJmtknLdp+0dTgAAAAAAKCKIClVxRmGoSG5q6UWREaLFmMAAAAAAKAskJSC+rasIScHO+2PTdIfJxOK8A0AAAAAAIDrQ1IK8nZzVM9mgRINzwEAAAAAQBkhKQXpkobny6NOKTUz29bhAAAAAACASo6kFCRJN4X6qbafm5IzsrXij1hbhwMAAAAAACo5klKQJNnZGRrU5uJqqYWRx20dDgAAAAAAqORISsFiQOuasjOkyKMXdOhMsq3DAQAAAACg0vnpp5/UuHFj5eTklMrxR4wYob59+xZ5fmZmpurUqaOdO3eWSjyFISkFi+peLrqtUYAkafFOGp4DAAAAAK7PiBEjZBiG3nzzTavxZcuWyTAMy/6GDRtkGMYVt9jYiy1mJk+ebBmzt7dXSEiIRo8erfPnzxcaw+TJk9WyZctSusJr9+yzz2rixImyt7eXSiG+6dOna+7cuUWe7+TkpKefflrjx48vsRiKiqQUrOSV8C397YQys022DgcAAAAAUMG5uLho6tSpunDhwlXnHjhwQDExMVZbQECA5fOmTZsqJiZGx48f15w5c7Rq1So98sgjpXwFJWfz5s06fPiw7rnnnmv+blZWVpHmeXt7y8fH55qOPXz4cG3evFl//vnnNcd1PUhKwUrXRgHy93TWueRM/bz/tK3DAQAAAABUcN26dVNgYKDeeOONq84NCAhQYGCg1WZn97/UhYODgwIDA1WjRg1169ZNAwcO1Nq1a68rvj/++EO33XabXF1d5efnp9GjRys5+X8tbTZs2KB27drJ3d1dPj4+6tixo44dOyZJ+v3339W1a1d5enrKy8tLrVu3LrQMbsGCBerevbtcXFwkSXPnztWUKVP0+++/W1aB5a1yMgxDs2bNUp8+feTu7q7XXntNOTk5GjlypEJDQ+Xq6qqGDRtq+vTpVue4vHyvS5cuGjt2rJ599ln5+voqMDBQkydPtvrODTfcoI4dO2rBggXXdS+vlUOZng3lnqO9nQa0rqlZGw5rYWS07mgWZOuQAAAAAABXYDablZadZtk3mUxKy06TQ5aDVSKnpLk6uFqV3l2Nvb29Xn/9dQ0bNkxjx45VzZo1SySOo0ePavXq1XJycir2MVJSUhQREaEOHTooMjJSZ86c0UMPPaQxY8Zo7ty5ys7OVt++fTVq1Ch98803yszM1I4dOyzXP3z4cIWHh2vWrFmyt7dXVFSUHB0dCzzfpk2bNGzYMMv+4MGDtXfvXq1atUrr1q2Tclc65Zk8ebLefPNNTZs2TQ4ODjKZTKpZs6YWL14sPz8/bd26VaNHj1ZQUJAGDRpU4Hm/+OILjRs3Ttu3b9e2bds0YsQIdezYUd27d7fMadeunTZt2lTse1kcJKWQz6A2IZq14bB++fusTsWnKdjH1dYhAQAAAAAuk5adpvbz25f5ebcP2y43R7dr+k6/fv3UsmVLvfTSS/rss88KnHd5wqp27dpWJWV//PGHPDw8lJOTo/T0dEnSe++9d83XkGf+/PlKT0/Xl19+KXd3d0nSzJkz1bt3b02dOlWOjo5KSEjQXXfdpbp160qSGjdubPn+8ePH9cwzz6hRo0aSpPr16xd6vmPHjik4ONiy7+rqKg8PD8sKsMsNGzZMDzzwgNXYlClTLP8cGhqqbdu2adGiRYUmpVq0aKGXXnrJEuPMmTP1008/WSWlgoODLSvAygpJKeQTWs1d7UN9tf3IeS3ZdUJjby/8oQIAAAAA4GqmTp2q2267TU8//XSBczZt2iRPT0/L/uWrjho2bKjly5crPT1dX3/9taKiovT4448XO6Z9+/YpLCzMkpCSpI4dO8pkMunAgQPq3LmzRowYoYiICHXv3l3dunXToEGDFBR0sapo3Lhxeuihh/TVV19ZygnzkldXkpaWZindK4o2bdrkG/vwww/1+eef6/jx40pLS1NmZuZVG6W3aNHCaj8oKEhnzpyxGnN1dVVqamqRYysJJKVwRUPahWj7kfNatDNaY7rWk51d0ZdmAgAAAABKn6uDq7YP227ZN5lMSkpKkqenZ6mX7xVH586dFRERoQkTJmjEiBFXnBMaGlpok24nJyfVq1dPkvTmm2/qzjvv1JQpU/TKK68UK6aimDNnjsaOHatVq1Zp4cKFmjhxotauXaubbrpJkydP1rBhw/Tjjz9q5cqVeumll7RgwQL169fviseqVq1akRq+57k0WabcnlRPP/203n33XXXo0EGenp56++23tX379gKPoSsk9wzDkMlk/XKz8+fPy9/fv8ixlQSSUriins2CNOn7P3XiQpq2Ho7TLfWr2TokAAAAAMAlDMOwKqMzmUzKdsiWm6NbqSalrsebb76pli1bqmHDhiVyvIkTJ+q2227TI488YlUWV1SNGzfW3LlzlZKSYkkAbdmyRXZ2dlYxhoeHKzw8XBMmTFCHDh00f/583XTTTZKkBg0aqEGDBvrPf/6joUOHas6cOQUmpcLDw/XXX39ZjTk5OSknJ6dI8W7ZskU333yzHn30UcvY4cOHr/m6r2Tv3r0KDw8vkWMVVfn8lcLmXBzt1bdlDUnSgsjjtg4HAAAAAFAJNG/eXMOHD9eMGTOu+PmZM2cUGxtrtWVlZRV4vA4dOqhFixZ6/fXXCz1vWlqaoqKirLbDhw9r+PDhcnFx0f3336+9e/dq/fr1evzxx3XvvfeqevXqOnLkiCZMmKBt27bp2LFjWrNmjQ4ePKjGjRsrLS1NY8aM0YYNG3Ts2DFt2bJFkZGRVj2nLhcREaHNmzdbjdWpU0dHjhxRVFSUzp07p4yMjAK/X79+fe3cuVOrV6/W33//rRdffFGRkZGFXntRbdq0ST169CiRYxUVSSkUaHDbEEnSmj9P60JKpq3DAQAAAABUAi+//HK+0rE8DRs2VFBQkNW2a9euQo/3n//8R59++qmio6MLnPP3339bVjvlbQ8//LDc3Ny0evVqnT9/Xm3bttWAAQN0++23a+bMmZIkNzc37d+/X/fcc48aNGig0aNH67HHHtPDDz8se3t7xcXF6b777lODBg00aNAg9ezZ06oR+eWGDx+uP//8UwcOHLCM3XPPPbrjjjvUtWtX+fv765tvvinw+w8//LD69++vwYMHq3379oqLi7NaNVVc27ZtU0JCggYMGHDdx7oWhtlsNpfpGSuAxMREeXt7KyEhQV5eXrYOp9iysrK0YsUK9erVq9BXUhbmzhmb9OepRE26q4kevCW0xGMEKrOSeAYBFA/PH2A7PH9A6UhPT9eRI0cUGhpaYKNsk8mkxMREeXl5ldvyPUjPPPOMEhMT9X//93+2DsVi8ODBCgsL0/PPP1/k7xT2myxqXoVfKQo1JHe11MLIaJG/BAAAAADg+rzwwguqXbt2gavFylpmZqaaN2+u//znP2V+bpJSKFSfljXk7GCnA6eT9PuJBFuHAwAAAABAhebj46Pnn3++3Kxmc3Jy0sSJE+XqWry3Kl6P8nEHUG55uzqqV/MgSdJCGp4DAAAAAIASQlIKV5XX8Hx51CmlZGTbOhwAAAAAAFAJkJTCVbUP9VUdPzelZOboxz9ibB0OAAAAAACoBEhK4aoMw9CgSxqeAwAAAAAAXC+SUiiSAa1qyt7O0K5jF3ToTJKtwwEAAAAAABUcSSkUSYCXi25rFCCxWgoAAAAAAJQAklIossFtLpbwLf3tpDKzTbYOBwAAAAAAVGAkpVBkXRr6K8DTWedTMrVu32lbhwMAAAAAQIXz008/qXHjxsrJySmxY44YMUJ9+/a17A8ZMkTvvvtuiR2/tJCUQpE52NtpYJuaEiV8AAAAAIAiGDFihAzD0Jtvvmk1vmzZMhmGYdnfsGGDDMO44hYbGytJmjx5smXM3t5eISEhGj16tM6fP19oDJMnT1bLli1L6Qqv3bPPPquJEyfK3t6+1M4xceJEvfbaa0pISCi1c5QEklK4JoNyS/g2Hjyrk/Fptg4HAAAAAFDOubi4aOrUqbpw4cJV5x44cEAxMTFWW0BAgOXzpk2bKiYmRsePH9ecOXO0atUqPfLII6V8BSVn8+bNOnz4sO65555SPU+zZs1Ut25dff3116V6nutFUgrXpLafuzrc6CezWVq8k9VSAAAAAIDCdevWTYGBgXrjjTeuOjcgIECBgYFWm53d/1IXDg4OCgwMVI0aNdStWzcNHDhQa9euva74/vjjD912221ydXWVn5+fRo8ereTkZMvnGzZsULt27eTu7i4fHx917NhRx44dkyT9/vvv6tq1qzw9PeXl5aXWrVtr586dBZ5rwYIF6t69u1xcXCRJf//9twzD0P79+63mvf/++6pbt64kKScnRyNHjlRoaKhcXV3VsGFDTZ8+/arX1bt3by1YsKDY96UskJTCNRvS7uJqqcU7TyjHZLZ1OAAAAABQJZnNZplSU623tLT8YyW8mc3X9t+B9vb2ev311/XBBx/oxIkTJXb9R48e1erVq+Xk5FTsY6SkpCgiIkI33HCDIiMjtXjxYq1bt05jxoyRJGVnZ6tv37669dZbtWfPHm3btk2jR4+2lB4OHz5cNWvWVGRkpHbt2qXnnntOjo6OBZ5v06ZNatOmjWW/QYMGatOmjebNm2c1b968eRo2bJgkyWQyqWbNmlq8eLH++usvTZo0Sc8//7wWLVpU6LW1a9dOO3bsUEZGRrHvT2lzsHUAqHgimgbKy8VBJ+PTtOXQOXVu4G/rkAAAAACgyjGnpelAq9b5xkv7tVQNf9slw83tmr7Tr18/tWzZUi+99JI+++yzAufVrFnTar927dr6888/Lft//PGHPDw8lJOTo/T0dEnSe++9d83XkGf+/PlKT0/Xl19+KXd3d0nSzJkz1bt3b02dOlWOjo5KSEjQXXfdZVm51LhxY8v3jx8/rmeeeUaNGjWSJNWvX7/Q8x07dkzBwcFWY8OHD9fMmTP1yiuvSLmrp3bt2mUpvXN0dNSUKVMs80NDQ7Vt2zYtWrRIgwYNKvBcwcHByszMVGxsrGrXrl2Mu1P6WCmFa+biaK9+4TUkGp4DAAAAAIpo6tSp+uKLL7Rv374C52zatElRUVGWbcWKFVafN2zYUFFRUYqMjNT48eMVERGhxx9/vNgx7du3T2FhYZaElCR17NhRJpNJBw4ckK+vr0aMGKGIiAj17t1b06dPV0xMjGXuuHHj9NBDD6lbt2568803dfjw4ULPl5aWZindyzNkyBAdPXpUv/76q5S7SqpVq1aWRJckffjhh2rdurX8/f3l4eGhjz/+WMePHy/0XK6urpKk1NTUa7wrZYeVUiiWwW1r6Yttx7Tmr1idT8mUr3vxl0sCAAAAAK6d4eqqhr/tsuybTCYlJiXJy9PTqg9TaZy3ODp37qyIiAhNmDBBI0aMuOKc0NBQ+fj4FHgMJycn1atXT5L05ptv6s4779SUKVMsq4xKw5w5czR27FitWrVKCxcu1MSJE7V27VrddNNNmjx5soYNG6Yff/xRK1eu1EsvvaQFCxaoX79+VzxWtWrV8jV8DwwM1G233ab58+frpptu0vz5862aty9YsEBPP/203n33XXXo0EGenp56++23tX379kLjznsrob9/+a1uYqUUiqVJsJea1/BWVo5Z3/5WcjXBAAAAAICiMQxDdm5u1pura/6xEt7y+ikVx5tvvqn//ve/2rZtW4ncg4kTJ+qdd97RqVOnivX9xo0b6/fff1dKSoplbMuWLbKzs1PDhg0tY+Hh4ZowYYK2bt2qZs2aaf78+ZbPGjRooP/85z9as2aN+vfvrzlz5hR4vvDwcP3111/5xocPH66FCxdq27Zt+ueffzRkyBCreG6++WY9+uijCg8PV7169a66IkuS9u7dq5o1a6patWpFvh9lzaZJqY0bN6p3794KDg6WYRhatmxZofNHjBghwzDybU2bNrXMmTx5cr7PL13yhpIzuO3FhucLI6OvudEdAAAAAKDqad68uYYPH64ZM2Zc8fMzZ84oNjbWasvKyirweB06dFCLFi30+uuvF3retLQ0q7LAqKgoHT58WMOHD5eLi4vuv/9+7d27V+vXr9fjjz+ue++9V9WrV9eRI0c0YcIEbdu2TceOHdOaNWt08OBBNW7cWGlpaRozZow2bNigY8eOacuWLYqMjLTqOXW5iIgIbd68Od94//79lZSUpEceeURdu3a16jtVv3597dy5U6tXr9bff/+tF198UZGRkYVer3JLIXv06HHVebZk06RUSkqKwsLC9OGHHxZpfl7tZt4WHR0tX19fDRw40Gpe06ZNreZd6X9wXL8+LYPl4ming2eS9dvxeFuHAwAAAACoAF5++WWZTKYrftawYUMFBQVZbbt27bri3Dz/+c9/9Omnnyo6uuCex3///bfCw8Ottocfflhubm5avXq1zp8/r7Zt22rAgAG6/fbbNXPmTEmSm5ub9u/fr3vuuUcNGjTQ6NGj9dhjj+nhhx+Wvb294uLidN9996lBgwYaNGiQevbsadWU/HLDhw/Xn3/+qQMHDliNe3p6qnfv3vr99981fPhwq88efvhh9e/fX4MHD1b79u0VFxenRx99tNB7kp6ermXLlmnUqFGFzrM1w1xOlrgYhqHvvvtOffv2LfJ3li1bpv79++vIkSOWTvKTJ0/WsmXLFBUVVexYEhMT5e3trYSEBHl5eRX7OLaWlZWlFStWqFevXoW+kvJ6jFsUpW9/O6nBbUI0dUCLUjkHUFGVxTMI4Mp4/gDb4fkDSkd6erqOHDmi0NDQfI2y85hMJiUmJsrLy6tUe0rh+jzzzDNKTEzU//3f/5XaOWbNmqXvvvtOa9asKbVzFPabLGpepUL/Sj/77DN169Yt36sNDx48qODgYN14440aPnz4VTvSo/iGtK0lSfrvnlNKzsi2dTgAAAAAAJRrL7zwgmrXrl3garGS4OjoqA8++KDUjl9SKuzb906dOqWVK1daNReTpPbt22vu3Llq2LChYmJiNGXKFHXq1El79+6Vp6fnFY+VkZGhjIwMy35iYqKU+//lKax2tbzLi700r6FlDQ+F+rnpSFyqvv8tWoPa1Cy1cwEVTVk8gwCujOcPsB2eP6B0ZGVlyWw2y2QyFZjMyCuEypuH8snLy0vPPfeclLu6rTQ8+OCDpXr8vGObzWZlZWXJ3t7e6rOi/jugwpbvvfHGG3r33Xd16tQpOTk5FTgvPj5etWvX1nvvvaeRI0decc7kyZOvWPM5f/58ubm5XcNVVE0/nTS0/Li96niY9Z/mObYOBwAAAAAqHQcHBwUGBiokJKTQ/wYGykpmZqaio6MVGxur7GzryqnU1FQNGzbsquV7FXKllNls1ueff6577733qg+jj4+PGjRooEOHDhU4Z8KECRo3bpxlPzExUSEhIerRo0eF7ym1du1ade/evVTr+dsmZWjFOxt1NFmq17qTGlS/8oo0oKopq2cQQH48f4Dt8PwBpSM9PV3R0dHy8PAosKeU2WxWUlKSPD09ZRhGmceIqiU9PV2urq7q3LnzFXtKFUWFTEr98ssvOnToUIErny6VnJysw4cP69577y1wjrOzs5ydnfONOzo6Vop/kZb2dQT7Our2xgFa/edpLd0dq0m9fUvtXEBFVFn+LgEqIp4/wHZ4/oCSlZOTI8MwZGdnV2AT87xSrbx5QGmys7OTYRhX/Pu+qH//2/RXmpycrKioKMub8o4cOaKoqChLY/IJEybovvvuy/e9zz77TO3bt1ezZs3yffb000/rl19+0dGjR7V161b169dP9vb2Gjp0aBlcUdWV1/D8u90nlJFNCR8AAAAAACicTZNSO3fuVHh4uMLDwyVJ48aNU3h4uCZNmiRJiomJyffmvISEBC1durTAVVInTpzQ0KFD1bBhQw0aNEh+fn769ddf5e/vXwZXVHV1buCvQC8XXUjN0tq/Tts6HAAAAAAAUM7ZtHyvS5cuKqzP+ty5c/ONeXt7KzU1tcDvLFiwoMTiQ9HZ2xka2KamPvj5kBZGRuuuFsG2DgkAAAAAAJRjFJmixAxqEyJJ2nzonKLPF5w4BAAAAAAAICmFEhPi66aO9fxkNkuLd52wdTgAAAAAAJQLmZmZqlevnrZu3Voqx9+wYYMMw1B8fHyRv/Pcc8/p8ccfL5V4ioqkFErU4NyG54t3RivHVHBpJgAAAACg8jt79qweeeQR1apVS87OzgoMDFRERIS2bNlimVOnTh0ZhiHDMOTm5qbmzZvr008/tTrOtSZdjh49KsMwLC9Ws7XZs2crNDRUN998s1QK8d18882KiYmRt7d3kb/z9NNP64svvtA///xTIjEUB0kplKgeTarLx81RMQnp2nTwrK3DAQAAAADY0D333KPdu3friy++0N9//63ly5erS5cuiouLs5r38ssvKyYmRnv37tW//vUvjRo1SitXrrRZ3CXJbDZr5syZBb6wrTCZmZlFmufk5KTAwEAZhlHkY1erVk0RERGaNWvWNcdVUkhKoUS5ONqrb8sakqSFkdG2DgcAAAAAYCPx8fHatGmTpk6dqq5du6p27dpq166dJkyYoD59+ljN9fT0VGBgoG688UaNHz9evr6+Wrt2banFlpGRobFjxyogIEAuLi665ZZbFBkZafn8woULGj58uPz9/eXq6qr69etrzpw5Um6iaMyYMQoKCpKLi4tq166tN954o8Bz7dq1S4cPH9add95pGQsNDZUkhYeHyzAMdenSRZI0YsQI9e3bV6+99pqCg4PVsGFDSdJXX32lNm3aWO7TsGHDdObMGcvxLl9JNnfuXPn4+Gj16tVq3LixPDw8dMcddygmJsYqtt69e9v0hXEkpVDiBre92PB87V+ndS45w9bhAAAAAEClZDablZWRY7VlZ+bkGyvpzWwuWqsWDw8PeXh4aNmyZcrIKNp/G5pMJi1dulQXLlyQk5PTdd6hgj377LNaunSpvvjiC/3222+qV6+eIiIidP78eUnSiy++qL/++ksrV67Uvn37NGvWLFWrVk2SNGPGDC1fvlyLFi3SgQMHNG/ePNWpU6fAc23atEkNGjSQp6enZWzHjh2SpHXr1ikmJkbffvut5bOffvpJBw4c0Nq1a/XDDz9IkrKysvTKK6/o999/17Jly3T06FGNGDGi0GtMTU3VO++8o6+++kobN27U8ePH9fTTT1vNadeunU6cOKGjR48W6z5eLwebnBWVWuMgL4WF+Oj36Hh999tJjep8o61DAgAAAIBKJzvTpI+f+KXMzzt6+q1ydLa/6jwHBwfNnTtXo0aN0uzZs9WqVSvdeuutGjJkiFq0aGE1d/z48Zo4caIyMjKUnZ0tX19fPfTQQ6USf0pKimbNmqW5c+eqZ8+ekqRPPvlEa9eu1WeffaZnnnlGx48fV3h4uNq0aSPl9r3Kc/z4cdWvX1+33HKLDMNQ7dq1Cz3fsWPHFBwcbDXm7+8vSfLz81NgYKDVZ+7u7vr000+tknIPPvig5Z9vvPFGzZgxQ23btlVycrI8PDyueN6srCzNnj1bdevWlSSNGTNGL7/8stWcvLiOHTtWaGKttLBSCqVicJuLq6UWRB4vchYdAAAAAFC53HPPPTp16pSWL1+uO+64Qxs2bFCrVq00d+5cq3nPPPOMoqKi9PPPP6t9+/Z6//33Va9evVKJ6fDhw8rKylLHjh0tY46OjmrXrp327dsnSXrkkUe0YMECtWzZUs8++6zVW/NGjBihqKgoNWzYUGPHjtWaNWsKPV9aWppcXFyKHF/z5s3zrRLbtWuXevfurVq1asnT01O33nqrlJsgK4ibm5slISVJQUFBViV/kuTq6irlrqqyBVZKoVT0DgvSKz/8pcNnU7Tr2AW1qeNr65AAAAAAoFJxcLLT6Om3WvZNJpOSkhLl6eklO7vSW4Pi4HRtx3ZxcVH37t3VvXt3vfjii3rooYf00ksvWZWfVatWTfXq1VO9evW0ePFiNW/eXG3atFGTJk1K4QqurmfPnjp27JhWrFihtWvX6vbbb9djjz2md955R61atdKRI0e0cuVKrVu3ToMGDVK3bt20ZMmSKx6rWrVq+uOPP4p8bnd3d6v9lJQURUREKCIiQvPmzZO/v7+OHz+uiIiIQhuhOzo6Wu0bhpFv0UheuWLeyq2yxkoplApPF0fd1SJIouE5AAAAAJQKwzDk6GxvtTk42ecbK+ntWt7wdiVNmjRRSkpKgZ+HhIRo8ODBmjBhwnWdpyB169aVk5OTtmzZYhnLyspSZGSkVRLM399f999/v77++mtNmzZNH3/8seUzLy8vDR48WJ988okWLlyopUuXWhI8lwsPD9f+/futEkJ5K6FycnKuGu/+/fsVFxenN998U506dVKjRo3yrXgqrr1798rR0VFNmzYtkeNdK1ZKodQMbhuixbtO6Ic9MZrUu4k8XRyL8C0AAAAAQGUQFxengQMH6sEHH1SLFi3k6empnTt36q233tLdd99d6HefeOIJNWvWTDt37rT0dZKkP/74w6phuGEYCgsLK/A4Bw4cyDfWtGlTPfLII3rmmWfk6+urWrVq6a233lJqaqpGjhwpSZo0aZJat26tpk2bKiMjQz/88IMaN24sSXrvvfcUFBSk8PBw2dnZafHixQoMDJSPj88VY+jatauSk5P1559/qlmzZpKkgIAAubq6atWqVapZs6ZcXFzk7e19xe/XqlVLTk5O+uCDD/Tvf/9be/fu1SuvvFLo/SuqTZs2qVOnTpYyvrJGUgqlpnXtG1TX312Hz6bov7/HaFj7WrYOCQAAAABQRjw8PCz9ofL6OIWEhGjUqFF6/vnnC/1ukyZN1KNHD02aNEkrVqywjHfu3Nlqnr29vbKzsws8zpAhQ/KNRUdH680335TJZNK9996rpKQktWnTRqtXr9YNN9wg5a5kmjBhgo4ePSpXV1d16tRJCxYskCR5enrqrbfe0sGDB2Vvb6+2bdtqxYoVBZZM+vn5qV+/fpo3b57eeOMNKbcJ/IwZM/Tyyy9r0qRJ6tSpkzZs2HDF7/v7+2vu3Ll6/vnnNWPGDLVq1UrvvPOO+vTpU+g9LIoFCxZo8uTJ132c4jLMdKHOJzExUd7e3kpISJCXl5etwym2rKwsrVixQr169cpXS1pWPtn4j15bsU9hIT76/rGORfgGUHmUh2cQqKp4/gDb4fkDSkd6erqOHDmi0NDQAptmm0wmJSYmysurdHtK4drt2bNH3bt31+HDhwt8W15ZW7lypZ566int2bNHDg7XvmapsN9kUfMq/EpRqvq1qiEHO0O/R8drX0yircMBAAAAAKDMtWjRQlOnTtWRI0dsHYpFSkqK5syZU6yEVEkhKYVSVc3DWd2bVJdoeA4AAAAAqMJGjBih5s2b2zoMiwEDBqh9+/Y2jYGkFErd4LYhkqRlUSeVnnX1NwsAAAAAAIDKj6QUSl2n+v4K9nZRfGqW1vx12tbhAAAAAACAcoCkFEqdvZ2hAW0urpZaGHnc1uEAAAAAAIBygKQUysTA1jVlGNKWQ3GKPp9q63AAAAAAAICNkZRCmQjxddMt9apJkhbtpOE5AAAAAABVHUkplJm8hueLd55Qjsls63AAAAAAAIANkZRCmenepLpucHNUbGK6Nv591tbhAAAAAAAAGyIphTLj7GCvfuE1JUkLaHgOAAAAAKgiMjMzVa9ePW3durXEjrlhwwYZhqH4+HhJ0qpVq9SyZUuZTKYSO0dpIymFMpVXwvfTvjM6m5Rh63AAAAAAAKXo7NmzeuSRR1SrVi05OzsrMDBQERER2rJli2VOnTp1ZBiGDMOQm5ubmjdvrk8//dTqOJcnYK7m6NGjMgxDUVFRJX5NxTF79myFhobq5ptvLrVz3HHHHXJ0dNS8efNK7RwljaQUylTDQE+F1/JRtsmsb387YetwAAAAAACl6J577tHu3bv1xRdf6O+//9by5cvVpUsXxcXFWc17+eWXFRMTo7179+pf//qXRo0apZUrV9os7pJkNps1c+ZMjRw5stTPNWLECM2YMaPUz1NSSEqhzA1uc3G11MLIaJnNNDwHAAAAgMooPj5emzZt0tSpU9W1a1fVrl1b7dq104QJE9SnTx+ruZ6engoMDNSNN96o8ePHy9fXV2vXri212DIyMjR27FgFBATIxcVFt9xyiyIjIy2fX7hwQcOHD5e/v79cXV1Vv359zZkzR8otxRszZoyCgoLk4uKi2rVr64033ijwXLt27dLhw4d15513WsZuvvlmjR8/3mre2bNn5ejoqI0bN0qSvvrqK7Vp08Zyb4YNG6YzZ84Uel29e/fWzp07dfjw4WLfm7JEUgpl7q6wYLk52eufcymKPHrB1uEAAAAAQIVkNpuVlZ5uvWWk5x8r4a2oiws8PDzk4eGhZcuWKSOjaO1bTCaTli5dqgsXLsjJyek671DBnn32WS1dulRffPGFfvvtN9WrV08RERE6f/68JOnFF1/UX3/9pZUrV2rfvn2aNWuWqlWrJkmaMWOGli9frkWLFunAgQOaN2+e6tSpU+C5Nm3apAYNGsjT09MyNnz4cC1YsMDqXi5cuFDBwcHq1KmTJCkrK0uvvPKKfv/9dy1btkxHjx7ViBEjCr2uWrVqqXr16tq0adN136Oy4GDrAFD1eDg7qHeLYC3cGa2FkdFqF+pr65AAAAAAoMLJzsjQjPsHlPl5x36xRI4uLled5+DgoLlz52rUqFGaPXu2WrVqpVtvvVVDhgxRixYtrOaOHz9eEydOVEZGhrKzs+Xr66uHHnqoVOJPSUnRrFmzNHfuXPXs2VOS9Mknn2jt2rX67LPP9Mwzz+j48eMKDw9XmzZtpNy+V3mOHz+u+vXr65ZbbpFhGKpdu3ah5zt27JiCg4OtxgYNGqQnn3xSmzdvtiSh5s+fr6FDh8owDEnSgw8+aJl/4403asaMGWrbtq2Sk5Pl4eFR4PmCg4N17NixYt2bssZKKdjEoNyG5z/+cUqJ6Vm2DgcAAAAAUAruuecenTp1SsuXL9cdd9yhDRs2qFWrVpo7d67VvGeeeUZRUVH6+eef1b59e73//vuqV69eqcR0+PBhZWVlqWPHjpYxR0dHtWvXTvv27ZMkPfLII1qwYIFatmypZ5991uqteSNGjFBUVJQaNmyosWPHas2aNYWeLy0tTS6XJfH8/f3Vo0cPS1PyI0eOaNu2bRo+fLhlzq5du9S7d2/VqlVLnp6euvXWW6XcpFhhXF1dlZqaek33xFZYKQWbaFXLR/UDPHTwTLKWR53Sv24qPLMMAAAAALDm4OyssV8sseybTCYlJiXKy9NLdnaltwbFwdn5mua7uLioe/fu6t69u1588UU99NBDeumll6xK0apVq6Z69eqpXr16Wrx4sZo3b642bdqoSZMmpXAFV9ezZ08dO3ZMK1as0Nq1a3X77bfrscce0zvvvKNWrVrpyJEjWrlypdatW6dBgwapW7duWrJkyRWPVa1aNf3xxx/5xocPH66xY8fqgw8+0Pz589W8eXM1b95cyl3NFRERoYiICM2bN0/+/v46fvy4IiIilJmZWWjs58+fl7+/fwndidLFSinYhGEYGpy7WmrRzmhbhwMAAAAAFY5hGHJ0cbHenF3yj5XwlldeVlxNmjRRSkpKgZ+HhIRo8ODBmjBhwnWdpyB169aVk5OTtmzZYhnLyspSZGSkVRLM399f999/v77++mtNmzZNH3/8seUzLy8vDR48WJ988okWLlyopUuXWvpRXS48PFz79+/P14vr7rvvVnp6ulatWqX58+dbrZLav3+/4uLi9Oabb6pTp05q1KjRVZucS1J6eroOHz6s8PDwa74vtsBKKdhM/1Y1NXXVfu05kaA/TyWoabC3rUMCAAAAAJSQuLg4DRw4UA8++KBatGghT09P7dy5U2+99ZbuvvvuQr/7xBNPqFmzZtq5c6elr5Mk/fHHH1YNww3DUFhYWIHHOXDgQL6xpk2b6pFHHtEzzzwjX19f1apVS2+99ZZSU1M1cuRISdKkSZPUunVrNW3aVBkZGfrhhx/UuHFjSdJ7772noKAghYeHy87OTosXL1ZgYKB8fHyuGEPXrl2VnJysP//8U82aNbOMu7u7q2/fvnrxxRe1b98+DR061PJZrVq15OTkpA8++ED//ve/tXfvXr3yyiuF3jNJ+vXXX+Xs7KwOHTpcdW55QFIKNuPr7qQeTQL14x8xWhQZrSl3k5QCAAAAgMrCw8PD0h8qr49TSEiIRo0apeeff77Q7zZp0kQ9evTQpEmTtGLFCst4586drebZ29srOzu7wOMMGTIk31h0dLTefPNNmUwm3XvvvUpKSlKbNm20evVq3XDDDZIkJycnTZgwQUePHpWrq6s6deqkBQsWSJI8PT311ltv6eDBg7K3t1fbtm21YsWKAksm/fz81K9fP82bN09vvPGG1WfDhw9Xr1691LlzZ9WqVcsy7u/vr7lz5+r555/XjBkz1KpVK73zzjvq06dPofftm2++0fDhw+Xm5lbovPLCMBf1XY5VSGJiory9vZWQkCAvLy9bh1NsWVlZWrFihXr16iVHR0dbh3NFG/8+q/s+3yEvFwfteKGbXBztbR0SUGIqwjMIVFY8f4Dt8PwBpSM9PV1HjhxRaGhovqbZeUwmkxITE+XlVbo9pXDt9uzZo+7du+vw4cOFvjnvepw7d04NGzbUzp07FRoaWirnuFRhv8mi5lX4lcKmbqlXTTV8XJWYnq3Vf8baOhwAAAAAAEpcixYtNHXqVB05cqTUznH06FF99NFHZZKQKikkpWBTdnaGBrapKUlasIOG5wAAAACAymnEiBGWt+uVhjZt2mjw4MGldvzSQFIKNjewTYgMQ9r2T5yOxRX8BgYAAAAAAFB5kJSCzdXwcVWn+v6SpEU7WS0FAAAAAEBVQFIK5cKQtiGSpMU7Tyg7x2TrcAAAAACgXOJdZSgvSuK3SFIK5UK3xtXl6+6kM0kZ+uXvs7YOBwAAAADKlby3Waampto6FEC65Ld4PW9adSjBeIBic3Kw0z2tauiTTUe0IDJatzeubuuQAAAAAKDcsLe3l4+Pj86cOSNJcnNzk2EYVnNMJpMyMzOVnp4uOzvWoKB0mM1mpaam6syZM/Lx8ZG9vX2xj0VSCuXG4LYh+mTTEf28/4zOJKYrwMvF1iEBAAAAQLkRGBgoSZbE1OXMZrPS0tLk6uqaL2EFlDQfHx/Lb7K4SEqh3KgX4KnWtW/QrmMXtPS3k3qkS11bhwQAAAAA5YZhGAoKClJAQICysrLyfZ6VlaWNGzeqc+fO11VSBVyNo6Pjda2QykNSCuXK4LYh2nXsghZGHte/b72R7D4AAAAAXMbe3v6KCQF7e3tlZ2fLxcWFpBQqBIpMUa7c2TxI7k72OhqXqu1Hzts6HAAAAAAAUEpISqFccXd2UJ+WwZKkhZHRtg4HAAAAAACUEpJSKHcGt60lSVrxR4wS0vLXSQMAAAAAgIqPpBTKnbCa3mpY3VMZ2SYtjzpp63AAAAAAAEApICmFcscwDA1uGyJJWkAJHwAAAAAAlRJJKZRL/cJryMneTn+eStTekwm2DgcAAAAAAJQwmyalNm7cqN69eys4OFiGYWjZsmWFzt+wYYMMw8i3xcbGWs378MMPVadOHbm4uKh9+/basWNHKV8JStoN7k7q0bS6RMNzAAAAAAAqJZsmpVJSUhQWFqYPP/zwmr534MABxcTEWLaAgADLZwsXLtS4ceP00ksv6bffflNYWJgiIiJ05syZUrgClKYhuQ3Pl0WdVHpWjq3DAQAAAAAAJcimSamePXvq1VdfVb9+/a7pewEBAQoMDLRsdnb/u4z33ntPo0aN0gMPPKAmTZpo9uzZcnNz0+eff14KV4DSdHNdP9W8wVVJ6dlauTfG1uEAAAAAAIASVCF7SrVs2VJBQUHq3r27tmzZYhnPzMzUrl271K1bN8uYnZ2dunXrpm3bttkoWhSXnZ2hQW1yG57voIQPAAAAAIDKxMHWAVyLoKAgzZ49W23atFFGRoY+/fRTdenSRdu3b1erVq107tw55eTkqHr16lbfq169uvbv31/gcTMyMpSRkWHZT0xMlCRlZWUpKyurFK+odOXFXpGvoW9YoKat+1vbj5zXwdh41fFzt3VIQJFVhmcQqKh4/gDb4fkDbIfnD+VFUX+DFSop1bBhQzVs2NCyf/PNN+vw4cN6//339dVXXxX7uG+88YamTJmSb3zNmjVyc3Mr9nHLi7Vr19o6hOvSyNtOf8XbaeqiTepd22TrcIBrVtGfQaAi4/kDbIfnD7Adnj/YWmpqapHmVaik1JW0a9dOmzdvliRVq1ZN9vb2On36tNWc06dPKzAwsMBjTJgwQePGjbPsJyYmKiQkRD169JCXl1cpRl+6srKytHbtWnXv3l2Ojo62DqfY7Guf1pgFv+v3RBfNiOgsR/sKWXWKKqiyPINARcTzB9gOzx9gOzx/KC/yKtCupsInpaKiohQUFCRJcnJyUuvWrfXTTz+pb9++kiSTyaSffvpJY8aMKfAYzs7OcnZ2zjfu6OhYKR7kin4dPZoFq5rHPp1NztTmwxfUo2nBCUagPKrozyBQkfH8AbbD8wfYDs8fbK2ovz+bJqWSk5N16NAhy/6RI0cUFRUlX19f1apVSxMmTNDJkyf15ZdfSpKmTZum0NBQNW3aVOnp6fr000/1888/a82aNZZjjBs3Tvfff7/atGmjdu3aadq0aUpJSdEDDzxgk2vE9XNysNM9rWrq/zb+o0U7o0lKAQAAAABQCdg0KbVz50517drVsp9XQnf//fdr7ty5iomJ0fHjxy2fZ2Zm6qmnntLJkyfl5uamFi1aaN26dVbHGDx4sM6ePatJkyYpNjZWLVu21KpVq/I1P0fFMrBNiP5v4z/6ef8ZxSakK9DbxdYhAQAAAACA62DTpFSXLl1kNpsL/Hzu3LlW+88++6yeffbZqx53zJgxhZbroeKpF+ChtnVuUOTRC1r62wk91rWerUMCAAAAAADXgY7RqDAGt60lSVq0M1omU8HJTAAAAAAAUP6RlEKF0at5oDycHXQsLlW/HomzdTgAAAAAAOA6kJRCheHm5KA+LYMlSQsjo20dDgAAAAAAuA4kpVChDGkbIklauTdWCalZtg4HAAAAAAAUE0kpVCjNa3irUaCnMrNNWhZ10tbhAAAAAACAYiIphQrFMAzLaqkFkdGFvr0RAAAAAACUXySlUOH0Da8hJwc77YtJ1N6TibYOBwAAAAAAFANJKVQ4Pm5OuqNpoCRpQeRxW4cDAAAAAACKgaQUKqS8Er7lUaeUlplj63AAAAAAAMA1IimFCummG/1Uy9dNSRnZWvFHjK3DAQAAAAAA14ikFCokOztDg9rUlCQtjIy2dTgAAAAAAOAakZRChTWgdYjsDGnH0fM6fDbZ1uEAAAAAAIBrQFIKFVagt4u6NgyQJC3ayWopAAAAAAAqEpJSqNAG5TY8X7rrhLJyTLYOBwAAAAAAFBFJKVRotzUKUDUPZ51LztRP+87YOhwAAAAAAFBEJKVQoTna22lA64sNzynhAwAAAACg4iAphQov7y18Gw6cUUxCmq3DAQAAAAAARUBSChXejf4eahfqK5NZWrLzhK3DAQAAAAAARUBSCpXCkNyG54t2RctkMts6HAAAAAAAcBUkpVAp9GwWJE8XB0WfT9O2f+JsHQ4AAAAAALgKklKoFFyd7HV3y2BJ0oJIGp4DAAAAAFDekZRCpTGkbS1J0uq9sbqQkmnrcAAAAAAAQCFISqHSaFbDW02DvZSZY9KyqJO2DgcAAAAAABSCpBQqlcG5Dc8XRkbLbKbhOQAAAAAA5RVJKVQqd4fVkLODnfbHJmnPiQRbhwMAAAAAAApAUgqVirebo3o1D5JoeA4AAAAAQLlGUgqVzqA2F0v4/vv7KaVmZts6HAAAAAAAcAUkpVDp3HSjr+r4uSk5I1s/7omxdTgAAAAAAOAKSEqh0jEMQ4MuaXgOAAAAAADKH5JSqJQGtKopeztDO49d0KEzSbYOBwAAAAAAXIakFCqlAC8XdW0YIElatPOErcMBAAAAAACXISmFSmtIbgnf0l0nlJltsnU4AAAAAADgEiSlUGl1aeivAE9nxaVk6qd9p20dDgAAAAAAuARJKVRaDvZ2GtC6piRp4U4angMAAAAAUJ6QlEKlNqjNxRK+X/4+q1PxabYOBwAAAAAA5CIphUqtTjV33XSjr8xmaTENzwEAAAAAKDdISqHSG9K2liRp0c5omUxmW4cDAAAAAABISqEquKNZoLxcHHQyPk1bDp+zdTgAAAAAAICkFKoCF0d79Q2vIUlaEEnDcwAAAAAAygOSUqgSBre92PB8zZ+xOp+SaetwAAAAAACo8khKoUpoGuyt5jW8lZVj1ne7T9o6HAAAAAAAqjySUqgyBuWulloYeVxmMw3PAQAAAACwJZJSqDL6hAXLxdFOf59O1u7oeFuHAwAAAABAlUZSClWGt6ujejUPkiQtouE5AAAAAAA2RVKqkqNKzdrgNhdL+Jb/fkrJGdm2DgcAAAAAgCqLpFQlFnciWWd/dVPCmTRbh1JutAv1VWg1d6Vm5ujHPadsHQ4AAAAAAFUWSalKbMd/jyoz3l4b5v0tk4klU5JkGIYGWxqeU8IHAAAAAICtkJSqxG4ZXE+GvVmn/0nUH+tP2DqccqN/qxqytzP02/F4/X06ydbhAAAAAABQJZGUqsQ8fV3k3ShDkvTrssOKP51q65DKhQBPF93eKEBitRQAAAAAADZDUqqScw/JUo0GPsrOMunnr/bJTBmfJGlIu4slfN/tPqmM7BxbhwMAAAAAQJVj06TUxo0b1bt3bwUHB8swDC1btqzQ+d9++626d+8uf39/eXl5qUOHDlq9erXVnMmTJ8swDKutUaNGpXwl5ZdhSJ2H1Zejs71iDiVoD2V8kqTO9f1V3ctZ51Myte6vM7YOBwAAAACAKsemSamUlBSFhYXpww8/LNL8jRs3qnv37lqxYoV27dqlrl27qnfv3tq9e7fVvKZNmyomJsaybd68uZSuoGLw9HPRzffUkyjjs3Cwt9PA1hdXSy2IPG7rcAAAAAAAqHIcbHnynj17qmfPnkWeP23aNKv9119/Xd9//73++9//Kjw83DLu4OCgwMDAEo21omvaKViHdp3RyQMX9PNX+9RvXCsZdoatw7KpQW1CNHP9IW0+dE4nLqSq5g1utg4JAAAAAIAqo0L3lDKZTEpKSpKvr6/V+MGDBxUcHKwbb7xRw4cP1/HjrIQxDEO33duIMr5L1PJz0811/WQ2S4t3cj8AAAAAAChLNl0pdb3eeecdJScna9CgQZax9u3ba+7cuWrYsKFiYmI0ZcoUderUSXv37pWnp+cVj5ORkaGMjAzLfmJioiQpKytLWVlZZXAlpSMv9rw/Xb0d1L5vqDYvPKRtyw6rRmNvefu72jhK2xrQKlhbD8dp0c5oPdK5juyr+OoxlKzLn0EAZYfnD7Adnj/Adnj+UF4U9TdomM3mcvE6NsMw9N1336lv375Fmj9//nyNGjVK33//vbp161bgvPj4eNWuXVvvvfeeRo4cecU5kydP1pQpU654Dje3ylXSZTZL5yJdlRHnIKcbsuXfPk1GFc7DZJmkSTvtlZpj6N+Nc9TYp1w8DgAAAAAAVFipqakaNmyYEhIS5OXlVeC8CrlSasGCBXrooYe0ePHiQhNSkuTj46MGDRro0KFDBc6ZMGGCxo0bZ9lPTExUSEiIevToUejNK++ysrK0du1ade/eXY6OjpbxpJvStfj1Xcq84KDa7mFq1qWGTeO0td+N/frq1+M6ahesp3qF2TocVCIFPYMASh/PH2A7PH+A7fD8obzIq0C7mgqXlPrmm2/04IMPasGCBbrzzjuvOj85OVmHDx/WvffeW+AcZ2dnOTs75xt3dHSsFA/y5dfhG+iojvfU0y/f/K0dy48qNCxAPgGVa0XYtRjWvra++vW4ftp/RokZJvl55P8tANejsvxdAlREPH+A7fD8AbbD8wdbK+rvz6aNzpOTkxUVFaWoqChJ0pEjRxQVFWVpTD5hwgTdd999lvnz58/Xfffdp3fffVft27dXbGysYmNjlZCQYJnz9NNP65dfftHRo0e1detW9evXT/b29ho6dKgNrrD8atqphmo0vEHZWSb9/OU+mU1Vt2ytcZCXwmp6KyvHrO92n7R1OAAAAAAAVAk2TUrt3LlT4eHhCg8PlySNGzdO4eHhmjRpkiQpJibG6s15H3/8sbKzs/XYY48pKCjIsj3xxBOWOSdOnNDQoUPVsGFDDRo0SH5+fvr111/l7+9vgyssvwy7i2/jc8h7G9+Gqv32ucFta0mSFkRGq5y0WQMAAAAAoFKzaflely5dCk0AzJ0712p/w4YNVz3mggULSiS2qsCrmqs69q+rX775W79+d1i1m/lV2TK+3mFBeuWHv3ToTLJ+O35BrWv72jokAAAAAAAqNZuulILtXSzj86nyZXyeLo66s0WQJGlhZLStwwEAAAAAoNIjKVXFXSzja0wZn6QhbUMkSf/9PUZJ6Vm2DgcAAAAAgEqNpBTkVc1VN/erK0n69bvDij+TauuQbKJ17Rt0o7+70rJy9MOeGFuHAwAAAABApUZSCpKkZp3/V8a3/qv9VbKMzzAMy2opSvgAAAAAAChdJKUgXVbGd+pgvP74pWqW8fVvVVMOdoaiouO1PzbR1uEAAAAAAFBpkZSCxaVlfNu+O6yEs1WvjK+ah7O6Na4usVoKAAAAAIBSRVIKVixlfJkm/fxl1SzjG9zuYgnfd7tPKiM7x9bhAAAAAABQKZGUghXK+KTO9f0V5O2i+NQsrfnztK3DAQAAAACgUiIphXyqehmfvZ2hga1rSpTwAQAAAABQakhK4Yqada6hGg2qbhnfwDYhMgxp86Fzij5ftZJyAAAAAACUBZJSuCLDztBt911axnfS1iGVqRBfN91Sr5okafFOVksBAAAAAFDSSEqhQNZlfIeqXBnfoDYXG54v3nVCOVVspRgAAAAAAKWNpBQKVZXL+Ho0rS4fN0fFJKRr48Gztg4HAAAAAIBKhaQUCmXYGep6b9Us43N2sFf/8NyG5zso4QMAAAAAoCSRlMJVeftfXsaXZuuQyszgthdL+NbtO62zSRm2DgcAAAAAgEqDpBSKxLqMb1+VKeNrGOipliE+yjaZ9d3uE7YOBwAAAACASoOkFIrEUsbnZKdTB+O1d2PVKeMbkrtaakFktMzmqpGMAwAAAACgtJGUQpF5+7uqQ796kqSt31adMr67woLl5mSvf86maOexC7YOBwAAAACASoGkFK5J81trKLh+1Srj83B20F0tgiRJCyNpeA4AAAAAQEkgKYVrYtgZuu2+qlfGN7htLUnSj3tilJieZetwAAAAAACo8EhK4ZpVxTK+VrV8VC/AQ2lZOfrv76dsHQ4AAAAAABUeSSkUy6VlfOu/qvxlfIZhWBqeL6KEDwAAAACA60ZSCsVyaRnfyb+rRhlfv/AacrQ39PuJBP11KtHW4QAAAAAAUKEVKykVHR2tEydOWPZ37NihJ598Uh9//HFJxoZyzqqM77vDlb6Mz8/DWd2bVJckLdrJaikAAAAAAK5HsZJSw4YN0/r16yVJsbGx6t69u3bs2KEXXnhBL7/8cknHiHLMUsaXkVMlyvjyGp5/t/uk0rNybB0OAAAAAAAVVrGSUnv37lW7du0kSYsWLVKzZs20detWzZs3T3Pnzi3pGFGOXSzja1RlyvhuqVdNNXxclZCWpdV/xto6HAAAAAAAKqxiJaWysrLk7OwsSVq3bp369OkjSWrUqJFiYmJKNkKUe97+burQr66UW8aXeK7ylvHZ2xka0LqmJGkhDc8BAAAAACi2YiWlmjZtqtmzZ2vTpk1au3at7rjjDknSqVOn5OfnV9IxogJofmtNSxnfz19W7jK+gW1qyjCkrYfjdDwu1dbhAAAAAABQIRUrKTV16lT93//9n7p06aKhQ4cqLCxMkrR8+XJLWR+qlqpUxlfzBjd1qu8v0fAcAAAAAIBiK1ZSqkuXLjp37pzOnTunzz//3DI+evRozZ49uyTjQwVSlcr4BrcJkSQt3hWt7ByTrcMBAAAAAKDCKVZSKi0tTRkZGbrhhhskSceOHdO0adN04MABBQQElHSMqECsyvgq8dv4ujUJkK+7k04nZmjjwbO2DgcAAAAAgAqnWEmpu+++W19++aUkKT4+Xu3bt9e7776rvn37atasWSUdIyoQSxmfo51OHojXn5sqZxmfs4O9+ofXkCQt2EEJHwAAAAAA16pYSanffvtNnTp1kiQtWbJE1atX17Fjx/Tll19qxowZJR0jKhhvfzfdlFvGt+XbylvGN7jtxRK+n/af0ZmkdFuHAwAAAABAhVKspFRqaqo8PT0lSWvWrFH//v1lZ2enm266SceOHSvpGFEBtehSU0H1vCt1GV/96p5qVctHOSazlu6qnCvCAAAAAAAoLcVKStWrV0/Lli1TdHS0Vq9erR49ekiSzpw5Iy8vr5KOERXQxTK+xpW+jG9I21pS7lv4zObKl3gDAAAAAKC0FCspNWnSJD399NOqU6eO2rVrpw4dOki5q6bCw8NLOkZUUD4Blb+M784WQXJ3steRcynaceS8rcMBAAAAAKDCKFZSasCAATp+/Lh27typ1atXW8Zvv/12vf/++yUZHyo46zK+/ZWujM/d2UG9w4IlSQsjaXgOAAAAAEBRFSspJUmBgYEKDw/XqVOndOLECUlSu3bt1KhRo5KMDxWcdRnfBf25+ZStQypxeQ3PV+yNUUJalq3DAQAAAACgQihWUspkMunll1+Wt7e3ateurdq1a8vHx0evvPKKTCZTyUeJCs0nwE039b1Yxrd16aFKV8bXMsRHDap7KD3LpOW/V76kGwAAAAAApaFYSakXXnhBM2fO1Jtvvqndu3dr9+7dev311/XBBx/oxRdfLPkoUeG16HqxjC+rEpbxGYahwbkNzxdGHrd1OAAAAAAAVAjFSkp98cUX+vTTT/XII4+oRYsWatGihR599FF98sknmjt3bslHiQqvspfx9QuvISd7O+09mai9JxNsHQ4AAAAAAOVesZJS58+fv2LvqEaNGun8ed5AhiurzGV8vu5O6tG0uiRp0U4angMAAAAAcDXFSkqFhYVp5syZ+cZnzpypFi1alERcqKTylfGZK08ZX17D8+92n1R6Vo6twwEAAAAAoFxzKM6X3nrrLd15551at26dOnToIEnatm2boqOjtWLFipKOEZVIXhnfwld2XCzj23RKzTrXsHVYJaJj3Wqq4eOqk/FpWrU3Vn3DK8d1AQAAAABQGoq1UurWW2/V33//rX79+ik+Pl7x8fHq37+//vzzT3311VclHyUqlcpaxmdnZ1hWSy2g4TkAAAAAAIUqVlJKkoKDg/Xaa69p6dKlWrp0qV599VVduHBBn332WclGiErp0jK+9V9XnjK+Aa1ryjCkX/85r6PnUmwdDgAAAAAA5Vaxk1LA9TDsDN1278W38Z3Yf7GMrzII9nHVrQ38JRqeAwAAAABQKJJSsBmf6pWzjG9Ibgnf4l0nlJ1jsnU4AAAAAACUSySlYFOVsYzvtkbV5efupLNJGVp/4KytwwEAAAAAoFy6prfv9e/fv9DP4+PjrzceVDF5ZXwLX91hKeOr6G/jc3Kw0z2ta+rjjf9oYWS0ujepbuuQAAAAAAAod65ppZS3t3ehW+3atXXfffcV+XgbN25U7969FRwcLMMwtGzZsqt+Z8OGDWrVqpWcnZ1Vr149zZ07N9+cDz/8UHXq1JGLi4vat2+vHTt2XMtloozlK+OLq/hlfIPaXCzhW3/gjE4npts6HAAAAAAAyp1rWik1Z86cEj15SkqKwsLC9OCDD151FZYkHTlyRHfeeaf+/e9/a968efrpp5/00EMPKSgoSBEREZKkhQsXaty4cZo9e7bat2+vadOmKSIiQgcOHFBAQECJxo+S07xrTR3+7YxiDido/Vf71eeJljIMw9ZhFVu9AA+1qX2Ddh67oCW7TuixrvVsHRIAAAAAAOWKTXtK9ezZU6+++qr69etXpPmzZ89WaGio3n33XTVu3FhjxozRgAED9P7771vmvPfeexo1apQeeOABNWnSRLNnz5abm5s+//zzUrwSXC87O0O33ddY9rlv4/trc8V/G9/g3Ibni3ZGV4peWQAAAAAAlKQK1eh827Zt6tatm9VYRESEtm3bJknKzMzUrl27rObY2dmpW7duljkov3yqu+mmu2+UJG1ZUvHL+O5sESQPZwcdi0vVr/+ct3U4AAAAAACUK9dUvmdrsbGxql7duml09erVlZiYqLS0NF24cEE5OTlXnLN///4Cj5uRkaGMjAzLfmJioiQpKytLWVlZJX4dZSUv9op0DY07BerQb2d0+p9E/fzlPvV6rFmFLeNzNKQ7mwdq4c4T+mb7MbWp5WXrkFDGKuIzCFQWPH+A7fD8AbbD84fyoqi/wQqVlCotb7zxhqZMmZJvfM2aNXJzc7NJTCVp7dq1tg7h2tQ0pKPuOnkgXov/b508alXcv1BrZEiSg1b+cUodnKPlxhNXJVW4ZxCoRHj+ANvh+QNsh+cPtpaamlqkeRXqP5EDAwN1+vRpq7HTp0/Ly8tLrq6usre3l729/RXnBAYGFnjcCRMmaNy4cZb9xMREhYSEqEePHvLyqrirW7KysrR27Vp1795djo6Otg7nmuzxO6lfv/tHKYfc1XNQK3n6utg6pGIxm81acWab9p9OVnr1ZhrQvpatQ0IZqsjPIFDR8fwBtsPzB9gOzx/Ki7wKtKupUEmpDh06aMWKFVZja9euVYcOHSRJTk5Oat26tX766Sf17dtXkmQymfTTTz9pzJgxBR7X2dlZzs7O+cYdHR0rxYNcEa8jvHttHdsTp5jDCdq84JB6j624b+Mb3K6Wpvz3Ly3edUoP3FLX1uHABiriMwhUFjx/gO3w/AG2w/MHWyvq78+mjc6Tk5MVFRWlqKgoSdKRI0cUFRWl48ePS7krmO677z7L/H//+9/6559/9Oyzz2r//v366KOPtGjRIv3nP/+xzBk3bpw++eQTffHFF9q3b58eeeQRpaSk6IEHHrDBFaK4Ln0bX/S+iv02vn7hNeTkYKe/YhK192SCrcMBAAAAAKBcsGlSaufOnQoPD1d4eLiUm1AKDw/XpEmTJEkxMTGWBJUkhYaG6scff9TatWsVFhamd999V59++qkiIiIscwYPHqx33nlHkyZNUsuWLRUVFaVVq1bla36O8q+yvI3Px81JdzS9WD66IPL4VecDAAAAAFAV2LR8r0uXLjKbzQV+Pnfu3Ct+Z/fu3YUed8yYMYWW66HiaHFbiP7ZfVYxhxO04ev9FbaMb3DbEC3//ZS+331KL/RqIlcne1uHBAAAAACATdl0pRRwNZWljK/DjX4K8XVVUka2Vu6NsXU4AAAAAADYHEkplHtWZXxLDynpfLqtQ7pmdnaGBrcJkSQtiIy2dTgAAAAAANgcSSlUCC1uC1Hgjd7KSs/R+q/2FVr2WV4NaB0iO0PaceS8/jmbbOtwAAAAAACwKZJSqBDs7Azdfn/FLuML9HZRl4YBkqRFO0/YOhwAAAAAAGyKpBQqjMpQxje47cUSviW7Tigrx2TrcAAAAAAAsBmSUqhQKnoZ322NAlTNw1nnkjP08/4ztg4HAAAAAACbISmFCuXi2/gaWcr49m2pWG+yc7S30z2ta0iSFtHwHAAAAABQhZGUQoVzQ6C72ve5WMa3ecnBClfGl/cWvvUHzig2oWLFDgAAAABASSEphQop7PYQBd7odbGM7+v9FaqM70Z/D7Wr4yuTWVqyi9VSAAAAAICqiaQUKqSLZXy5b+P763yFK+PLa3i+aOcJmUwVJ6EGAAAAAEBJISmFCqsil/H1ah4kT2cHHT+fql//ibN1OAAAAAAAlDmSUqjQLi3j21CByvhcnezVp2WwJGkBDc8BAAAAAFUQSSlUaJYyPgc7Hf/rvPZtrThlfEPa1pIkrfozVvGpmbYOBwAAAACAMkVSChXepWV8WxZXnDK+ZjW81CTIS5nZJi3bfdLW4QAAAAAAUKZISqFSCOsWouqhXsqsQGV8hmFYGp4viIyuEDEDAAAAAFBSSEqhUrCzM3T7/RWvjK9vyxpycrDT/tgk/XEywdbhAAAAAABQZkhKodKoiGV83m6O6tUsUKLhOQAAAACgiiEphUqlIpbxDcot4VsedUqpmdm2DgcAAAAAgDJBUgqVSkUs47sp1E+1/dyUnJGtFX/E2jocAAAAAADKBEkpVDoVrYzPzs7QoDYXV0stjDxu63AAAAAAACgTJKVQKVW0Mr4BrWvK3s5Q5NELOnQm2dbhAAAAAABQ6khKoVKqaGV81b1c1LWhvyRp8U4angMAAAAAKj+SUqi0bgh0V7s+oVIFKeMb3LaWJGnpbyeUmW2ydTgAAAAAAJQqklKo1Fp2q/W/Mr555buMr2tDfwV4OutccqZ+3n/a1uEAAAAAAFCqSEqhUrMq4/uzfJfxOdjb6Z7WNSVJCyMp4QMAAAAAVG4kpVDpXV7Gl3yh/Jbx5b2F75e/z+pUfJqtwwEAAAAAoNSQlEKVcGkZ3/py/Da+0GruuulGX5nM0pJdJ2wdDgAAAAAApYakFKoEOztDt933vzK+/dvKbxnf4LYXV0st2hktk6l8Js8AAAAAALheJKVQZfgGuatd74tlfJsXld8yvp7NguTp4qATF9K09XCcrcMBAAAAAKBUkJRCldKy+6VlfAfKZRmfi6O9+oXXkCQtiDxu63AAAAAAACgVJKVQpViX8cWV2zK+vIbna/48rQspmbYOBwAAAACAEudg6wCAspZXxrftu8PavPiQQhr7yuMGF1uHZaVZDW81q+GlvScTdd/nO9Qw0FPB3i4K8nFVoLeLgr0v/unl4iDDMGwdLgAAAAAA14ykFKqklt1CdHj3WZ05mqj1Xx/QXWNalLvkzn031dGzS/foj5MJ+uNkwhXnuDvZK8jHVUHeLgrydlGgt6sleZU35uniWOaxAwAAAABwNSSlUCXZ2dvp9vsba9FrkbllfLFqfHOQrcOyMrBNTdUN8NDRcymKTUzXqfg0xSSk525pik/NUkpmjg6dSdahM8kFHsfD2SE3YXVxhVWQj0tuwio3ceXjKg9n/ioAAAAAAJQt/ksUVZZ1Gd/B3DI+Z1uHZWEYhlrXvkGta99wxc/TMnMUk3BJoio+TTGJuX/mjiWkZSk5I1sHzyTrYCGJK08XB+tElSVh9b9/didxBQAAAAAoQfxXJqq0S8v4NszbrzsfK39lfAVxdbLXjf4eutHfo8A5qZnZuQmr9EsSWGlWY4np2UpKz1ZSerL+Pl1w4srLxeFigsrn0uSVyyVjLnJz4q8UAAAAAEDR8F+QqNIuLeM7trd8lvFdDzcnB9X191DdQhJXKRnZ/0tWxadbJ65y/0xKz1ZierYS05N04HRSgcfydnX8X7LKx1VBXhf/DM4tHwzydpWrk30pXS0AAAAAoCIhKYUqr7yX8ZU2d2cH1QvwUL2AghNXSelZik1Iz7fS6lRCmmU8OSNbCWlZSkjL0v7YghNXPm6OCvL+X6Iq2MdVgV4XSwXz3iro4kjiCgAAAAAqO5JSQAUv4ysLni6O8nRxVP3qngXOSUrPUkzCxYbssQnpOpWQrtjLel6lZOYoPjVL8alZ2heTWOCxfN2dFOjlomCf/62wCvZxUaDXxT+re5G4AgAAAICKjqQUkFfGd19jLXx9R6Us4ysLeYmrBgUkrsxmsxLTs3MTVrkrrOLTcpNXF8di4tOVlpWj8ymZOp+Sqb8KSVz5uTsp6JJEleXtgrlJrOreznJ2IHEFAAAAAOUVSSkgl2+wu9r3vrHKlvGVNsMw5O3qKG9XRzUMLCRxlZatmMQ0q/LAU/Hpir1kLD3LpLiUTMWlZGrvyYITV9U8nBSUWxIYnNfn6pK3C1b3cpGTg10pXjUAAAAAoCAkpYBLUMZnW4ZhyNvNUd5ujmoU6HXFOWazWQlpWZZE1al465VWsYkXSwgzsk06l5ypc8mZ+uNkQgHnk6p5OFu/SdAqeXUxceVoT+IKAAAAAEoaSSngEpeX8R34NVaNOlDGV54YhiEfNyf5uDmpSXDBiav41Kz/rbTKLRW8tHTwVEK6MrNNOpuUobNJGdpzouDElb8lceWqIJ/8Cazqns5yIHEFAAAAANeEpBRwGd9gd7W7K1S/LvtHmxYdVM1GlPFVNIZh6AZ3J93g7qSmwd5XnGM2m3U+JdPSiD02Ic2SvPrfWLoyc0w6k5ShM0kZ+r2AxJWdIfl7Ov8vUZX7Z4CHow4nSv+cTVGgj7u8XB1YeQcAAAAAuUhKAVcQ3r2W/tl9VmeOJWnD/P2681HK+CobwzDk5+EsPw9nNatRcOIqLiUzt6/VJW8StLxV8OKqq6wcs04nZuh0Yoaioi8/ioNm/LlFkuRob8jP3VnVPJ3k5+4sPw8n+Xtc/LNabix+7k7y93SWr7sTZYMAAAAAKjWSUsAV2Nnb6fb7m1ws4/uDMr6qyjAMVfNwVrVCElcm08XElSVRFZ+mmMT0i03Z41N17PQFpctRSenZysoxKzbxYt+rovBxc5Sfu5P8PJwvS15dTGr55ya3qnk6y93JnsQpAAAAgAqFpBRQAMr4UBR2dob8PZ3l7+msFjWtP8vKytKKFSvUq1eETIad4pIzFZecqXPJGTqXnKG4lEydS8r9MzlD55IzFZc7nmO62BcrPjVLh8+mXDUOZwe73ASak2XFVTXP/628yluZVc3DWTe4OdIDCwAAAIDNkZQCCkEZH0qKs4O9gn1cFezjetW5JtPFNwxaElUpGVdMXuX9mZKZo4xsk07Gp+lkfNpVj28Ykq+bk2XFlXXy6mJSq1puAquah7NcnexL6C4AAAAAwP+QlAIKYWdvp9vub6xFr0deLOPbHqtGN1HGh9JlZ/e/Ru31q199flpmzmUrry4mrKxWX+Xun0/NlNksxaVkKi4lU1LyVY/v5mT/v9LBS8oGLy0n9M/tieXj6ig7OxK3AAAAAK6OpFRllpUmmU22jqLC8wv2sJTxbV50UCGNfOXuQxkfyg9XJ3uF+LopxNftqnNzTBffOhiXknFJKeHFPy9NXuWNZWSblJqZo9TzaYo+f/VVWPZ2hnzdna6w8sq6sXteiaGLI6uwAAAAgKqKpFQlZvfrh7rr93dld+JGya+e5Ff34uZb9+K+Z+DFOh5c1aVlfOvnUcaHisv+kh5YV2M2m5WSmZNbKnhp8up/5YOX9seKT81Sjsmss0kZOpuUof2xSVc9h6ezg6V88NK3EPpf1hurmruzvFwdeO4AAACASqRcJKU+/PBDvf3224qNjVVYWJg++OADtWvX7opzu3Tpol9++SXfeK9evfTjjz9KkkaMGKEvvvjC6vOIiAitWrWqlK6gfDIuHJGdOUs6d+DidjlHd8n3RskvN2nle0nSyr0aCatLUMaHqsgwDHk4O8jD2UG1/dyvOj8rx6Tzlzdtv3w11iUrtLJyzErKyFZSRraOnLt6M3dHe6OAskHr3lh5nznSzB0AAAAo12yelFq4cKHGjRun2bNnq3379po2bZoiIiJ04MABBQQE5Jv/7bffKjMz07IfFxensLAwDRw40GreHXfcoTlz5lj2nZ2rXrlVzl3T9VNOW3VtUUsOCcekuEPS+cNS3GEp/riUlSKd/uPidjln74vJqrxVVZYVVjdKrjfY4nJsjjI+oHCO9naq7uWi6l4uV51rNpuVmJ592cqry1ZjXdIbKyk9W1k5ZsUmpis2Mb1I8Xi7OlreRljtkp5Y1XJ7YlW7JLnl4cwqLAAAAKCs2Twp9d5772nUqFF64IEHJEmzZ8/Wjz/+qM8//1zPPfdcvvm+vr5W+wsWLJCbm1u+pJSzs7MCAwNLOfpyzs5Bqc4BMte9TXJ0tP4sO1OKP3YxQXX+8MWEVdxh6fw/UsIJKSNBOrX74nY5N7//raqyJKty/3T2KLPLswWrt/HN269elPEBxWIYhrxdHeXt6qi6/lefn56VY1mFFZd8+VsI895MmNvMPSVTOblvMExIy9Lhs1dfheXsYGdJUFW7pGzQ38NZgd4uuck2ZwV4usjJgRVYAAAAQEmwaVIqMzNTu3bt0oQJEyxjdnZ26tatm7Zt21akY3z22WcaMmSI3N2tS0s2bNiggIAA3XDDDbrtttv06quvys/P74rHyMjIUEZGhmU/MTFRkpSVlaWsrKxiXp3t5cV+5WswJO86/9/encfJUdf543/V3edM5sidEMJhBOQOIIfHEpJAdt1lf7oeyy6BRSJCVMyuCOxyCQiCIgpIgOX8Kou6nqsQCFEOAQFBDpEz4QiEJHNkZvqs+/dHVXdXd1dP95w9x+vpox5d9enqmupIJzOveX/eH2/bY1nFC3PArrcg9G6BsGszhJ7NwK4t3nF6B5Dt8bZ3n6q6qpuYDbd9D6BtD7gde8Jt29M/3h1QomP1VsfVR0/aGz+/6s9468Ue/PXx9/CBwxtYHo2mpcE/gzQUEoCZcRkz4zIwe/CphI7joi9noidj+EGW4YdWXmDVkzbQ7T/2ZAxkDRu65eC9vhze66vfzL09rmB20gupZrdomJ2MYFbFfntMYWDdZPz8ETUPP39EzcPPH00Ujf43KLiu64753dSwbds2zJ8/H48//jiOPPLI4vg555yDhx9+GE8++eSgr3/qqadwxBFH4MknnyzrQVWonlq8eDE2b96M888/H4lEAk888QQkqXqlp4svvhiXXHJJ1fjdd9+NWKz+albTiWznENd3IK7vQELfgbj+vv+4A5pVu6mxCwE5pR0ZbTbSkTlIa3OQ0WYjo81BRp0JV2x60d6QDGxWMfCaBkF2MecjGUiRpn2MiGiEdBtIm0DKBNKmgJS/nzIFDJhAvyGg3wD6DcB2GwuaJMFFqwp/c9Gq+I/BMRXQuPggEREREU1B2WwW//zP/4z+/n60tLTUPG9Sh1Jf+MIX8MQTT+CFF14Y9LwtW7Zgzz33xIMPPohly5ZVPR9WKbVw4UJ0d3cP+oc30ZmmifseuA8nrDgBSuX0vbGQ64PQuwXYtdmrqur1pgMKvVsg6AM1X+YKEjBjN7h+dRXa9oDb7m1oXQiIE++nNsd28atrnkPXO2ns9qF2rFyzL6siqIppmti4cSOWL18+Pp9BGlOu66I3a2LngI4dqbz36O/v8Pd3pryphI1KRmTMSvpVVi0RzPb3vTGvGqszrkJm0/Yh4+ePqHn4+SNqHn7+aKIYGBhAZ2dn3VCqqeUpnZ2dkCQJO3bsKBvfsWNH3X5QmUwG99xzD77xjW/U/Tp77LEHOjs78cYbb4SGUpqmhTZCVxRlUn+Qn9n5DK4ZuAZ7DeyFA+ccOPZfUJkJtMwEdj+ifNx1gUx3qcl6sYfVFqB3MwQzC+x6E8KuN4Etm8pfK6ne1L+wHlbJeYDYpB/UFGDZ6n3xkyuexjt/6cWbz/ZgCVfjoxom+98lVDJHVTFnxuDTBw3Lwc5iUJXHDr85+47+0tj2gTyyho1U3kIqbw3a90oUgE6/t9WsZARzWr1pgrNbI5jjN5af0xJBS5TN2sPw80fUPPz8ETUPP3/UbI3+99fUUEpVVRx66KHYtGkTTjzxRACA4zjYtGkT1q5dO+hrf/rTn0LXdfzLv/xL3a/z7rvvoqenB3PnTq/Q4M6/3okBdwBrH1qLO46/A3vO2LM5NyIIQGKmt+324fLnXBdIvR8IqwLBVe+bgK0D3a95WyU5CrTv4a0I2LFXecP1xCzv646hjvkJHPa3i/Hkr7bg0Z+8jgX7tCPeytX4iKY7VRaxoC2GBW2DT/9O5U0/tNKxvT/vVVz1+wGWH17tTOmwHRc7U14VFtBf83oRpbT6oRdUaaX91kix31VEmXjVp0REREQ0PTW9kc+6deuwevVqLF26FIcffjiuvfZaZDKZ4mp8J598MubPn48rrrii7HW33norTjzxxKrm5el0Gpdccgk++clPYs6cOdi8eTPOOecc7LXXXli5cuW4vrdmu+LoK/C5n38O7+rvYs0Da3DnCXdiQXJBs2+rnCAALfO8bfFHyp9zbG8lwMqwqmezt3KglQN2vuRtldSkF1a17+kFVsEKq1h79fnDdMgKbzW+rndSeOhHr2LVF/dnpQIRNSQZUZCMKNhrVrLmObbjoiejY0d/qcKqVH2lY6c/1pc1kTcdvN2Txds92UG/7oyYUqywmt2iYU5LBLP8aqs5rV5w1RnXIIr8u4yIiIiIxlbTQ6nPfOYz6OrqwoUXXojt27fjoIMOwoYNGzB7trei2TvvvAOxYorWq6++ij/84Q944IEHqq4nSRJeeOEF3Hnnnejr68O8efOwYsUKXHrppaFT9KayuBLHyfGT8WPhx9jcvxmnP3A67jrhLsyMNbD++kQgSkDbIm/b89jy52wT6HunYjqgv9+3FTBSwPvPe1ulaFtgOuBefrWVH1pFhtZDTJRELFu9D35yxdN464VuvPbkdk7jI6JRI4kCZiW9aXv7o7XmeXnTxs4BvTy06s9jR0r3pg2mvGPdctCXNdGXNfHK9tqLU8iigJl+X6s5fng126+2mtNaCrSSEU4LICIiIqLha3ooBQBr166tOV3voYceqhpbsmQJavVnj0ajuP/++0f9HiermBjDDz7+A5z24Gl4N/0u1mxcg9tX3o4ZkRnNvrWRkZRSj6lKZh7Y9VagwuoNoHeLt5/aBuR2Ae/9ydsqxWeVV1UV9tv3ANTwqTicxkdEzRZRJOzWEcNuHbWnDLqui/6c6U0XLIRXFdMFdwzk0ZXWYTku3u/P4/3+/KBfN65KZWHVLL/yqlh91RrBzIQGVWajdiIiIiKqNiFCKRpbM2MzccuKW7D6vtV4o+8NnLnpTNyy4hbElcGb9U5aSgSY9UFvq2RkSgFV5bTATBeQ2elt7zxR/dqW+aWqqmAPq7bdOY2PiCY8QRAwI6ZiRkzFkjm1pwxatoOutF7sdbXTr7LaPpAvVWP155HSLWQMG1u6MtgySKN2AOhMqGX9rgrTBoOBVltM4d+bRERERNMMQ6lpYkFyAW5ecTNO2XAKXux+EV/53Vdww3E3QJOmWUWPGgfm7O9tlfL9fkC1JVBh5T/m+4GB97ztrUfLXyeIEFsXYln7YfjJ1s950/h+vQlLjlkMzFgESPyYEdHkIUsi5rZGMbc1CiysfV5Gt4qN2iv7XQUDLdN20Z020J028NK2gZrXUyWxWGlVFl61Bpu3RxBV2aidiIiIaKrgT8vTyJ4z9sT649bjtAdOw5Pbn8TXHv4avvPx70AR2RMEABBpBeYf4m1BrgtkeyumA24uBVhGGuh7Gx14G4fFgSfTJ+HRDRks+NMyxJUU0LZ7oOH6HqUKq5YFgMgpLUQ0OcU1GXvMTGCPmYma5ziOi11Zo6zKKlh9VQi0ejIGDNvBu7tyeHdXbtCvm4zIpabsyQjmtGplodXslgg6E+oYvGMiIiIiGm0MpaaZ/Tr3w3XHXocvPvhF/H7r73HhYxfi8mMuhygwHKlJEIB4h7ctPLz8OdcF0juKUwAP6dqMLRu70JWeiYdSZ2FV62UQet7wgqzXK3qdyRGgbXF576rC1MDEbO/rEhFNYqIooCOhoSOhYb95tc/TLRtdKb/iql8PVFyVel5t788jZ9pI5S2k8mm8vjNd++sKwMyEBsWR8LPuZzAzGUVnQkVHQkVHXENHQkVnQkNnQkN7XGXPKyIiIqImYSg1DR025zBc8/Fr8JXffQW/2fIbJJQEzj/ifPbyGA5BAJJzvG33oyECWPahNH7yzafxVv5QvPYPj2PJoq7y3lU9m71G7FYe6HrZ2yqpCaB9sRdUxWd6KwZGZ/iPbUBkRvmYPM2mYRLRlKLJEha0xbCgbfBG7Sndwk4/uCqfLpjH9gFvpcGutA7bcbEjpQMQ8O7rPXW/fktERmdCCwmtVC9Ui3uPnQkVrVH2viIiIiIaLQylpqmPLvgoLj/mcpz76Lm459V7kFST+PIhX272bU0JxdX4fr0Fj/5fDxZcdCTie3y8/CTbAvrfAXq2lPpWFUKrvne8KYHbX/S2Riix6qAqOqN6LBIItqIzAK2VUwiJaFIQBAEtEQUtEQV7zardqN12XPSkdbzbm8aGhx7H4n0OQF/ORk9aR0/GQHdaR3faKB7bjouBvIWBvIUt3YM3bAcAWRSqwqtCaNWRUDGzEG754xGFPbCIiIiIamEoNY2t2mMV0mYal/7xUtzy4i1Iqkmc+qFTm31bU8LBK3fDlucGWY1Pkr2V/Nr3AHBc+Ystw6uk6t0M9L4J5HqB3C5/6/Me832lY7iAmfW21LYh3qng9dIKBlVh4VXYmBIdlT8rIqLRJIkCZrVE0BaVsLXNxapD5kNRwnsnOo6LgbzpN2LX0ZM20JMJhFaFcT/MSuUtWI7r98LSG7qfhCZXhVedCTUkyNIwI6pAFFmFRURERNMHQ6lp7tNLPo20mcZ3n/kurnnmGiTVJD71gU81+7YmPUkSsWz1Pt40vhe68dpTO7DkiDmNvVhWgZkf8LZ6HAfQByqCqhrhVeWYmfUCrXyft+16c2hvUo6EVGeFTC2MzgAiwXCrFRBZOUBEzSeKAmbEVMyIqdhrVu2G7QW6ZaM3YxTDqmDFVTDUKjxv2i7SuoW0buHtnmz9+xGA9rhW1v+qMK2ws7I6K6EipvLbOCIiIprc+N0M4d8+9G9IGSn894v/jW888Q0klASOX3x8s29r0vOm8e2OJ3/9Jh798WtY8ME2xFtHufeTKPphz4yhv9bSa4RXu8LHgseu7fXESm/3tqGKtDYWaFVVZ8XYAJ6ImkaTJcxtjWJua/1KUdf1pgUWQqseP8QKrcjKGOjLmnBc+GFXY1VYMVUKhFfVoVWwT1ZbTIEscbo2ERERTSwMpQgA8OWDv4yUkcKPX/0xznv0PMSUGD664KPNvq1J7+CVi7Dlue7a0/iaSdaA5GxvGwrXBfRUg9VZ/mNhzPBXy8r3e1vf20P72pLa2NTCyrFIqzdlkohonAiCgNaogtaogj1m1j/ftB30hlZcFcYK4ZaBrrQOw3KQNWxke3PY2ptr4H6AtlhIxVVgGmGwuXtclSbOv1dEREQ0ZfGnNAL8b57PP+J8pIwU7n3zXqx7aB3WH7ceS+csbfatTWojmsY3UQkCEGnxNiwa2mtts3Z4NWjAtQtwLMA2gMxObxsqrcUPqmpVY9Wo2FITrM4iojGnSCJmt0QwuyVS91zXdZExbL/6qlBxZVQ0cy+EWwZ2ZQ24LtCbMdCbMQCk634NTRYDlVaFqqvyqYWFflhtcRUKq7CIiIhoGBhKUZEoiLjsmMuQNbN46N2HsPZ3a3HryluxX8d+zb61SW1cpvFNFpICJGZ621C4LmBkhlCdVRjr83puAd6jPuCtejgUotxYeBU2RkQ0BgRBQEKTkdBkLOqI1z3fsh3syppl/a6CzdwLUwkLYVbedKBbDt7ry+G9vvpVWAAwI6ZUNHKv6IcVqMpqiciswiIiIiKAoRRVUkQFV3/sapy56Uw8vf1pnLHxDNx5/J3YY8Yezb61SS04je/hu1/FCWdMoGl8k4EgAFrC27BwaK+1LW+q4HCqs2zDq9DKdHnbEMlqHMuEBKSe9UDrAqBlLpCcB7T4W3IukJjNqYVENKZkScTMpIaZycZ+IZI1rNLKgxWhVeXUwt6MDscF+rIm+rImNndl6l5flUSv2qrGVMLOQD+s9rgKTebiGERERFMVfxKiKhE5guuOvQ6fv//z+EvPX3D6xtNx1wl3YX5ifrNvbdIKTuN78/luvP70Dnzg8Ek+jW+ykGQg3uFtQ+G6gJkbRnXWLiA/AMCFYGSQQAZ4Z0ftryOIXjCVnFseVhX353lhllq/GoKIaDTEVBmxdhkL22N1z7UdF31Zo3wFwrJphEZZP6y0bsGwHbzfn8f7/fmG7icZkQOhVSG4qmzu7u23RhWIIn/pQ0RENFkwlKJQcSWOG4+7EadsOAWb+zfj9AdOx53H34mZsSFOu6Ki4DS+R378GuYvmcbT+CYDQQDUmLe1DjGQdWwg3w8ztRNPPvgrfHi/RZAzO4GBbUBqGzDwvref3u5VYqXe97Ztz9a+ptbqB1XBaqu5pdCqZT4Q62D/KyIaV5Io+I3SNXxgdrLu+XnT9gKrlB5YgbA6yOpO6+jNGLAdF6m8hVTewpvd9auwJFFAe7zUB6s4dTChotMPsApTCTsTGqIqq7CIiIiaiaEU1TQjMgM3r7gZJ993MramtmLNxjW44/g70Kq1NvvWJi1O45smRAmItQNKEj2JD8LdbxWgKNXnOY43LTC1zQupBrZ54VTlvpEG9H6gqx/oern215VUIDnHC6jCKq+Sc71NVsf07RMR1RJRJMyfEcX8GdG65zqOi4G8WVFtVWsqoY6BvAXbcdGV0tGV0gGk6n6NmCoVpxF2hjRyLzV719AWUyCzoTsREdGoYihFg5oVm4VbVtyC1fetxht9b+DMB8/ELStuQUypX9JP1SRJxLEn74OfXsFpfARAFIHkbG+bd3Dt8/IDFWFVoNqqsJ/Z6fXA6nvH2wYTn+kHVfMrqq0C0wW1FlZdEVFTiaKAGTEVM2Iq9pqVqHu+YTnoLUwjzJQauXdndHSnSgFWT1pHd8aAYTnIGjayvTls7a3f0F0QgLZYcDXC8l5YwWmEHQkVCY0N3YmIiOphKEV1LUwuxM3Lb8Yp95+CF7pfwJd//2XcsOwGaBKnng1H54IElq7aHU/9H6fxUYMiLd42c0ntcyzDmw448P7glVe2UWrcvv2F2tdTE35wFdKcvbAfn+lVhRERTQCqLGJOawRzWiN1z3VdF2ndKmvkHpxG2JXWA6sTGtiVNeC6QG/GQG/GwOs70w3dT2dcRWeyIrgKmUbYHlehyqzCIiKi6YehFDVkr7a9sP649Tjt/tPw5PtP4msPfw3XfPwayCL/ExqOQ45fhC3PdaF7a5rT+Gh0yCowYzdvq8V1gWxPSFhVUXmV7/emDPa87m21CFIguAppzl4IsZT603SIiMaTIAhIRhQkIwp276y/kIRlO9iVNcumC1ZOH+wOHGcNG4blYFt/HtsabOjeEpHRmdQCoVVhWmEpwCqsTtgaVfh9AxERTQlMFKhhH+r8EK5fdj3O2HgGfr/197jwsQtx2TGXQRT4m72h8lbj25fT+Gh8CQIQ7/S2uQfUPs/IBCqualRepXcArg0MvOttg4m2hTRnr6i8irZxuiARTViyJGJmUsPMZGOVzVnDKlZZBacRBgOtwjTDQkP3gbyFgbyFLV31G7rLolDd+6rGNMLOhIaIwqpWIiKamBhK0ZAcNucwXPPxa3D278/G/235PyTUBM47/Dz+tm4YOI2PJiw1DnTu5W212JbXx2qwBu0D2wArB+R2edvOl2pfT46EN2cPVl4l5gAS/9kiookvpsqItctY2F6/B6fjuOjPmeXTCIv7eqAqywuyUnkLluNix4COHQN6Q/eT0GQ/xCpVW3UEphF2BsZnxFRIIr+vIyKi8cHv7mnIPrbwY7j8mMtx7qPn4n9e+R8k1SS+dPCXmn1bkxKn8dGkJcml0KgW1wXyfdVN2YuVV/5+tgew8sCuN72tFkEE4rMqqq1CKq+0+g2RiYgmClEU0BZX0RZXsdes+ufrlo3ejFFjGmGhAqvQ1N2AYTtI6xbSuoW3e7L170cA2uPloZXX+yqwH5hmGFMlfu9CRETDxlCKhmXVHquQNtO49I+X4uYXbkZSSeKUD53S7NuadIrT+L7pT+P70w584DBO46MpQhC8aXnRNmD2vrXPM/NedVWtaqvCc47lNXNPbwfw59rX01pCqq0qKq9iHd7qh0REk4wmS5jbGsXc1vr9+lzXRarQ0L2i71VhFcLuVGm1wl1ZE44LP9wygB317yeiiFW9r9piMnZuE2A+/z7mtMbQmfSmEbaxCouIiCowlKJh+/SST2PAGMD3nv0evvPMd5BQE/jUBz7V7NuadDoXJLD0b/1pfPe8hvkf4DQ+mmaUCNC+2NtqcRxvxcBiU/b3/OCqovLKSAH6ANA1AHS9Uvt6ohIIqsIqr/zm7TI/i0Q0eQmCgJaIgpaIgsUNNnTvzRrFKqvgNMLCcVdgWmHedJA3HbzXl8N7fbmKq0n45dsvlo14VVhegNWZCDwmtbLjmUlvRUJF4i8PiIimOoZSNCKf3//zSBkp3PaX2/CNJ76BhJrA8bsf3+zbmnQ4jY+oDlEEkrO9bd7Btc/LD5SqrGpVXmW6AMcE+t/xtsGoSa/HlpYA1ASgJf3HhDdeNZYo3w8+p8TYzJ2IJjRZEjErGcGsZKSh8wsN3YPTCLvTBroGcnjhtbegtXb6vbAM7MoafhWWF2gBqbrXb4spXhP3YojlBVadgeNO/1iT2cydiGgyYihFI3b2IWcjZaTw09d+ivMePQ9xOY6PLPhIs29rUvGm8e2Dn37zT5zGRzQSkRZvm7mk9jm2CaS2D97namAbYBte5ZWRAtKjcXNCRWAVFmIlvCCs+Fy8OvTS/KCMIRcRNVmthu6maeJebMGqVUuhKApQqMLKGOgKrD7Y7U8p7E7p6Crsp/XiioS7siZ2ZU28vrP+vSQjMmb6QVVHWWgVCLT845jKH4GIiCYK/o1MIyYIAv7ziP9E2kzjvjfvw7qH1mH98vU4dPahzb61SaVzQbJsGt+CJe2ItajNvi2iqUdSgBkLva0W1wWyvV6jdiMN6Gn/MRU4zvjTBdPl55Sd7z/C9bZCyDUaBLE61BosxKoXeilRhlxENGZkScSslghmtdSvwnIcF7uypcbt3YHAqjtVcZzWYdouUnkLqbyFLd2ZutePqVJ1BVZCDUwj9KuxkhqSmszqdSKiMcRQikaFJEq4/JjLkTEzeOTdR7B201rcuvJW7NsxSHNjqlI5je/4L3yI3wgRNYMgAPEObxspxwHMrB9YZSqCrULQlQkJvWqcb/hlW67j9c/SBxqZBVOfIAYCqwZCrHqhlxxhyEVEwyKKgtc0PaFhCZKDnuu6LgZyll9ppRenEhb2u1LBYMvrg5U1bLzTm8U7vfVXI1Rl0a/A8hq5h00dLFRotUYViGzkTkQ0JAylaNQoooLvfOw7+OKDX8SfdvwJZ2w8A3eccAf2aN2j2bc2aQSn8W15rgtv/Gkn9j5sdrNvi4hGQhS9kEZLjM71HAcwM35glW6gWisVCLhCzjf9qgLXAfR+bxsNghQ+XXGwyq3B+nLJGkMuIqoiCAJaYwpaYwr2mjX437Ou6yJj2IFqq/LG7d2p8sqstG7BsGo1cq8mi0JZ9VVHILAKTiPsTHiN3LkSIRERQykaZRE5guuOvQ6ff+DzeKnnJZz+wOm464S7MD8xv9m3Nml0Lkji0FW74+nfvImH73kViTYNifYIYkkVksJVaIimPVH0ghotiToFBI0phFwNT1FMBQKukOou0688cO3RDblE2Q+z6oVYXtAlSBHM7dsMYWu71yA/3glEZnh/fkQ0LQmCgIQmI6HJ2L2B1Qjzpo2uiumCPf5+V8VUwv6cCctxsWNAx44Bve61vZUIywOsyqmDwR5ZXImQiKYqhlI06hJqAjcedyNO3XAqNvdvxpoH1uDOE+5EZ7Sz2bc2aRx6wiK8+bw3je/n3362OK5GZcRaVMRaVESTqr+vFPejLSpi/r6schUaImpAMOQaDY4dmI4YrMyqNUWxsgdXxfmFkMuxgHy/tzVABnA4ALz5/dKgIAGxdiDWAcQ6vemZxf1Of78jsN8JyOztRzRdRRQJC9tjVY3cwxiW460+6FdbdYVMJSw811tcidDrm9XIHOwZ/kqEhWmEM2tMJexMaIgo/B6QiCYPhlI0Jtoibbhp+U1YvWE13km9gzUb1+D2lbejVWtt9q1NCpIkYsVp++GRe17Dru1Z5FIGHNuFkbNg5Cz07ajfA0GJSF5A1eoFVdGqMKu0r2j85oWIRokolVZBHA2OHd48virEKu/B5eRT6Hv/TbRpDoRsj9d7y7WBTJe3NUpr8YOsQYKreGfpHC3JaYZE05Aqi5jbGsXc1mjdcy3bQW/WqJgu6AVYXYEVCbvTOnr8lQj7sib6sibeaGQlQk0uC6nKq7A0zAxMJYxr/HGQiJqLfwvRmJkdn41bVtyC1fetxuu7XseZm87ELctvQUyp/9smAtrmxPEPZx8M+D0Q9KyF7ICB3ICBbMoo7fvHwX3HcmHmbfTnc+jvaqAHgiYhllRKQVWrVjpuKQ+1FE1i83UiGj+iBERavW0IbNPEo/fei1WrVnlL0lu6t6JithvIdAPZHm8r7ncDmZ7SfranvJn8rrca+8KS6oVTMb9RfnG/MtAqHLd775GIpg1ZEjErGcGsZGMrEfblzOLKg4XQqid0VUIDhu0gpVtI6RbebGAlwqgiVfS7Kg+wvEBLRUecjdyJaGwwlKIxtTC5EDctvwmn3n8qXuh6AV/+/Zdxw7IboElas29tUhEEAZG4gkhcAeYO3gPBdb2KquyAgVzKQHbADOyXtkK4ZZsOLN3GgG5joDtf915kRQytuqqqwmpRoUYYYBHRBCFrQMtcb2uE4wD5vkBw1V0RYgXHe719KwfYBpDa5m0NEYBoW+3gKliFVXheqV+JQURTgygKaI+raI+r+MDsBlYizFtlIVV3ZXgVmEqYM23kTBtbe3PY2lv/l5iSfy8d8VIFVke8PLjqCFRnxVT+qElE9fFvChpze7ftjRuX3YjPP/B5PPn+kzjn4XPwnY9/B7LI//zGgiAI0GIKtJiCtjn1Aywzb4dWWxX2g2GWZTiwTAepnjxSPfUDLEkWEW1Rin2uij2vWqunEWoxmQEWEU0couiHQe1A596NvcbI1A6uMt3VlVr5PgAukOv1tp7XG/s6SryxfliFQCsyg1MKiaYBQRDQGlXQGlWw58z6K75mdKu0CmFgKmFPsam7ge6M99ifM2E7LrpSOrpSekN9sKKK5AVXCQ2dcbW4XxlqdSZUtMXZzJ1oumIqQONi/5n747pjr8MXH/wifrf1d7jo8Ytw6dGXQhT4j08zCYIANSpDjcqYMbuBJp55q1h9VTWNMBhqDRgwdRu25SDdqyPd28AqNLLgTRMMadoebVEQa9GKx1pMhsDycSKaaNS4t83YrbHzbdMPqnoan1boWN5qiX0ZoO+dxr6OKJeCqlh7+fTB0GmF7YCkjOiPgogmvrgmI67JWNRRfyVCw3KwK1sKq3oyhfDKm0rYkzGKKxN2p3XoloOcaePdXTm8u6t+FRb8Zu4dcT/EClRelUItvxIrrqElyl9mEk0VDKVo3Bw+93B8+2Pfxlcf+ip+vfnXSCgJnHv4ufwHZRJRIzLUiIzWmfXPNQ27LKTKVQRY3pg3tdDIWXAsF+ldOtK7GgiwRAHRpFKcRlirkXusRUUkrjDAIqKJSVKA5Gxva4TreqsPVk0fHGRaoZH2gqz0Dm9rVKS1fj+sYM8sNc5qLKIpTJVFzG6JYHZL/T5Yrusia9hllVaF4Cos1OrN6HBcFJu5b+6q3wtLkQpTCQNTBoPBVcV0Qq5ISDRxMZSicfU3u/0NLjvmMpz/6Pm4+5W70aK14KyDzmr2bdEYUFQJSmcULZ0NrEJj2n5gZdadRqhnLTiOi0y/gUy/UffagiggmggJsJIqYi2B8RYNkQQbeBLRBCYIQHSGt3Xs2dhrzFxI9dUg0wpzu7wphfl+b+vd3NjXkSP+lMHKaYWVKxf6+5EZ3hRJIppyBEEoVmHt1lG/Er/QzL1QadUTCLK6/QosL8jyQq1U3oJpu9gxoGPHQP1fZgJAXJWKgVVhymBHSB+sjriG9rgKid8PEo0bhlI07v5uj79D2kjj8icvx/rn1yOhJLB6v9XNvi1qIlmR0NIRRUtH/QDLtpzqpu1VVVje9MJ8xoTruMXzeupcWxCASEIpq7gqn0YY2E8qENn7gIgmOiUKtC7wtkY4thdMlU0fDOmHVZxW2O01d7fywMC73tYIQQSi7YP3w4rOALRWQEsCkRbvUU2wIotoigk2c9+7gcJR3bLRmzHKel+VTScMhlr+ioQZw0amN4t3erN1ry8IQHssJLSqUYmV0DiVkGgkGEpRU3z2g59F2kzje89+D9/+07eRVJP4//b+/5p9WzQJSLKIRFsEibb65eO27SDvTxGsDLDKwqyUgVzahOsCuZSJXMoEUKd0XAAi8fIAy6vCKo2pMRFWVoCRsyBL7INFRJOAKHmBULyzsfNd15siGDp9sKcUXAUrtfQBwHX88e6h3Z8geuGU1uJvgcCq7LjW8y3escxVgIkmK02WMLc1irmt9X+Z6bou0rpVDK68Hli1pxP2Zg24LrweWRkDQLru11Blsazn1WCVWO1xFZrMqYREQQylqGlO+9BpGDAGcPtfbsclT1yCuBLHyt1XNvu2aAqRJBHxGRriM+r/8OHYDnJps6zqKlPVxN2bXphPed+w5NMm8ul6AVYCdzz8BAQBUKMytJjsr44Y2I/K0OKB8Wj5OWpMhsSqLCKaiATBD3ySQPvixl5jGaVqq5rTCv3VCfMDXohVCLJcpzS1cCQktcEQK+n116oKvfxjkT9cEk1kgiAgGVGQjCjYvbN+Q3fbcbErWwquuupUYmUMG4blYFt/Htv6669MDQDJiByovKpu5F4KtTTMiLK1BE19DKWoaQRBwFcP+SpSRgr/+9r/4txHz0VcieOY+cc0+9ZoGhIlEfFWDfHWBgIsx0U+EGAVq676AwFW4TitA44A1wX0rAU9awFo7JuWIEWTyoOsYHgVl6vCLDUmI+Lvyyp/aCKiCURWgZa53tYo1wXMrB9SpUpBVdlxqjzEKh4Hnjf8qgfbGF6lViUlXh5ShVVl1aviUmKckkg0QUiigM6Ehs6EBiBZ9/ycYZdCqgYqsSzHRSpvIZW38GZ3/YbuogC016q8qliRsCOhIsbv+WgSYihFTSUIAv7riP9Cxsjgvrfuw1d//1XctPwmHDL7kGbfGlFNoigUV/frmF/7PNM0ce+992LF8uPhmKVQSs+a1fs5C3omMJ7z9s287V1Lt2HqdkOrE1aSZNEPqfzKq6gXVkViMrS4UqzgisQUqMXgyztWIhL7JBBR8wmCt8KfGgcwhDCrkmOHhFjDCLls/+9iM+NtqfdH8N6kGqFWjamHtUIuWR3+PRDRsERVCQvUGBa01W/o7rouBnJW2YqE5Y3cC6GWF2j1ZU04LtCd1tGdbuz7v4gioiOuQjIl/LznWXQmImiPK2iPeyFWe1xFe0It7rMfFk0EDKWo6SRRwuUfuRwZK4NH3n0EZ206C7euvBX7duzb7FsjGhWyIkKJKQ1VYVVybAdGzka+GGSVB1pGzkI+a0HPVBxnTRhZC67rN4f3pyIOlSDAD6r8aYaxQHVWremIsVLlFpvBE9GEIkqlFQxHwtL9kKq/dogVGnClykMu1wFc25+q2AeMZFaipDU49XCQ59QEpyQSjRFBENAaU9AaU7DnzPrnm7aDXZnyKYPdfmBVCLIKoVZ3WkfedJA3HbzXlwcg4J3X6leCqpJYbDIf3DrKwiutONbK6YQ0BhhK0YSgiAq+87Hv4IwHz8AzO57BGRvPwB0n3IE9Wvdo9q0RNZUoiYgkREQSypBf67ouzHwp0DL8MCvvh1d61qvOymct/zgYeFmwLcebdpjxQq/hUCJSVY+s2kFWsL+WDFnhD0ZENEHJmrc12hA+jOsCRqbBqYeDhFymPwXI1oFMl7eNhJocfgP5wjF/xCAaMUUSMaslglkt9Rf3AYCs4TV0396XwQMPP4HF+xyA/ryN3owXZPX6W0/ae8yZNgzbwfaBPLYPNNZaQhSAtlggvEoU9gOVWIFgqy2uQuEvKKkO/otBE0ZEjuD6Y6/HaQ+chr/2/BVrHliDu064C/MS85p9a0STkiAIUKMy1KgMdAz99ZZhh08zLOxnStMMK88zdX/aYd6GmR/+tMPBQ6wazeKjMqcdEtHEJwiAlvC2kbAtwAjpn1Wvv1awyis/ADimdz0j5W2pbcO+JVmUcYKgQX5rlleRVbX51Wqhz7WyzxbRMMRUGbF2GXOSCra1u1h16HwoSu1fauYMG71ZA71+JVYxtMoUxgz0ZnTsyproSesYyFtwylYmbExLREZHQqsKqzrKAiytWJkV4S8lpx2GUjShJNQE1h+3HqdsOAVb+rfg9AdOx50n3InO6Ah+E0lEwyKrEmRVamj1wkq27XjVV5nyHlk1e2pVBF/wpx0WGskPlSAKgemFtYMtNeo3hC82i/f6arE0nYgmDUkGom3eNhJmPqQyqzLQ6q8RcAWO4UJwLKiwgF1vDu9eRKV2YFUWZtXYVxqrLCGazqKqhPlqFPNnRBs6vzCdsKcsvNKL+4VVCwvh1q6sAccFBvIWBhps7A4AMVUKn07IvlhTFkMpmnDaIm24efnNWL1hNd5JvYMvbPwCblt5G1q11mbfGhE1SJJERBMqoomhN951HReGbnuN33P1gqzqKi7HcuE6LvIZE/mMOaz7VyNSsZdWJF4xxTAmIxKvDrgKjeIZaBHRpKREvC3RQLObWhwHMDMw0714dONv8NHDD4RsZbyqrHy/3zvL388F9oOba3tVWyNZHVHSBgmyQqq2IhXPs2k8UZWhTie0HRf9OdObPpguBVnBYKsYbvmVWqbtImvYyBo5vLsr19DXUSURbZXN3Cv6YrXFClMNNcxgX6wJh6EUTUiz47Nxy/JbcPKGk/Hartdw1qazcPPymxFT6q9sQUSTW7HKKTr0f6Jc14VlOjD83lmlXlqm3wC+/DjYSyuftWD50w6NvA0jbyPdO/Rph2q0vDqrPNQqjIVMR4zKEPhNEhFNZqLo9ZUSI0hFF8BdeAQwyPShKoU+W/kagVVZoFXjHLh+f62d3jYccrTBMCtsrAWQht4HkmiqkUShGBDtNav++a7rIqVbxamDuypCq8H6Yu0Y0LFjoLHv2Wr2xSqMJUrhFvtijQ+GUjRhLWxZiJuW34RTN5yK57uex9m/PxvXL7seqsTfXhFROEEQoKgSlJFMOww0hC9WYmVKVVl5v0F8sFKrLNDKeWFXqmeoN4+qFQ1L4VUpxKoaiytQ2UOLiKaCYJ+t1gVDf73j+P21BguzBgm7dH/5QysHpHNAevvw3oeaGGKYFQjAtBaugEjTkiAIaIkoaIko2L0z3tBrxrsvVlssUJGVYF+s0TIhQqkbbrgBV199NbZv344DDzwQ1113HQ4//PDQc++44w6ceuqpZWOapiGfL60Y4LouLrroItxyyy3o6+vD0UcfjRtvvBF77733mL8XGl0faPsAbjzuRnz+gc/jifefwNcf+Tqu/tjVkMUJ8Z8uEU0xkiQimlQRTQ49/LYtp2w6YT4TMuUwU6jYCjyXMWGZDuCiOAY0tgpOQVUPrbiCSOXKhnG/f1Ys0BQ+JkPRGGgR0RQhiqWQZzgc2++nNYxAK98HGGnvOkba2wbeG959aC2NBVlh1Vxq0vtzIJoGRrsvVm/FVtUXq9H7UqSK1Qmr+2K1BaYYJqd5X6ym/2T/4x//GOvWrcP69etxxBFH4Nprr8XKlSvx6quvYtas8Dq/lpYWvPrqq8Xjyv8Dr7rqKnz/+9/HnXfeicWLF+OCCy7AypUr8de//hWRCBsfTjYHzDwA1x17Hc588Ew8+M6DuPjxi/GNo78BUeA/uEQ0cUiyiFiLiljLMAIt0ylVZmXM8qqsirHCVMPCmG05I+qhJYpCWUhVnGLoh1u1pyIqkFVxWn8TRURTjCiNrGm8bfmhVt/QA618P2BmvesUms33bx3GTQjeFMLBGsEPNjVRTXDlQ5qyRqsvVmV4VdkXK2faeK8vh/f6ht8X6yN7d+Kfli4c4TueHJoeSl1zzTU4/fTTi9VP69evx29/+1vcdtttOPfcc0NfIwgC5syZE/qc67q49tpr8V//9V/4h3/4BwDAXXfdhdmzZ+OXv/wlPvvZz47hu6GxcsTcI3D1x67GuofW4Vebf4WEmsDXD/s6fxgioilBUkTEWzXEW4c+5dAy7NJ0w0x5FVb1WHkVl2O7cBwXuZSJXGoYgZYkBKqyyqcUFpq/B6u3gmOyytJ2IppiJBmItXvbcFhGoFKrb2iBVr4fsPJeT61if613hn4PguRPI0wCShSQI/6j5vXaUiLeo6wN8nwkMF7Yj4Q/z6mKNIGNtC9Wb1lj96H1xWqNKgylxoNhGHjmmWdw3nnnFcdEUcRxxx2HJ554oubr0uk0Fi1aBMdxcMghh+Cb3/wm9ttvPwDAm2++ie3bt+O4444rnt/a2oojjjgCTzzxRGgopes6dL30H8DAwAAAwDRNmObwVm6aCAr3PpnfQ9BH5n4EF3/4YlzwxAX40cs/QlyK44wDzmj2bRHVNNU+gzRBCYAaF6HGNWBm46GW67qwDG/KoZHzgiqjuIph+VY+7gVargM4tovcgIHcQON9GQokWSiGWGohuIr6Uwv9qYhqZdjlnyMp9Stl+fkjah5+/oZLANRWb2vZbegvt/JAfgDQ+yHkSxVbQr6/bFqioPd75+X7Ss/l+iA4prf6Ya7X28aBKyrlAZe/ucUgq3xziwFYKeRypeDrS+GYW/HaYngmKlO6Goyfv+aKSsD8VhXzWxurnM8ZNnZlDfRmTK8/lh9aLZmTnPT/HzZ6/00Npbq7u2HbNmbPnl02Pnv2bLzyyiuhr1myZAluu+02HHDAAejv78e3v/1tHHXUUXjppZewYMECbN++vXiNymsWnqt0xRVX4JJLLqkaf+CBBxCLTf7V3jZu3NjsWxg1AgT8XfTv8Jvcb3DzX27G1je24ujI0c2+LaJBTaXPIE0jIoCEvwFQ/S3pL5DlrdouFDe3uB8Yt4SQcwBAgG25yA4YyA4j0BJEF6LiQlC8R29DYN+FKLsQFAm/+ckmiLL3nCC7EKQp/bMI0YTCf/8mgoi/BX420vwt2HrLdSG6JhQ7C8XOQLFzkBwDomNCcg1IjgnRMSC5Zo3xwn5h3D/2z5f813rPl35QFRwTMMxSP67C+Bj+ibgQYIsqbFGFIyiwRQW24B8X95XA896xI6qw/ePSfmG8cE74tRxh/IMwfv4mJxXAHAD9/cC9rzbwggksm802dF7Tp+8N1ZFHHokjjzyyeHzUUUdhn332wU033YRLL710WNc877zzsG7duuLxwMAAFi5ciBUrVqClpWVU7rsZTNPExo0bsXz5cihDWY53gluFVVj00iLc8PwNuC9/Hw478DCcuOeJzb4toipT9TNINBKu48LU7eom8IVqrIz3aGRDKrbyFuACriPA1gWgsdWfywgioEa9iis1KkONSlXHpf2Q44gEQWSqRTQY/vtHtTgAHNfxqrrMPGDp3mqHVh6CmQfswnjeH9e9cf+c4vlmHoJVOM/fTP/84LGt++eWevsIcCE7OmRnGP+IjIAbUrXlBo8lrTjN0a2o+qquICuvGHPl0mtNSPjdI4/j2BWroKhDbwtANFoKM9DqaWoo1dnZCUmSsGPHjrLxHTt21OwZVUlRFBx88MF44403AKD4uh07dmDu3Lll1zzooINCr6FpGjSt+gOrKMqk/of06V/9FG/99lf45dOPonXWbLR0zkTLzFlIds5ES+cstMycBTXS2EoFE80XDvwCslYWt790Oy576jK0RlqxYvcVzb4tolCT/e8SotGmakB8GL/zcRwXRnAaYcYqNYiv6J+Vyxjo2t6LiBqHkbNg5Gy4jgvXgXdexhrezQuAqkne1MKoP/2wEGrFSuGWVrEfDL4kmQt10PTAf/+oJrWyVGuMua4faAUDrHwgHMsFwrChPK+X9v1QrHRuDnCd4i0Uw7KAsfgVhwLg7wDgefihVhRQYoAaK+2XPUYBJR4y5u+r8eqx4CP7glENjf7939RQSlVVHHroodi0aRNOPNGrdHEcB5s2bcLatWsbuoZt23jxxRexatUqAMDixYsxZ84cbNq0qRhCDQwM4Mknn8QXv/jFMXw3E0//zp2wshm8//oreP/18OmQkUQyEFL5j4HQKtrSOiGbiQuCgK8e+lUMGAP42es/w9cf/TriShxHz+dUPiKiqUoUBUTiCiJxBcDgv1QxTRP33nsvVq36OBRFget6FVpGLlCJ5QdcZWP50nPFAMx/zrYcwAWMvA0jbyM9nFItALIqFkMqL7BSoEUlqDHFD64kr49WSNClRmXIClc9JCIaEkHwKomUcVyJ3XUBxxo8tKoXajX0fEVo5gT6+Fg5bxvLHmGFnl6VgZUaCw+5lFh4uDVYeCYx3J7Kmj59b926dVi9ejWWLl2Kww8/HNdeey0ymUxxNb6TTz4Z8+fPxxVXXAEA+MY3voEPf/jD2GuvvdDX14err74ab7/9Nj7/+c8Dflhx9tln47LLLsPee++NxYsX44ILLsC8efOKwdd0cfRn/xUDWhz7L9kbmV29SHV3YaB7Jwa6dmKgeyf0TAb5dAr5dApdb20JvYasqF5oNbMUVpWOZyHR3gFJbs5/RoIg4IIPX4CMmcGGtzbg7N+fjZuW34RDZh/SlPshIqKJSxAEqBEZakRGYpirvVumDSNnQ8+a3mPOrA61cjWOsxZM3fauYziwDAPZ/qH300Jh1cOK6qvgcXn1llJVvaVonIJIRDTmBMELUyQFwPi1hDH1HB747a+w4tiPQnFNL8wys4HHbMhYDjDCnhvkNQW27m35vrF7U6JcI8xqpKKrwQBMUtl0skmaHkp95jOfQVdXFy688EJs374dBx10EDZs2FBsVP7OO+9AFEtl7rt27cLpp5+O7du3o62tDYceeigef/xx7LvvvsVzzjnnHGQyGaxZswZ9fX045phjsGHDBkQi45iMTwDRZAsinbOw9xFHh5bO6dksUt07MdDd5QVVPV3FwCrV3YX0rl5YpoFd77+HXe+/F/o1BEFEvL29VGE1s7zSqqVzFpQx/HOXRAnfPOabyJgZPPreozhr01m4beVt2KdjnzH7mkREND3JigRZkRBraWxFnUqO7cDI2yFBllkMu4IhVvGcwLHr+qsepkzkUsNclUdAeZgVKZ9uWKjOKg+6FL+/lvcoSpyCSEQ0IYkyLCkKxGcCYzV91nVL1VtGJjzkMnOAWfmcv18WgA0SmhWmPzqWt0qk3liPomERxNGb0lgzCIsy+AohuK7rNvsmJpqBgQG0traiv79/0jc696YurBrWfH7bMpHq6SkLqoKVVqnuLthW/Z4ckUSybHpgsNKqZeYsRJMtI56GkLNyOGPjGXh257Noj7TjjuPvwOLWxSO6JtFIjfQzSETDNxU/f4UpiDWrs4pTDU3oORtGoZIrXwq8HGt0vu2TNakYXAUbwdeu2Cqv6pIV9iCZyqbi549ospgynz/XBWyjPLwaTgBW9rqQ551h/oJnuGpWe1WM7/4R4IB/Gt97G2WN5ipNr5SiiUuSFcyYPQczZoc3nXcdB9mBfj+kqg6sKqcI7nxrc+h1ZFXz+1r5YVVHeWiVaO+AKA3+zWtUjuL6ZdfjtPtPw8u9L2PNxjW46/i7MDcxd9DXERERTRbBKYjDZZnhodZgQZeRLwVeVmEKom7D0m1k+obXV0uSxWLvrFoVW96YBCVSsVpihA3jiYimPEHwVxjUgOgw5903wjZrT1usO6UxLAALeY0d+Ley8Dr0DH5fojzpQ6lGMZSiYRNEEfEZbYjPaMPcvZeEnqNns6WQyg+sClMFU107ke7bBcvQsWvbu9i17d3wryOISLR3lFdaBaYHtnTOhBKJIKkmsX75epyy4RS82f8m1mxcg9uPvx2d0c4x/pMgIiKaHGRFgtwqId46vGXCbduBGdJPqzLUCo4XAq7CGFzAthzkUs7wpyAGgq1CSFW2H/ECLbUyzCo+LxUfORWRiGgakxRAagUiY7gapGMP0p+rxpTGuQeO3f1MMAylaExpsRhm7rY7Zu62e+jzlmki3dNds9KqMEUw1dOFVE8X3sNfQ68TSbYUe1mtaT0Wv+q6H9u278C/963BVZ/4PmZ1zOdKRURERCMkSSKkhIhIYnhTQlzHn4JYFWSZ1dMQc97UQ6Niv9AwfjSCLfhTEdWIVBZYaVEZSlSGFpGh+IGWFpWh+M+pwf0Im8cTEdEgRAnQEt5GVRhKUVPJioIZc+ZixpzwaXau4yDT31fVz2qg26u0Gujugp7NIJ8aQD41gJ1velMEPwgJH8QsAAZ+uOEMyJpWnBZYXmk1s7iKYL0pgkRERDQygigUq5eS7cO7huO4MPPhgZWR9wItM+8FX6b/nDfmh115G2bOgmV6DXQLUxGHuxqi98YAVQtUZhUrscKOazwXlSErIn+JRkRE0wpDKZrQBFFEoq0dibb2QaYIZkorCPrBVaq7Czu3v4Nt77+JaF6Epevo3fYuemtNERT9KYJlqwh6+0m/SbuiTa/VG4mIiCYiURSgxRRosZE18LUtpxheGfmwyiwLRs6ufq5wnPOOHccFXHjP521g1/D6bBXemxKVaoRZMrRooMcW+20REdEUwFCKJj0tFsfM3eKhUwSf73oeX7jvdIhpEx9tOQKfm/ePyPjTBVPdOzHQ5U0LtC0Lqe4upLq78F6NrxNNtniVVmWN2Et9rkZjFUEiIiIaH5I8sqmI8FdEtE0nPLDKlx/rea9yS8/ZfqVXqarLyFlwXa8KTM9Y0DP1Vzeu995C+22FVGex3xYRETUTQyma0g6ceSCuXf59nPngmfit/RhktRPf+NQ3IAqlb7IKUwQr+1kVVxXs2gkjl0UuNYBcagA7trwR+rVkTStVWvnTA4OrCibaOEWQiIhoKhEEAbIqQVYlxFrUYV/Hdb1eW2FhVs1KrSb32wpWcVX22xJkF1ZOgKnbkGWZv7QjIqKaGErRlPfhuR/G1R+7Gv/+0L/jV5t/haSaxDmHnVP8Bik4RXDeBz4Yeg09mynrZ1UIrFL+SoKZXb3eFMH3tqL3va2h1yibIhiYHtjSORNJv78VpwgSERFNP4IgFKuVgOGtjIgJ128rgdsfehyiJECLydBiCtSojEhMhhaTocYUf9wLtbSYAi0e2I95AZfIBvJERFMaQymaFpbttgyXHn0pzv/D+fjhyz9Ei9qCLx70xYZfr8XimLloMWYuWhz6vGWaSPV0FftZDfhTA0uVV11w7MAUwVdeCr1OYYpgYWpgsmMmtHgCajQKNRKFEon6+xFvPxKFpCj8DSQRERFNmH5b3uqKJuAKcGwXuZQ57Kotr5eW7AVWMRlaNBBmBcIuLSYjEg/sxxRICqcfEhFNdAylaNr4xJ6fQMpI4YqnrsAPnv8BEmoC/7rvv47KtWVFQduceWibMy/0eddxkOnbVV1p1T20KYJhREmCEolAjcS8x0ECLDUa9c8NPl9xrhaBIPKbOCIioulqpP22TNPEb397L1YctxKOCehZL6TyHq3y45wV8rwJy/CqtQpBWKp3GO9DEYvBVTHYClRiBYOtwvNeNZcCJSLxl35EROOAoRRNK/+8zz8jbaZx3Z+vw1VPX4WEksA/7v2PY/51C1P3Eu0dmPeBfULPyWfSXqVVTzC06oKRy8LI52Dk8zBzOX8/B0v3VvdxbBt6JgM9kxm1+1W0SCDAKoVdwXArPOyqCMb8R0nmXzVERETTiSAAiiZBSShItA399bblQM96gVTeD6wMP7DKB/ZDg62cBbiAbTrI9hvDmn4oCIDqB1YRfyph+RTDinCrUMHlB1sSm8QTETWEPynStHP6/qcjZaRwx0t34OInLkZCTWD5ouXNvi1E4glE4gnM2n2Phs53HBtmXoeRz8LM52HkcjDzufIAK5eFkc/DyPvP5XIw9cC5fshVeL3rer+VNPU8TD0/au9NkmUo0RjUYpVWBGo0Fgi/SlVbaiRSDLPKzvVfq0aikDWNv70kIiKawiRZRKxFHVYDeddxYeg29EwwtCqvxDKyFvL+sZHznsv7zzmWC9dFcSXEgWHcv6JJVVMMI4X9WGGKYaC3ViD0khWR3+cQ0bTBUIqmHUEQsO7QdUgZKfzs9Z/hnEfOwQ3H3oCj5h/V7FsbElGUoMVi0GKxUbme67qwTMOvxsrDyPlhVyHMygcDLC/sKoZgFeFWYdw2vf4RtmXBTg0gnxrOt3XVBEH0Q6pIeNgVMkWx+tzy6YxcGZGIiGhqEETBC3miw/tRxzK8flp6xg+zakwxLO4HQi8z762AaOo2TN1Gepc+5K8vykLINEOlWKGl+j2zqvb99yywOTwRTSIMpWhaEgQBF3z4AqSMFB54+wGc/dDZuHn5zTho1kHNvrWmEQQBiqpBUTXEWkfnmrZlFYOtYGVWWIBVfD5QzVUcD5wLAK7reMFYLgvsGkaTiRCyonoVWtEoVG3wACvYg8ubpuifG5jiKCkjazJLREREzSGrEmRVQrx16CshOrYDI2cjnzW9pu8Zq7Qf1k8rUz4F0XVcONYImsMLgBaViw3fy6YZhgVdFaEXm8MT0XhjKEXTliRKuPIjVyJjZfDYe4/hzAfPxO3H344l7UuafWtThiTLkBIJRBKJUbme6zgwDb1UrZXLlaq5ihVd+bIpjWVTFwPnFvpzObb3G03LNGCZBnID/aNyr4IoQo1EYUPA3X/YCC0WhxaPe4+FLR73q90S3mPF87I69CkLRERE1DyiJCIyzCbxruvC1O1ieGXkTOQzVs3eWsUKroy3bxkO4KL4+lTP0O9f9pvDqzEFWlSCGvECLjUieY9R2R8LPucf+89z+iERDQVDKZrWFEnBdz/+XZyx8Qw8u/NZrNm4Bncefyd2b9292bdGIQpBjxqJIj5jGF1TQ1imWTZVMSzAKuvXFXiu6txcDpbhlem7jgM96zWf734nPax7kxQlEFLFoMUT0KIxqKGBlv8YDLyiMa6kSERENEkIguAFPBEZyfahv942ner+WTnTm4aYC+unFajg8pvDW6YDq99AZhjN4QtEUYBSEWhpURlKRParuKRAmBUIvArH/vNsFk80PTCUomkvKkdx/bLrcdr9p+Hl3pdx+sbTcdfxd2FuYm6zb43GgawokJVWoGV05ix6Dei9kCqbSuGhBx/EoQcfBFvPQ896qyTq2Sz0bNp/9MaMbAb5bAaGPwYAtmki29+HbH/fsO9HjcYqqrLioVVbaiyGSCweCLy8R1lR+dtOIiKiSUBSRMSUETSHz5dPMTTyNoy8V6Vl5GzvsXCcLx3rOa+XlpGz4LqA47jFJvEjfT+hoVagakvxnwut6IrKUDWJPbaIJjiGUkQAkmoS65evxykbTsGb/W9izcY1uOP4O9AR7Wj2rdEk4zWg90KdSLIVkc5ZWLT/QVCG0GPKdRwY+ZwfYPlBVi5TcZwNhFyFLQs9k4aRzcIyvd9wFnpvpXq6hvd+JLnuNMPy41LVluofiyKbyBMREU1kgij4PaaG3xOzMP3QyAXCrLxVfpyzqp7XczZMP9wy8jYs3WutYJsOcqaB3AjXyVEitaq2JChRORBqcUoiUTMwlCLytUfacfPym7H6vtV4a+AtnPHgGbh15a1oUVuafWs0zQiiWAx5hssyTa/6qqwKq3Ss57IVgVZ5FZeRzcF1HTi2hdxA/4h6bSmRaHE6Yeg0w5BAKxiAyarGbwSJiIgmuOD0Q2DoTeILHNsJVGLVqNDKlY69Sq1SqFV4zrFdAICZt2HmbWT6hr4SYsGQpiTWmI6oRmRIMqckElViKEUUMCc+Bzev8IKpV3pfwdpNa7H+uPWIKbFm3xrRkMiKArl1BmKtM4b1eq9aK18VWhmFiqyyIKsi2PIDr0J/LdPvvzW8zlqAKEnl0wzjcajRRhrHlwIwUWK1FhER0WQgSiIicRGR+MhWMrZMe9Bph9VVW+M0JbEytCoEXH7VVr0piYomQeSURJpCGEoRVVjUsgg3Lb8Jp95/Kv6888/46kNfxXXHXgdV4kpoNH141VoxaLEYgJnDuoZtmcUAy8hmkfenFuaz/mMx5Kqu1PKmImb8ai0budQAcqnh1+8rWqSqKkuNxb2Aa5BgS41EIauqv2kMt4iIiCYJWZEgK9KwemwVjM2URAe5lDmi9zbYlERJFTGwVcULv3sXkZjqnat5YZeiea8r7Csqe25R8zGUIgqxpH0JbjzuRpz+wOl4fNvjOPfRc3HVR6+CLPIjQ9QoSVYQa2lFbJhN5L1vBGs0iA9UaFVPUyz12zL1PADA1PMw9TzSvcNYHztAlCTIqlYMqYKBlayqUDQNslL9nKJVn1vaLx0rhX3/fPbjIiIiap7JOyVRwx/feLOBNwgoqh9Q+UFXcV8rjMshwVbpueA+q7hoOPgTNlENB848EN/7m+/hrE1nYePbG3HJE5fgkqMugShwLjjRePC+EYxCjUSRbO8c1jVsy4KRyxabwNcKtorTE3OZ4nP5bAaWrhenIQKAY9vF5vHjQZTk8OArGG4VQjB/XKkTfNW8DkMwIiKiMTGeUxJzGRObX9uCubPnwzJcmLo/FTFvw9S9Ci5DtwEXgAtvTLeBETaUL5AV0Quv/JDKC60C+8GKrWIAFqjeqgjHJIk/e011DKWIBnHkvCNx9Uevxr8//O/45Ru/REJJ4JzDzmHTZaJJQpJlRJMtiCaHv2CB67qwTAOWYcAydP/RKAZWxWNDh1l2TsVzun9sVj9n6YFjf+VEAHDswm9Oxy8Ea7SqqxhuDTH4klWtWBEmiPxGk4iIqFGNTEk0TRO9976MY1d9sObqz973No4fVnmhlan7+3ohvAoc520YevC8UsBVOHYdr4rLMh1YozBFsUCSxUBYFajQqqjkCg/AgpVcXvglygJ/lptgGEoR1bFs0TJ84+hv4D//8J/44cs/RIvWgi8e+MVm3xYRjRNBEKD4QQqQHPOv5zoOLMusGXxZhgGzRvBlGUYp/DIqQjC9+tywEEzPWtCzmTF/n/BDw0aCr1LAVVEdNkjwBVGE5Tfml5NJBmBEREQ+73sbr6fUSHpuFbiuC9tyykOrQGBl6oH9ioqt4n7eLgvAHMsLuWzLgW05yGdGJ+QSRcGv5PICrGJoVdFvqxhwhVV2BZ6TFZEh1wgxlCJqwN/v+fdIGSlc+dSV+MFzP0CL2oKT9jmp2bdFRFOQIIqlECwxTiFYsRIsJNwyGwi+ivvhwZcZeM42S99U2pYF2xrbEOymX94NAFAiUahRbzqoGo15+5XHkShUv8F99XGseB4b3hMREZUIglCs4oqO0rcutuWUV28FAq3yqq3KCi6rKhgz8zYs0wHgr6SY9X4JB9TqydU4QUBIf63qiq3yXlxsPh/EUIqoQSftcxJSRgo3PHcDrnzqSiSUBP5hr39o9m0REY2IIIpQtAgULTIuXy8YgoWGW4UQLPCc2UjwVagqKwRsug49lwXcQlPYHMx8DqMRf8mKCiUahRaNQfHDKy0Wqx18BUOvQLilRqKQFIW/YSUiIqogySIkeeR9uAoc2ylOTSxVbAUqtCoCrvJKrsqpjKUVFV0XXl+vvD0q32MUms/ve/Q8HPPpvUfjihMeQymiIfjCAV9Aykjhrr/ehQsfvxBxJY7jFh3X7NsiIpo0giHYaP02NYxpmvjtb3+LlcuXw7UtGNksjHzOa1SfzwWO/bFcrsaxt2/mcsWpjpbpTXvMDfSP+D5FSS4Pr0KrtMqDLSUag+aPKX4gpkaikDWNARcREVEIURKhxURosdEJuVzHhWnY4RVblf23yqYy2qHN503d9n6P5jefd/1fqk0HDKWIhkAQBPzH0v9A2kzj56//HOc8cg6uX3Y9jpp3VLNvjYiIKgiC4PWdUuKItbSO+Hq2ZcHIewGVkctCz+Vg+iGXnsv64+VhVlUA5h+beh7w+3jl0ynk06lReL8ilEikOswKCbbKj2NQY/6jH5ApkQhXYyQiIqpBEAWoERlqZHQilcrm84o2ff4NZihFNESCIODCD1+ItJHGA28/gLN/fzZuXn4zDpp1ULNvjYiIxpAky4gmkoiOQq8vx7Fh5vVAeJWtrtIKhFlmPgfdPzZzXghWCshycF0Hruv4rx+d1RoVLRJSpeVNW/SCq8C+H25pUa+SS62Y3ijJ/JaTiIioltFuPj+Z8DsEomGQRAlXfuRKZKwMHnvvMZy56UzcvvJ2LGlf0uxbIyKiSUAUJWixGLRYbMTXcl0Xlq6XV2iFTVNscNqiY1sAAFPPw9TzyPTtGvE9SooS0ltr8ObySiRavQqjqkHWSses5iIiIprcGEoRDZMiKfjux7+LMzaegWd3Pos1G9fgrhPuwqKWRc2+NSIimkYEQYASiUCJRBCf0Tbi61mmWR1uFY/Lw6xC1ZbpB1vFaYx+AFbow2WbJnJm/6j04QoSJbkUWGlaWYBV3FdUP8jS/GArGHRVPnr7ihZyHYZgREREo46hFNEIROUorl92PU67/zS83PsyTn/gdNx1wl2YE5/T7FsjIiIaFllRICuto9KHy7Ht0vTEoTaX1/OBlRiDKzQagetbMHLWqE1ZrEeS5fDgq7hf/ZyiaV4w1mDwJauaVxGmqhBEcVzeFxERUbMwlCIaoaSaxI3H3YhTNpyCtwbewukPnI47jr8DHdGOZt8aERFRU4mShEgigUgiMWrXdB0HlmX6YZVRHlgZOsyK48K+WRluGZWvD7+ObZrFr21bFmzLgp4dlYW/6yoLwWqEW6UqsYrqsDrBV9W0SIZgRETUBAyliEZBR7QDt6y4BSffdzLeGngLZzx4Bm5deSta1JZm3xoREdGUIogiFL+aaDy4jgPLNGqHW6YeCMgKgVYw+KoOwsxgoGYGntN12JZV/NpNCcGC0yCVQYKvinArOC0SkoTMe+/g3Zf/gmg8AUWLQIlo3qMWgaQoEARhXN4TERFNbAyliEbJnPgc3LLiFqy+bzVe6X0FazetxU3Lb0JUjjb71oiIiGiYBFEshinjwXFs2IYZUvHVeLhl6uHVYmHXCQ3BMqMTgv384ftDx70/01JIpUSCj8Fxb1+uOK58PvhaWdUYeBERTSIMpYhG0aKWRbhp+U049f5T8eedf8ZXf/9VXHfsdVAkpdm3RkRERJOAKEoQIxKUyPiFYLUCrHrhlln1nB+W5fPo6epCLKJ54VreW8mxEIC5jlNsnD/qBMEPqrRS2FUIugY5lkOeVyPBsQinOBIRjQGGUkSjbEn7Evxg2Q+wZuMaPLbtMZz76Lm46qNXQeKKPURERDTBiKIENRKFGhm9ym7TNHHvvfdi1apVUJTSL+Yc24ap52H6IZWp64H9wnHOH9NL4/nAftWxF3oVG+C7rn+NHDC6iz0CAORgJdcQgy8lEq1d4aVpXN2RiKYlhlJEY+CgWQfh2r+5Fms3rcUDbz+AxB8TuPjIi1lOTkRERNOWKEnQYnFosfioX9txbFi6XhZ0GYHAywoGXcFQrOZxKfiydL34dSzd6/2VG4PES1ZUyDWmMFYdRyJVwVbosX+eKDHwIqKJiaEU0Rg5at5RuOqjV+HfH/53/Pz1nyOhJPAfS/+DwRQRERHRKBNFCWo0BjUaG/Vru44DyzBg5HODBllW3aCr4jifh2nogOsCgNcXzDSQT436W4Aky94UxNBKLg1qJOr37tKKz4uSDFGWIEkyREmCKHuPkj8uSjWO/fPKXx88R2RVGBEVMZQiGkPHLToOlxx1CS547ALc9de70KK24AsHfqHZt0VEREREDRJE0QtvxqDPl+u6xf5dZVMZa0xZtGpMYSy+tuIarusAxSb2aSCTHvX3MCyCACkQXImS7B0HAq2q40Dw5b1OHtk1ar0+9Hq1AzdBFPlLZ6IRYChFNMZO3OtEpI00vvX0t3D9c9cjoSZw0j4nNfu2iIiIiKjJhGJj9gjQ0jqq13ZdF7ZpNtabKzBlsVDx5dg2HMuCbdtwbKv62PIeQ48t/3z/dSE3V1ztEXrY3U8ug1aVDVZlFgy5/GNBlGqHZ4OFaf61XACZbVux9aUXoPoVb+HVa+H3xYCNxhtDKaJx8C/7/gtSZgo/eO4HuPKpK5FUk/j7Pf++2bdFRERERFOUIAiQVRWyqiKabGnafbiuWxZQ2cHAyrJhBwIvx/aPrcD5lccVgdeQwjPLCrzOgm3VeM0g1yhMtwzyXmPBmkAJ2y8e2jCs1wmiWBWeDTUYE8WhBXG1A7MGquEGq37jNNFJgaEU0Tg544AzkDJS+H9//X+44LELEJfjWLZoWbNvi4iIiIhozAiCAEn2qnWmAsexa1aF1Qy5qqrKGgvcGgrwyu7Bhm2Z2LVrFxLxGFzbCa9e86/lOk7V+3MdB5ZjAGZT/nhHV41posIwq9jEegFZA9M9G6pYEyWo0eiY9MibiKbG3wxEk4AgCPja0q8hbaTxizd+ga898jXcsOwGHDnvyGbfGhERERERNUAUJYiqBEBt9q2EMk0T9957L1atWgVFUQY913WcYshWt2ItWGU2pMq0IQZ4nCYKADhg2fFYvmZts29jXDCUIhpHgiDgoiMvQtpMY+PbG/GV338FNy+/GQfNOqjZt0ZERERERNOIIIqQRBGSrGDw+Gric10XruMMXn02jLBrWOHZKEwTFeXpM/WQodQUlvq/32DOT3+CHX98EpKmQlBVCIpSfBQrjgW1+pzwRxWCqgQeFTbEGwJJlHDlR65E1szisW2P4cxNZ+L2lbdjSfuSZt8aERERERHRpCMIAgRJgihJE7WIbUjckN5lUxVDqSks/9Jf0PLn55D683Nj/8UUBaKiDC/kqgy4AqEZKsOzhkKzwLmyt0zrRKNKKq75+DU448Ez8Oedf8YXNn4Bd55wJxa1LGr2rREREREREVETTaeiD4ZSU1h82TK80deHJXvtBdGy4RoGXNOsfjQNuEbp0ak4DnsNKufsmiYcc4J2w/PDMlFRAFWBqAw35AoJ3UZQcRZVIrh+2fU47f7T8ErvKzj9gdNx1wl3YU58TrP/xIiIiIiIiIjGHEOpKSx22GHY1dWF9gaa3A2Vaw8SclU8OoYRGBs87HLLzq1xPdPwQjAj/DphgZlrmiiMhrTAax5ZxqWKggwc6OJWbPn2cgy0zIWsaV71mKIAigxB9gMxWfY2RQZkf7zWmOKNDzrmHxfHFCVkvPxYkGUv6JugVWhEREREREQ0OUyIUOqGG27A1Vdfje3bt+PAAw/Eddddh8MPPzz03FtuuQV33XUX/vKXvwAADj30UHzzm98sO/+UU07BnXfeWfa6lStXYsOGDWP8TqYPQZIgRKNANNrsW6ni2rZfAdZgyGUEQ65geFYncKtTeeaEhG+wrPKbtSy4loUYAG/BTwt239aJFZwNRhSrg6qy8KoiOCuMFcKvMQ/Z1OpArVbINo1KZImIiIiIiCaCpodSP/7xj7Fu3TqsX78eRxxxBK699lqsXLkSr776KmbNmlV1/kMPPYTPfe5zOOqooxCJRPCtb30LK1aswEsvvYT58+cXzzv++ONx++23F481TRu390TNJUgSBEkCIpFm30oV13Fqhlzv9b6FSx65ANncAPZJ7oWv7H8WFLsUXLmmBdcy4VqWN2b644ONFY9L43XHzMJrzbLzqirQAMBxigHdpCdJ4dVgZSFZ2JgfflWFcTJcUULntvfQu/VdKMkEhFgMYiwGMeo/xv3H4ngUgjoFOjMSERERERE1oOmh1DXXXIPTTz8dp556KgBg/fr1+O1vf4vbbrsN5557btX5P/rRj8qO//u//xs/+9nPsGnTJpx88snFcU3TMGcOe/PQxCKIIgRNA0JC0j323BPn7Tkf/7bh3/CKuRldzs/x/WO/D0WaGAu0uq5bFVS5pgVYdcYqw7RGAjbTDIz5wVnZebXG/K9dOdZIwGbbXpWdro/qn1s7gN6HHm78BYpSHlT5YVVx3w+yqgKuypArGvXPiUOMRTnVkoiIiIiIJpymhlKGYeCZZ57BeeedVxwTRRHHHXccnnjiiYaukc1mYZom2tvby8YfeughzJo1C21tbTj22GNx2WWXoaOjI/Qauq5DD/wgOjAwAAAwTRPmRG3e3YDCvU/m9zDd7JncE9/7+Pdw5u/OxB/e+wO+/sjXce7ScyGLMhRRgSzKkASpeVPNBAFQFG8LDvvbZOE6jhdCmRVhWViIVRGyoey8kEAscJ6V1/H2a69h4cyZQD4PJ5eFm83CyeXgZnNwstnihsLn1DTh9PfD6e8f1fcsRCMQozEIgYCrGGzFotXjfthVGo8GzvfGBU3jtEeasPhvIFHz8PNH1Dz8/NFE0eh/g4Lruu6Y300N27Ztw/z58/H444/jyCOPLI6fc845ePjhh/Hkk0/WvcaZZ56J+++/Hy+99BIi/nSte+65B7FYDIsXL8bmzZtx/vnnI5FI4IknnoAkSVXXuPjii3HJJZdUjd99992IxWIjfp9EQ/W6+Tp+mPkh7JDuUgIEiBAhQYIkSJAgecf+/qiNB54XBbHq3MK4DLnqerXGGWAMwrYhGgZE3YBg6P6+9yj442JxPHiO4Z+jF/dFXfdeYxgQxvCveFcQ4KgqXE2Do6pwNA2OqsBVC8cqHFWDo6lwVdUbKx5r/vPeFjxGyN/TREREREQ0eWSzWfzzP/8z+vv70dLSUvO8pk/fG4krr7wS99xzDx566KFiIAUAn/3sZ4v7+++/Pw444ADsueeeeOihh7Bs2bKq65x33nlYt25d8XhgYAALFy7EihUrBv3Dm+hM08TGjRuxfPnyUV99j8be/lv3x2VPXYY+va9s3IUL2/8f3LInJjxZkCGLpa1Q/VX2KMhQJKXs3NDzRut1gdfLkgxFUKpeP9zqtGZ/Bl3XhavrXnWWv7m5HBy/Ssur2gqO+/uBKq6y8VwWTjYHN58HvN9qQNJ1YJSnPAqqWlG5FS1OVSyMB6u8StVd5dVcXkWXX+UViXAK4zTT7M8f0XTGzx9R8/DzRxNFYQZaPU0NpTo7OyFJEnbs2FE2vmPHjrr9oL797W/jyiuvxIMPPogDDjhg0HP32GMPdHZ24o033ggNpTRNC22ErijKlPggT5X3Md2s3GMlVu6xEo7rwHIsmI5Z/mibMF0Tpm3Ccr3j0POG+xi4rumasOzycxq5TiXLtWDZFibP8oIeAcLgoZZUHoopkh9sQUZ3pht/eOoPUGW17HXBcxVRKX8ucN2w58qeF5Ti16t8XhAEQFWBZHJU/zxc24aTy8PJZsoCL2/LVRxnSuFWpvJcbyqjk83CyWSKq1MWmueP+hTGwXp1Bft1RaN+L66YF5BJotcIX5IBSYQgyRBkCRAlCLK/sEJhgQVJLp0vy14QJsvec8Hzq54TWUk4RvhvIFHz8PNH1Dz8/FGzNfrfX1NDKVVVceihh2LTpk048cQTAQCO42DTpk1Yu3ZtzdddddVVuPzyy3H//fdj6dKldb/Ou+++i56eHsydO3dU759oPIiCCFVSoUqTa1U213Vhu/agoddQgrJBA7FgMDcKAZ3tlqdmLtzi1x2Ol95+aZT+VIcmLCgbLAAb9Hk/SKsZnrUqkNsKQVoLFLFj0CBNCwnSJMspVWYVw6rygCtYuVX2XHC8EIRlvHH4UxjdbBZ2NjtxM1GpPOAqC7uKIZYXjnnBlzzI+RIEUapxfkWwFnZ+4LlgAFd+j4Hryn6wVhHAlZ1feA8NhHpl75mIiIiIpqymT99bt24dVq9ejaVLl+Lwww/Htddei0wmU1yN7+STT8b8+fNxxRVXAAC+9a1v4cILL8Tdd9+N3XffHdu3bwcAJBIJJBIJpNNpXHLJJfjkJz+JOXPmYPPmzTjnnHOw1157YeXKlU19r0TTiSAIxal0k81oVaflzTyef/F5fGCfD8ARStcMvq4ybCurUGsglCucV7Myza8+wiTpdVkZoJUFYJoMJapA6QwL0hKQxRnVIZgoQ7MEaIbrbboL1bChGi4U3YaiW5B1G3LegqybkPLeJuYNb7MciI4L0XYhuIBou4BjA7YD17YB24JrO3BtC7Bsr4m+ZfnP+Ss6BvZDV34sKJwzOWbjjh85JHyrDOmCVWf+Oa4oYkFqAO/94peQIhGv6k3TIKgKBFWFqGqBMRWCqkAs7oeMFR4VFaKmVrxWZYBGRERENAxN/2nxM5/5DLq6unDhhRdi+/btOOigg7BhwwbMnj0bAPDOO+9ADHyjd+ONN8IwDHzqU58qu85FF12Eiy++GJIk4YUXXsCdd96Jvr4+zJs3DytWrMCll14aOkWPiKjSaFWnmaaJyGsRrPrgqjEvnw6rTBss4KpVsTZY+BX6+iEEaZXn1Q3SxoPqb0OY4SgKIiJSBBE5Ak3SEJEjiEjx0nHVcxFosoaoHIUmqoiIGiKCighkRAQVGhREBAUqZGiCAs2VoQkyVMhQHBFwHbhWKQDzHm1vzPEeXdvygzLLD7f88ywbrmN7gZk9jPOLwZpVCuIsy1/Bcgjn27XO84M6x6n9B15Y0XIYYV0MQO7Nt4b4qmFSFIiKUh5U+eGVqAQCLD8YE4PhVzAs0zQISr2wLDwYE1UVUBROBSUiIqJJo+mhFACsXbu25nS9hx56qOz4rbcG/+YyGo3i/vvvH9X7IyKa6CZjZVojQVpoyDXEKrKaIVudwM6wDZiOiZyVg27rcFwvOHFcB1kri6yVHfM/IwFCMdzSJD/YkjQv5JKi0GQ/BFPLQ7DygCxefX7l9fzXikJzqn28kKu80qyywswL4izAqRHSFc7zwzozr+PZp57CwR/6EETbgmMYcHWj2LPMNXSvd5muwzVMb0z3x4zqMW/cKBtDcHVL04RjmkB27P+7qKcsrNL8UKxirBBilYKxsLGKsGwoVWSqCkGePH8fERERUXPwuwUiImqKyRSkua7XVyxv56FbOvJWHnk7X3zU7fKx4LFu6cVgq9bzZdeydFhuoTLI9Z6z8+PyPlVRLavuCg+56gRkcnVAVrxe4Png/++CKHo9qQBglKqaTdNEOp9DctXYVCq6rgtYFhzdgGtWhFeF/cJzfpAVFoxVjek6XNPwrlsRgtUKy1yzvOqwMN50khQIuqqDsbphmaL4m+xNz1QUCLLi9SfzxwVZ9qrDZNl7LjAuKIr3OlnxwrXiNQLPs7KMiIioqSb+TwJERERNJghCaUrnOKw5YDpmWVhVFmhZOnJ2DrqlQ7cDgVeNgCwsSAteLziN0nAMGIaBATS2hO9IyKJcs2qrsB+Rw0OuYEBWa7qk7MrIOTnkrBwESRj18FMQBEBRICkKgPioXnuoXMeBa5ohwZhZrAirCsuMUpjWcFgWrBYLqyLT9fKpmLYNN5eDncs184+nPkkKCasCIVdg3AvBQgKyQcYFJRCQBZ/3g7Ja42XBmlLrXhiqERHR5MZQioiIaIJRRAWKqiCBxJh/LduxiyFVMPAKq+oKO66s9qoMyIKhmW7rxa9rORbSThppMz2m7+/yn1wO+L3AFFGBKqrFlSQVUYEqqWWPwZUmB33Ov44qls6RRTn0NcX9sNf6YyOZOimIIgRNG7Uqs5FwLSsQgtWqIqsTlgWCMVgWXNPrK+aapvdomV51WNW4Bdc0aox7+zBDVn0oTA9txh/YaCgEVY0EZH7oBbkidGugIq2s8qzOuA1A27YN+htvwIlEyr++LANy4PWS1Ow/QSIiaiKGUkRERNOYJEqIiTHElNiYfy3HdWDYRs2qrarKr4qx0MqwkOmPhWM3EDM4rlO8xkRcjbKw8uSQwrIaAVswLBssYBs0LAtcRxblhqtxiqFDLIaJGDUUpl2WhVWm9wjLLA+zzMK+6Ydj1eOFoKt8PBCcVY0HrtnIeMXzCFsIovB8M/5AB7EIwNbvfb/+iYJQXvkVDNkKAZtcUSVWCNDKxkPGigFcYLx4XiFIC4R5FeeVjSkVwV9F2AdJYtUaEdEwMJQiIiKicSEKYnFK3lgzDAO/ufc3WLZiGSCh2Li++OgYpYb6/mNhzHBK5wab3pe9vjBW8dqqa9d4TaFvWEFh5ckcJuZUt5phWViQVissCwvCaoRlsihDFVVoklacOlu1L6pDDgEK0y6FMV4RdawUQ7XKMMu0vFAtZLwsIGtoPCSMq6w+M41BAzfXtJBPpaDKclkIGFqp5roTpw/aSFWGasHKNVkZnbAt2E+tXogWDM2CwVrYWDBsY7hGROOIoRQRERFNOYIgQBIkxJTYmDQ6HynHdcoCr9CwzA+wKsOyYvg1WFhW57WDBWymY8J27bL7LYyPx6qTQ6GISnhwVSPQ0iSt+BpN0qBIgf1hjkvi+NWETZZQzTRN3HvvvVhVsdCA67r+KpnBIMysqF6zSoFYcPpmVYhmlarVap1nBq5rhY+VTQUNnls15lerFaaCuiG1af77mWhVa0NWCK4kydsXRUCWIEj+mCRBkCQIsgRIhee954LPF18jS4BYPgZJLF1PliCIgetJUun5QcdE/2sFvoYslcYk0TuvMFZ4L/5Y+b36X8O/RvE5sTmr0hJNJwyliIiIiMaZKIil5vkTkO3YoRVlwXArWFEWFpZVhmaWY4VWowVDtMrXG7YB3da9Jvz+flDhtc2cklmYelkvEGuk8it0vIFrDmWKZTMJglAMPCY7txCuFarUqqaFDiNsqxeimYZfcdZAiFaYkloxDrP8XmHb1W9ugk4JbYrCf7PB8GqEIV1VIDeskK4ikAuMOQDiL72ETCIBWYvUqKQrr5wrm46qKOz1RuNq8v+LQERERESjShIlSKKECMZ+quVQuK4Ly7GK/cFMxyzt2+bwxwv7Tmm/LBSzDRhOaT9YSVacemk1b+qlAKGxgKvGeM1qMEmBJtYYr6gYUyV1RA37J5ti4DABFhgYCddxQsMq17QAx4Zr2YBtlc6zA2O2A9cOjPnnF8dsxzuvOObAte3SmGMDll1zrPQaq3Re8GtYNlzH8UI0f8ECWN69lr6uXVrMoNZrbLt85dCyPyB3UlbAzQfw/l3/b/gXEITqPm/B6aANTz8trGYa0tctuKhC5VRVpda01DphWvH+lFJlHE14DKWIiIiIaFIQBMHrRyWNz+qUtRSmXlaGVbVCrEbHy45DqsSC46ZTKg9z4RYDthRSTftzkUW5GH7Zho31v14f2lNMluS6/ceC4zV7kwXPq2jYX/U1RXlahWaNEkQRgqoC6sSs2hwvXpDlh1eFgK0QcgWDrVohXWUgNxohXUUg12hI55gmdnV1YUYy6d9LRSVdIICsOR3VdUvTVZv1f8poEMXwsKoqNGtgfLTDtHq93yIRiNPkc8lQioiIiIhoCGRRhizK47JqZS2O6xQrv6oCrjqB1lBCs3rXCq5yaTkWLMdCBhkAwEB6oGl/PmFqrXJZudrkYCtYhoVmYdepCtbqhGaF4GwyTMOcigRRLAYYk51pmnjh3nuxf0VPt8GUpqPWmHoaXPwgOK20MKW0stdbVf+3RqaqVk43DVmldJDx0NVJHQeursPV9bC3PaHN+PSnMfcblzT7NsYFQykiIiIioklGFMTiFLpmcV0XlmtVhVUZPYPfP/J7HH7k4XBFt7x5f40m/ZXN9mutbGnZVt3rFCrZKhv2T/RVLhFY6XIo4VZwXxblqgAtbHXLetcpnBuc6ikLDM2mqqkwHTV8ddJAz7fi6qEV/d0qViVFVRgX0uOtkfEG+7wFF1BwLas4lXQq9N5r1PR5p0RERERENGoEQYAieAFGXIkXx03TxGvyazhw5oFNXf3SduxiaBYWdFmONXhIFmjSH9rUP2S1zMGuEwzMCmNuxeSoYvP+kKKPZhMglPUUK/Qoq+xNVhirOV7RuD+skX+hn1nltQrVbAzHqNJkWZ20nuLU0GmEoRQREREREU05kihBgtTUarJ6bMcuW9Wy0XArrLKsanXMIQRmheCt8NrCVE7LKf1w7MJF3s4jb+eb2rss2Ng/LOAKW+EydLxGOFa2muYg5zEco7FQ7PM2jTCUIiIiIiIiagJJlBAVo4gi2uxbCeW4Ts1G/JX9xoJN+MvO8/uUVa5sWTVecZ3gOZXhWKGxf7OFVoxVVHkNGo41WG022HmKqDAco0mNoRQRERERERFVEQURETmCCCJNvY/KcKwqIKsTcFUFZSHBVyOvDa56CaAUjpk1b31cBKu5FFGBlbdw5713IiJHoEoqIlKkLCALHhfH5AbOqRjTJA2SKDX3zdOkx1CKiIiIiIiIJqyJFI7VCrjqVYgNGo4FgrDQVTBDgrIgw/HOCYZj3X3d4/JnIgtyKaiSSwFWoVosGHYFt8ECsMpzCtctG/OnUNLkx/8XiYiIiIiIiOqYCKteIhCOFUOqQJVXVs/i4ccexiGHHQJbsIvhVt7OF8/XbR26pRefCzsn9DX+FpxOWVjVMmtlgXGeUSkJUlWY1Ui4Neg5DV6Hq1GOHoZSRERERERERJNEMBxLIln2nGmaeFt+Gx+e++ExW/2y0KDfsA3krUBw5VSHXcGtcqwqJHMCYzWuE6wSs10bOSuHnJUbk/c5mML/B2FhVuV0ybBwKyJHyqZdVlaEzY7Nxm4tu437+2oGhlJERERERERE1JBig345ilatdVy/dqG/2JDCrkFCssrqsNDrFK7hGGX3MZaB2Kc+8ClcdORFY3LtiYahFBERERERERFNeMX+YvL49xcL9hQbTtgVrCoznPIqs8rrzIrNGvf31ywMpYiIiIiIiIiIBlHWU0xt9t1MHWKzb4CIiIiIiIiIiKYfhlJERERERERERDTuGEoREREREREREdG4YyhFRERERERERETjjqEUERERERERERGNO4ZSREREREREREQ07hhKERERERERERHRuGMoRURERERERERE446hFBERERERERERjTuGUkRERERERERENO4YShERERERERER0bhjKEVEREREREREROOOoRQREREREREREY07hlJERERERERERDTuGEoREREREREREdG4YyhFRERERERERETjjqEUERERERERERGNO4ZSREREREREREQ07hhKERERERERERHRuGMoRURERERERERE405u9g1MRK7rAgAGBgaafSsjYpomstksBgYGoChKs2+HaNrhZ5Coefj5I2oefv6ImoefP5ooCnlKIV+phaFUiFQqBQBYuHBhs2+FiIiIiIiIiGhSSqVSaG1trfm84NaLraYhx3Gwbds2JJNJCILQ7NsZtoGBASxcuBBbt25FS0tLs2+HaNrhZ5Coefj5I2oefv6ImoefP5ooXNdFKpXCvHnzIIq1O0exUiqEKIpYsGBBs29j1LS0tPAvJKIm4meQqHn4+SNqHn7+iJqHnz+aCAarkCpgo3MiIiIiIiIiIhp3DKWIiIiIiIiIiGjcMZSawjRNw0UXXQRN05p9K0TTEj+DRM3Dzx9R8/DzR9Q8/PzRZMNG50RERERERERENO5YKUVEREREREREROOOoRQREREREREREY07hlJERERERERERDTuGEpNYTfccAN23313RCIRHHHEEXjqqaeafUtEU94VV1yBww47DMlkErNmzcKJJ56IV199tdm3RTQtXXnllRAEAWeffXazb4Vo2njvvffwL//yL+jo6EA0GsX++++PP/3pT82+LaIpz7ZtXHDBBVi8eDGi0Sj23HNPXHrppWALaZroGEpNUT/+8Y+xbt06XHTRRXj22Wdx4IEHYuXKldi5c2ezb41oSnv44Ydx1lln4Y9//CM2btwI0zSxYsUKZDKZZt8a0bTy9NNP46abbsIBBxzQ7FshmjZ27dqFo48+Goqi4L777sNf//pXfOc730FbW1uzb41oyvvWt76FG2+8Eddffz1efvllfOtb38JVV12F6667rtm3RjQorr43RR1xxBE47LDDcP311wMAHMfBwoUL8aUvfQnnnntus2+PaNro6urCrFmz8PDDD+OjH/1os2+HaFpIp9M45JBD8IMf/ACXXXYZDjroIFx77bXNvi2iKe/cc8/FY489hkcffbTZt0I07fzd3/0dZs+ejVtvvbU49slPfhLRaBQ//OEPm3pvRINhpdQUZBgGnnnmGRx33HHFMVEUcdxxx+GJJ55o6r0RTTf9/f0AgPb29mbfCtG0cdZZZ+Fv//Zvy/4dJKKx9+tf/xpLly7FP/3TP2HWrFk4+OCDccsttzT7toimhaOOOgqbNm3Ca6+9BgB4/vnn8Yc//AEnnHBCs2+NaFBys2+ARl93dzds28bs2bPLxmfPno1XXnmlafdFNN04joOzzz4bRx99ND70oQ81+3aIpoV77rkHzz77LJ5++ulm3wrRtLNlyxbceOONWLduHc4//3w8/fTT+PKXvwxVVbF69epm3x7RlHbuuediYGAAH/zgByFJEmzbxuWXX46TTjqp2bdGNCiGUkREY+Sss87CX/7yF/zhD39o9q0QTQtbt27FV77yFWzcuBGRSKTZt0M07TiOg6VLl+Kb3/wmAODggw/GX/7yF6xfv56hFNEY+8lPfoIf/ehHuPvuu7Hffvvhueeew9lnn4158+bx80cTGkOpKaizsxOSJGHHjh1l4zt27MCcOXOadl9E08natWvxm9/8Bo888ggWLFjQ7NshmhaeeeYZ7Ny5E4ccckhxzLZtPPLII7j++uuh6zokSWrqPRJNZXPnzsW+++5bNrbPPvvgZz/7WdPuiWi6+NrXvoZzzz0Xn/3sZwEA+++/P95++21cccUVDKVoQmNPqSlIVVUceuih2LRpU3HMcRxs2rQJRx55ZFPvjWiqc10Xa9euxS9+8Qv87ne/w+LFi5t9S0TTxrJly/Diiy/iueeeK25Lly7FSSedhOeee46BFNEYO/roo/Hqq6+Wjb322mtYtGhR0+6JaLrIZrMQxfIf7yVJguM4TbsnokawUmqKWrduHVavXo2lS5fi8MMPx7XXXotMJoNTTz212bdGNKWdddZZuPvuu/GrX/0KyWQS27dvBwC0trYiGo02+/aIprRkMlnVvy0ej6Ojo4N93YjGwVe/+lUcddRR+OY3v4lPf/rTeOqpp3DzzTfj5ptvbvatEU15n/jEJ3D55Zdjt912w3777Yc///nPuOaaa/Bv//Zvzb41okEJruu6zb4JGhvXX389rr76amzfvh0HHXQQvv/97+OII45o9m0RTWmCIISO33777TjllFPG/X6IpruPf/zjOOigg3Dttdc2+1aIpoXf/OY3OO+88/D6669j8eLFWLduHU4//fRm3xbRlJdKpXDBBRfgF7/4BXbu3Il58+bhc5/7HC688EKoqtrs2yOqiaEUERERERERERGNO/aUIiIiIiIiIiKiccdQioiIiIiIiIiIxh1DKSIiIiIiIiIiGncMpYiIiIiIiIiIaNwxlCIiIiIiIiIionHHUIqIiIiIiIiIiMYdQykiIiIiIiIiIhp3DKWIiIiIiIiIiGjcMZQiIiIimiYEQcAvf/nLZt8GEREREcBQioiIiGh8nHLKKRAEoWo7/vjjm31rRERERE0hN/sGiIiIiKaL448/HrfffnvZmKZpTbsfIiIiomZipRQRERHRONE0DXPmzCnb2traAH9q3Y033ogTTjgB0WgUe+yxB/73f/+37PUvvvgijj32WESjUXR0dGDNmjVIp9Nl59x2223Yb7/9oGka5s6di7Vr15Y9393djX/8x39ELBbD3nvvjV//+tfj8M6JiIiIqjGUIiIiIpogLrjgAnzyk5/E888/j5NOOgmf/exn8fLLLwMAMpkMVq5ciba2Njz99NP46U9/igcffLAsdLrxxhtx1llnYc2aNXjxxRfx61//GnvttVfZ17jkkkvw6U9/Gi+88AJWrVqFk046Cb29veP+XomIiIgE13XdZt8EERER0VR3yimn4Ic//CEikUjZ+Pnnn4/zzz8fgiDgjDPOwI033lh87sMf/jAOOeQQ/OAHP8Att9yCr3/969i6dSvi8TgA4N5778UnPvEJbNu2DbNnz8b8+fNx6qmn4rLLLgu9B0EQ8F//9V+49NJLAT/oSiQSuO+++9jbioiIiMYde0oRERERjZO/+Zu/KQudAKC9vb24f+SRR5Y9d+SRR+K5554DALz88ss48MADi4EUABx99NFwHAevvvoqBEHAtm3bsGzZskHv4YADDijux+NxtLS0YOfOnSN+b0RERERDxVCKiIiIaJzE4/Gq6XSjJRqNNnSeoihlx4IgwHGcMbknIiIiosGwpxQRERHRBPHHP/6x6nifffYBAOyzzz54/vnnkclkis8/9thjEEURS5YsQTKZxO67745NmzaN+30TERERDQcrpYiIiIjGia7r2L59e9mYLMvo7OwEAPz0pz/F0qVLccwxx+BHP/oRnnrqKdx6660AgJNOOgkXXXQRVq9ejYsvvhhdXV340pe+hH/913/F7NmzAQAXX3wxzjjjDMyaNQsnnHACUqkUHnvsMXzpS19qwrslIiIiGhxDKSIiIqJxsmHDBsydO7dsbMmSJXjllVcAf2W8e+65B2eeeSbmzp2L//mf/8G+++4LAIjFYrj//vvxla98BYcddhhisRg++clP4pprrilea/Xq1cjn8/jud7+L//iP/0BnZyc+9alPjfO7JCIiImoMV98jIiIimgAEQcAvfvELnHjiic2+FSIiIqJxwZ5SREREREREREQ07hhKERERERERERHRuGNPKSIiIqIJgB0ViIiIaLphpRQREREREREREY07hlJERERERERERDTuGEoREREREREREdG4YyhFRERERERERETjjqEUERERERERERGNO4ZSREREREREREQ07hhKERERERERERHRuGMoRURERERERERE446hFBERERERERERjbv/HzyKPR/zxpEsAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# === TRAINING ===\n",
+ "history = model.fit(\n",
+ " X_train,\n",
+ " {\"ner_output\": np.array(y_ner_train), \"srl_output\": np.array(y_srl_train)},\n",
+ " validation_data=(X_val, {\"ner_output\": np.array(y_ner_val), \"srl_output\": np.array(y_srl_val)}),\n",
+ " batch_size=2,\n",
+ " epochs=10\n",
+ ")\n",
+ "\n",
+ "# === SAVE ===\n",
+ "model.save(\"multi_task_bilstm_model.keras\")\n",
+ "with open(\"word2idx.pkl\", \"wb\") as f:\n",
+ " pickle.dump(word2idx, f)\n",
+ "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
+ " pickle.dump(tag2idx_ner, f)\n",
+ "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
+ " pickle.dump(tag2idx_srl, f)\n",
+ " \n",
+ " \n",
+ "history_dict = history.history\n",
+ "\n",
+ "# === LOSS ===\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "\n",
+ "plt.plot(history_dict[\"loss\"], label=\"Total Loss (train)\")\n",
+ "plt.plot(history_dict[\"val_loss\"], label=\"Total Loss (val)\")\n",
+ "plt.plot(history_dict[\"ner_output_loss\"], label=\"NER Loss (train)\")\n",
+ "plt.plot(history_dict[\"val_ner_output_loss\"], label=\"NER Loss (val)\")\n",
+ "plt.plot(history_dict[\"srl_output_loss\"], label=\"SRL Loss (train)\")\n",
+ "plt.plot(history_dict[\"val_srl_output_loss\"], label=\"SRL Loss (val)\")\n",
+ "\n",
+ "plt.title(\"Model Loss per Epoch\")\n",
+ "plt.xlabel(\"Epoch\")\n",
+ "plt.ylabel(\"Loss\")\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# === ACCURACY ===\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "\n",
+ "plt.plot(history_dict[\"ner_output_accuracy\"], label=\"NER Accuracy (train)\")\n",
+ "plt.plot(history_dict[\"val_ner_output_accuracy\"], label=\"NER Accuracy (val)\")\n",
+ "plt.plot(history_dict[\"srl_output_accuracy\"], label=\"SRL Accuracy (train)\")\n",
+ "plt.plot(history_dict[\"val_srl_output_accuracy\"], label=\"SRL Accuracy (val)\")\n",
+ "\n",
+ "plt.title(\"Model Accuracy per Epoch\")\n",
+ "plt.xlabel(\"Epoch\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "aeef32c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 421ms/step\n",
+ "\n",
+ "📊 [NER] Test Set Classification Report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " DATE 0.00 0.00 0.00 5\n",
+ " EVENT 0.00 0.00 0.00 1\n",
+ " LOC 1.00 0.43 0.60 7\n",
+ " ORG 0.00 0.00 0.00 2\n",
+ " PER 0.00 0.00 0.00 1\n",
+ " TIME 0.00 0.00 0.00 4\n",
+ "\n",
+ " micro avg 0.60 0.15 0.24 20\n",
+ " macro avg 0.17 0.07 0.10 20\n",
+ "weighted avg 0.35 0.15 0.21 20\n",
+ "\n",
+ "\n",
+ "📊 [SRL] Test Set Classification Report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " CAU 0.00 0.00 0.00 1\n",
+ " COM 0.00 0.00 0.00 2\n",
+ " LOC 0.00 0.00 0.00 8\n",
+ " MNR 0.00 0.00 0.00 2\n",
+ " MOD 0.00 0.00 0.00 3\n",
+ " PRP 0.00 0.00 0.00 1\n",
+ " RG0 1.00 0.11 0.20 9\n",
+ " RG1 0.19 0.17 0.18 24\n",
+ " RG2 0.33 0.50 0.40 4\n",
+ " SRC 0.00 0.00 0.00 1\n",
+ " TMP 0.22 0.22 0.22 9\n",
+ " _ 1.00 0.11 0.19 19\n",
+ "\n",
+ " micro avg 0.28 0.13 0.18 83\n",
+ " macro avg 0.23 0.09 0.10 83\n",
+ "weighted avg 0.43 0.13 0.16 83\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, msg_start, len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-TMP seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-SRC seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MOD seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRP seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-CAU seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-LOC seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-MNR seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-TMP seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-COM seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-MOD seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# evaluation\n",
+ "y_pred_ner, y_pred_srl = model.predict(X_test)\n",
+ "\n",
+ "y_true_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_ner_test]\n",
+ "y_pred_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_pred_ner]\n",
+ "\n",
+ "y_true_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_srl_test]\n",
+ "y_pred_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_pred_srl]\n",
+ "\n",
+ "print(\"\\n📊 [NER] Test Set Classification Report:\")\n",
+ "print(classification_report(y_true_ner, y_pred_ner))\n",
+ "\n",
+ "print(\"\\n📊 [SRL] Test Set Classification Report:\")\n",
+ "print(classification_report(y_true_srl, y_pred_srl))\n",
+ "\n",
+ "\n",
+ "# import numpy as np\n",
+ "\n",
+ "# # Prediksi model (output = probabilitas)\n",
+ "# y_pred_ner = model.predict(X_test)[0]\n",
+ "# y_pred_ner_idx = np.argmax(y_pred_ner, axis=-1)\n",
+ "# y_true_ner_idx = np.argmax(y_ner_test, axis=-1)\n",
+ "\n",
+ "# # Mapping ke string\n",
+ "# y_pred_ner_str = []\n",
+ "# y_true_ner_str = []\n",
+ "\n",
+ "# for y_true_seq, y_pred_seq in zip(y_true_ner_idx, y_pred_ner_idx):\n",
+ "# true_seq = []\n",
+ "# pred_seq = []\n",
+ "# for t, p in zip(y_true_seq, y_pred_seq):\n",
+ "# if idx2tag_ner[t] != \"PAD\":\n",
+ "# true_seq.append(idx2tag_ner[t])\n",
+ "# pred_seq.append(idx2tag_ner[p])\n",
+ "# y_true_ner_str.append(true_seq)\n",
+ "# y_pred_ner_str.append(pred_seq)\n",
+ "\n",
+ "# from seqeval.metrics import classification_report\n",
+ "# print(\"\\n📊 [NER] Test Set Classification Report:\")\n",
+ "# print(classification_report(y_true_ner_str, y_pred_ner_str))\n",
+ "\n",
+ "\n",
+ "# from collections import Counter\n",
+ "\n",
+ "# flat_preds = [tag for seq in y_pred_ner_str for tag in seq]\n",
+ "# print(Counter(flat_preds))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "5a18da05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def plot_confusion_matrix(y_true_flat, y_pred_flat, labels, title=\"Confusion Matrix\"):\n",
+ " cm = confusion_matrix(y_true_flat, y_pred_flat, labels=labels)\n",
+ " plt.figure(figsize=(10, 8))\n",
+ " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
+ " xticklabels=labels, yticklabels=labels)\n",
+ " plt.title(title)\n",
+ " plt.xlabel(\"Predicted\")\n",
+ " plt.ylabel(\"Actual\")\n",
+ " plt.xticks(rotation=45)\n",
+ " plt.yticks(rotation=0)\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "cee30988",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Flatten label\n",
+ "y_true_flat_ner = [tag for seq in y_true_ner for tag in seq]\n",
+ "y_pred_flat_ner = [tag for seq in y_pred_ner for tag in seq]\n",
+ "\n",
+ "# Buat plot\n",
+ "plot_confusion_matrix(\n",
+ " y_true_flat_ner, \n",
+ " y_pred_flat_ner, \n",
+ " labels=list(tag2idx_ner.keys()), \n",
+ " title=\"NER Confusion Matrix\"\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "4ba2b85c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "y_true_flat_srl = [tag for seq in y_true_srl for tag in seq]\n",
+ "y_pred_flat_srl = [tag for seq in y_pred_srl for tag in seq]\n",
+ "\n",
+ "plot_confusion_matrix(\n",
+ " y_true_flat_srl, \n",
+ " y_pred_flat_srl, \n",
+ " labels=list(tag2idx_srl.keys()), \n",
+ " title=\"SRL Confusion Matrix\"\n",
+ ")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ner_lstm.ipynb b/old/ner_lstm.ipynb
similarity index 100%
rename from ner_lstm.ipynb
rename to old/ner_lstm.ipynb
diff --git a/old/new_lstm_ner_srl.ipynb b/old/new_lstm_ner_srl.ipynb
new file mode 100644
index 0000000..c94785b
--- /dev/null
+++ b/old/new_lstm_ner_srl.ipynb
@@ -0,0 +1,795 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "fb106e20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json, pickle\n",
+ "import numpy as np\n",
+ "from keras.models import Model\n",
+ "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
+ "from keras.preprocessing.sequence import pad_sequences\n",
+ "from keras.utils import to_categorical\n",
+ "from seqeval.metrics import classification_report\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from tensorflow.keras.metrics import CategoricalAccuracy\n",
+ "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+ "import matplotlib.pyplot as plt\n",
+ "from collections import Counter\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "00347a5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Distribusi label SRL:\n",
+ "ARG1: 2597\n",
+ "O: 1487\n",
+ "V: 943\n",
+ "ARGM-LOC: 781\n",
+ "ARGM-TMP: 736\n",
+ "ARG0: 685\n",
+ "ARG2: 445\n",
+ "ARGM-MNR: 221\n",
+ "ARGM-CAU: 113\n",
+ "ARGM-MOD: 90\n",
+ "ARGM-ADV: 29\n",
+ "ARGM-NEG: 18\n",
+ "ARGM-PRP: 18\n",
+ "ARGM-SRC: 16\n",
+ "ARGM-COM: 16\n",
+ "ARGM-PRD: 13\n",
+ "ARGM-DIR: 13\n",
+ "ARG3: 12\n",
+ "ARGM-DIS: 8\n",
+ "ARGM-BNF: 6\n",
+ "I-TIME: 4\n",
+ "ARGM-FRQ: 2\n",
+ "ARGM-ORD: 2\n",
+ "ARGM-EXT: 2\n",
+ "ARGM-REC: 2\n",
+ "B-TIME: 2\n",
+ "Osoe: 1\n",
+ "ARGM-ADJ: 1\n",
+ "ARGM-EX: 1\n",
+ "\n",
+ "Distribusi label NER:\n",
+ "O: 5782\n",
+ "B-LOC: 551\n",
+ "I-QUANT: 224\n",
+ "B-QUANT: 203\n",
+ "I-LOC: 202\n",
+ "I-EVENT: 151\n",
+ "I-DATE: 146\n",
+ "B-EVENT: 143\n",
+ "B-DATE: 139\n",
+ "I-PER: 121\n",
+ "B-TIME: 113\n",
+ "B-PER: 110\n",
+ "I-TIME: 98\n",
+ "B-ETH: 83\n",
+ "I-ETH: 65\n",
+ "B-ORG: 60\n",
+ "I-ORG: 24\n",
+ "B-MISC: 22\n",
+ "B-RES: 8\n",
+ "B-MIN: 6\n",
+ "I-MISC: 4\n",
+ "I-TERM: 3\n",
+ "B-REL: 2\n",
+ "B-TERM: 2\n",
+ "I-RES: 2\n",
+ "\n",
+ "Total kalimat: 799\n",
+ "Total token: 8264\n"
+ ]
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "\n",
+ "data = []\n",
+ "\n",
+ "with open(\"../dataset/dataset_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
+ " tokens, ner_labels, srl_labels = [], [], []\n",
+ " \n",
+ " for line in f:\n",
+ " line = line.strip()\n",
+ " if not line:\n",
+ " if tokens:\n",
+ " data.append({\n",
+ " \"tokens\": tokens,\n",
+ " \"labels_ner\": ner_labels,\n",
+ " \"labels_srl\": srl_labels\n",
+ " })\n",
+ " tokens, ner_labels, srl_labels = [], [], []\n",
+ " else:\n",
+ " token, ner, srl = line.split(\"\\t\")\n",
+ " tokens.append(token)\n",
+ " ner_labels.append(ner)\n",
+ " srl_labels.append(srl)\n",
+ "\n",
+ "# Preprocessing\n",
+ "sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
+ "labels_ner = [item[\"labels_ner\"] for item in data]\n",
+ "labels_srl = [item[\"labels_srl\"] for item in data]\n",
+ "\n",
+ "# Hitung total label SRL\n",
+ "srl_counter = Counter()\n",
+ "for srl_sequence in labels_srl:\n",
+ " srl_counter.update(srl_sequence)\n",
+ "\n",
+ "print(\"Distribusi label SRL:\")\n",
+ "for label, count in srl_counter.most_common():\n",
+ " print(f\"{label}: {count}\")\n",
+ "\n",
+ "# Hitung total label NER\n",
+ "ner_counter = Counter()\n",
+ "for ner_sequence in labels_ner:\n",
+ " ner_counter.update(ner_sequence)\n",
+ "\n",
+ "print(\"\\nDistribusi label NER:\")\n",
+ "for label, count in ner_counter.most_common():\n",
+ " print(f\"{label}: {count}\")\n",
+ "\n",
+ "# Total kalimat dan token\n",
+ "total_kalimat = len(data)\n",
+ "total_token = sum(len(item[\"tokens\"]) for item in data)\n",
+ "\n",
+ "print(\"\\nTotal kalimat:\", total_kalimat)\n",
+ "print(\"Total token:\", total_token)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "ac8eb374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tagging \n",
+ "words = sorted({w for s in sentences for w in s})\n",
+ "ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
+ "srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
+ "\n",
+ "word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
+ "word2idx[\"PAD\"], word2idx[\"UNK\"] = 0, 1\n",
+ "\n",
+ "tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}\n",
+ "tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}\n",
+ "idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
+ "idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "80356f1f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "39\n"
+ ]
+ }
+ ],
+ "source": [
+ "# encoding\n",
+ "\n",
+ "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
+ "y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
+ "y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
+ "\n",
+ "maxlen = max(len(s) for s in sentences)\n",
+ "print(maxlen)\n",
+ "\n",
+ "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
+ "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
+ "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
+ "\n",
+ "y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
+ "y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
+ "\n",
+ "X = np.array(X)\n",
+ "y_ner = np.array(y_ner)\n",
+ "y_srl = np.array(y_srl)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "fe219c96",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_ner_train, y_ner_test, y_srl_train, y_srl_test = train_test_split(\n",
+ " X, y_ner, y_srl, \n",
+ " test_size=0.20, \n",
+ " random_state=42,\n",
+ " shuffle=True \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a9636b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_6\"\n",
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+ "Epoch 1/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 10ms/step - loss: 1.8462 - ner_output_accuracy: 0.9063 - ner_output_loss: 0.7988 - srl_output_accuracy: 0.8035 - srl_output_loss: 1.0474 - val_loss: 0.8543 - val_ner_output_accuracy: 0.9196 - val_ner_output_loss: 0.3470 - val_srl_output_accuracy: 0.8340 - val_srl_output_loss: 0.5073\n",
+ "Epoch 2/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.7499 - ner_output_accuracy: 0.9186 - ner_output_loss: 0.3147 - srl_output_accuracy: 0.8535 - srl_output_loss: 0.4352 - val_loss: 0.6488 - val_ner_output_accuracy: 0.9325 - val_ner_output_loss: 0.2552 - val_srl_output_accuracy: 0.8750 - val_srl_output_loss: 0.3936\n",
+ "Epoch 3/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.5438 - ner_output_accuracy: 0.9362 - ner_output_loss: 0.2241 - srl_output_accuracy: 0.9011 - srl_output_loss: 0.3197 - val_loss: 0.5199 - val_ner_output_accuracy: 0.9534 - val_ner_output_loss: 0.1888 - val_srl_output_accuracy: 0.9000 - val_srl_output_loss: 0.3311\n",
+ "Epoch 4/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.3795 - ner_output_accuracy: 0.9596 - ner_output_loss: 0.1522 - srl_output_accuracy: 0.9356 - srl_output_loss: 0.2273 - val_loss: 0.4739 - val_ner_output_accuracy: 0.9631 - val_ner_output_loss: 0.1609 - val_srl_output_accuracy: 0.9090 - val_srl_output_loss: 0.3131\n",
+ "Epoch 5/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.2946 - ner_output_accuracy: 0.9706 - ner_output_loss: 0.1146 - srl_output_accuracy: 0.9507 - srl_output_loss: 0.1799 - val_loss: 0.4509 - val_ner_output_accuracy: 0.9678 - val_ner_output_loss: 0.1383 - val_srl_output_accuracy: 0.9114 - val_srl_output_loss: 0.3125\n",
+ "Epoch 6/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.2389 - ner_output_accuracy: 0.9789 - ner_output_loss: 0.0907 - srl_output_accuracy: 0.9578 - srl_output_loss: 0.1482 - val_loss: 0.4452 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1355 - val_srl_output_accuracy: 0.9165 - val_srl_output_loss: 0.3097\n",
+ "Epoch 7/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.1932 - ner_output_accuracy: 0.9821 - ner_output_loss: 0.0719 - srl_output_accuracy: 0.9663 - srl_output_loss: 0.1214 - val_loss: 0.4347 - val_ner_output_accuracy: 0.9704 - val_ner_output_loss: 0.1281 - val_srl_output_accuracy: 0.9228 - val_srl_output_loss: 0.3066\n",
+ "Epoch 8/30\n",
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+ "Epoch 9/30\n",
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+ "Epoch 10/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0948 - ner_output_accuracy: 0.9914 - ner_output_loss: 0.0358 - srl_output_accuracy: 0.9846 - srl_output_loss: 0.0590 - val_loss: 0.4690 - val_ner_output_accuracy: 0.9716 - val_ner_output_loss: 0.1320 - val_srl_output_accuracy: 0.9202 - val_srl_output_loss: 0.3370\n",
+ "Epoch 11/30\n",
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+ "Epoch 12/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0625 - ner_output_accuracy: 0.9952 - ner_output_loss: 0.0218 - srl_output_accuracy: 0.9902 - srl_output_loss: 0.0407 - val_loss: 0.5084 - val_ner_output_accuracy: 0.9712 - val_ner_output_loss: 0.1403 - val_srl_output_accuracy: 0.9213 - val_srl_output_loss: 0.3681\n",
+ "Epoch 13/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0495 - ner_output_accuracy: 0.9957 - ner_output_loss: 0.0173 - srl_output_accuracy: 0.9923 - srl_output_loss: 0.0321 - val_loss: 0.4905 - val_ner_output_accuracy: 0.9718 - val_ner_output_loss: 0.1420 - val_srl_output_accuracy: 0.9226 - val_srl_output_loss: 0.3485\n",
+ "Epoch 14/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0447 - ner_output_accuracy: 0.9957 - ner_output_loss: 0.0180 - srl_output_accuracy: 0.9934 - srl_output_loss: 0.0267 - val_loss: 0.5138 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1449 - val_srl_output_accuracy: 0.9205 - val_srl_output_loss: 0.3689\n",
+ "Epoch 15/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0353 - ner_output_accuracy: 0.9980 - ner_output_loss: 0.0116 - srl_output_accuracy: 0.9942 - srl_output_loss: 0.0236 - val_loss: 0.5168 - val_ner_output_accuracy: 0.9679 - val_ner_output_loss: 0.1456 - val_srl_output_accuracy: 0.9197 - val_srl_output_loss: 0.3712\n",
+ "Epoch 16/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0315 - ner_output_accuracy: 0.9969 - ner_output_loss: 0.0122 - srl_output_accuracy: 0.9957 - srl_output_loss: 0.0193 - val_loss: 0.5367 - val_ner_output_accuracy: 0.9694 - val_ner_output_loss: 0.1467 - val_srl_output_accuracy: 0.9231 - val_srl_output_loss: 0.3900\n",
+ "Epoch 17/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0229 - ner_output_accuracy: 0.9988 - ner_output_loss: 0.0073 - srl_output_accuracy: 0.9971 - srl_output_loss: 0.0157 - val_loss: 0.5494 - val_ner_output_accuracy: 0.9691 - val_ner_output_loss: 0.1525 - val_srl_output_accuracy: 0.9213 - val_srl_output_loss: 0.3969\n",
+ "Epoch 18/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0235 - ner_output_accuracy: 0.9985 - ner_output_loss: 0.0081 - srl_output_accuracy: 0.9971 - srl_output_loss: 0.0154 - val_loss: 0.5552 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1546 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.4006\n",
+ "Epoch 19/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0184 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0065 - srl_output_accuracy: 0.9976 - srl_output_loss: 0.0119 - val_loss: 0.5899 - val_ner_output_accuracy: 0.9686 - val_ner_output_loss: 0.1588 - val_srl_output_accuracy: 0.9196 - val_srl_output_loss: 0.4311\n",
+ "Epoch 20/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0157 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0057 - srl_output_accuracy: 0.9982 - srl_output_loss: 0.0100 - val_loss: 0.5719 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1592 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4126\n",
+ "Epoch 21/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0142 - ner_output_accuracy: 0.9987 - ner_output_loss: 0.0061 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0081 - val_loss: 0.5963 - val_ner_output_accuracy: 0.9705 - val_ner_output_loss: 0.1665 - val_srl_output_accuracy: 0.9220 - val_srl_output_loss: 0.4298\n",
+ "Epoch 22/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0114 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0047 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0067 - val_loss: 0.6074 - val_ner_output_accuracy: 0.9707 - val_ner_output_loss: 0.1674 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4400\n",
+ "Epoch 23/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0080 - ner_output_accuracy: 0.9994 - ner_output_loss: 0.0030 - srl_output_accuracy: 0.9993 - srl_output_loss: 0.0050 - val_loss: 0.6179 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1691 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4488\n",
+ "Epoch 24/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0094 - ner_output_accuracy: 0.9988 - ner_output_loss: 0.0038 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0057 - val_loss: 0.6226 - val_ner_output_accuracy: 0.9686 - val_ner_output_loss: 0.1738 - val_srl_output_accuracy: 0.9229 - val_srl_output_loss: 0.4488\n",
+ "Epoch 25/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0086 - ner_output_accuracy: 0.9993 - ner_output_loss: 0.0032 - srl_output_accuracy: 0.9991 - srl_output_loss: 0.0054 - val_loss: 0.6720 - val_ner_output_accuracy: 0.9696 - val_ner_output_loss: 0.1785 - val_srl_output_accuracy: 0.9232 - val_srl_output_loss: 0.4935\n",
+ "Epoch 26/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0094 - ner_output_accuracy: 0.9993 - ner_output_loss: 0.0032 - srl_output_accuracy: 0.9983 - srl_output_loss: 0.0062 - val_loss: 0.6702 - val_ner_output_accuracy: 0.9699 - val_ner_output_loss: 0.1800 - val_srl_output_accuracy: 0.9244 - val_srl_output_loss: 0.4902\n",
+ "Epoch 27/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0071 - ner_output_accuracy: 0.9996 - ner_output_loss: 0.0023 - srl_output_accuracy: 0.9989 - srl_output_loss: 0.0048 - val_loss: 0.6484 - val_ner_output_accuracy: 0.9708 - val_ner_output_loss: 0.1834 - val_srl_output_accuracy: 0.9221 - val_srl_output_loss: 0.4650\n",
+ "Epoch 28/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0092 - ner_output_accuracy: 0.9992 - ner_output_loss: 0.0036 - srl_output_accuracy: 0.9988 - srl_output_loss: 0.0055 - val_loss: 0.6592 - val_ner_output_accuracy: 0.9668 - val_ner_output_loss: 0.1848 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.4744\n",
+ "Epoch 29/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0071 - ner_output_accuracy: 0.9990 - ner_output_loss: 0.0034 - srl_output_accuracy: 0.9995 - srl_output_loss: 0.0037 - val_loss: 0.6624 - val_ner_output_accuracy: 0.9696 - val_ner_output_loss: 0.1908 - val_srl_output_accuracy: 0.9220 - val_srl_output_loss: 0.4716\n",
+ "Epoch 30/30\n",
+ "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0053 - ner_output_accuracy: 0.9994 - ner_output_loss: 0.0025 - srl_output_accuracy: 0.9995 - srl_output_loss: 0.0028 - val_loss: 0.6863 - val_ner_output_accuracy: 0.9668 - val_ner_output_loss: 0.1852 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.5011\n"
+ ]
+ }
+ ],
+ "source": [
+ "input_layer = Input(shape=(maxlen,))\n",
+ "embed = Embedding(len(word2idx), 64)(input_layer)\n",
+ "bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)\n",
+ "\n",
+ "ner_output = TimeDistributed(\n",
+ " Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
+ ")(bilstm)\n",
+ "srl_output = TimeDistributed(\n",
+ " Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
+ ")(bilstm)\n",
+ "\n",
+ "model = Model(inputs=input_layer, outputs=[ner_output, srl_output])\n",
+ "model.compile(\n",
+ " optimizer=\"adam\",\n",
+ " loss={\n",
+ " \"ner_output\": \"categorical_crossentropy\",\n",
+ " \"srl_output\": \"categorical_crossentropy\",\n",
+ " },\n",
+ " metrics={\n",
+ " \"ner_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
+ " \"srl_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "callbacks = [\n",
+ " tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",\n",
+ " patience=3,\n",
+ " restore_best_weights=True),\n",
+ " tf.keras.callbacks.ModelCheckpoint(\n",
+ " \"best_model.keras\", monitor=\"val_loss\", save_best_only=True)\n",
+ "]\n",
+ "\n",
+ "model.summary()\n",
+ "history = model.fit(\n",
+ " X_train, {\"ner_output\": y_ner_train, \"srl_output\": y_srl_train}, \n",
+ " validation_data=(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}),\n",
+ " batch_size=2,\n",
+ " epochs=30,\n",
+ " verbose=1\n",
+ ")\n",
+ "\n",
+ "# ---------- 6. Simpan artefak ----------\n",
+ "model.save(\"multi_task_lstm_ner_srl_model.keras\")\n",
+ "with open(\"word2idx.pkl\", \"wb\") as f:\n",
+ " pickle.dump(word2idx, f)\n",
+ "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
+ " pickle.dump(tag2idx_ner, f)\n",
+ "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
+ " pickle.dump(tag2idx_srl, f)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "82dfe902",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "ner_accuracy = history.history['ner_output_accuracy']\n",
+ "srl_accuracy = history.history['srl_output_accuracy']\n",
+ "epochs = list(range(1, len(ner_accuracy) + 1))\n",
+ "\n",
+ "ner_loss = history.history['ner_output_loss']\n",
+ "srl_loss = history.history['srl_output_loss']\n",
+ "val_ner_loss = history.history['val_ner_output_loss']\n",
+ "val_srl_loss = history.history['val_srl_output_loss']\n",
+ "\n",
+ "\n",
+ "# --------- Plot Accuracy ---------\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(epochs, ner_accuracy, marker='o', label='NER Accuracy')\n",
+ "plt.plot(epochs, srl_accuracy, marker='s', label='SRL Accuracy')\n",
+ "plt.title('Training Accuracy per Epoch')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.ylim(0.7, 1.0)\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(\"accuracy_plot.png\") # Simpan sebagai file gambar\n",
+ "plt.show()\n",
+ "\n",
+ "# --------- Plot Loss ---------\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(epochs, ner_loss, label='NER Loss (Train)')\n",
+ "plt.plot(epochs, val_ner_loss, label='NER Loss (Val)')\n",
+ "plt.plot(epochs, srl_loss, label='SRL Loss (Train)')\n",
+ "plt.plot(epochs, val_srl_loss, label='SRL Loss (Val)')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(\"loss_plot.png\") \n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "3a55990b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Metrics names: ['loss', 'compile_metrics', 'ner_output_loss', 'srl_output_loss']\n",
+ "loss: 0.6862505674362183\n",
+ "compile_metrics: 0.18517984449863434\n",
+ "ner_output_loss: 0.5010706782341003\n",
+ "srl_output_loss: 0.9668269157409668\n",
+ "[NER] Token-level Accuracy: 0.97\n",
+ "[SRL] Token-level Accuracy: 0.92\n",
+ "[NER] Classification Report (test set):\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " DATE 0.45 0.56 0.50 18\n",
+ " ETH 0.84 0.80 0.82 20\n",
+ " EVENT 0.47 0.58 0.52 26\n",
+ " LOC 0.63 0.74 0.68 121\n",
+ " MISC 0.00 0.00 0.00 3\n",
+ " ORG 0.38 0.25 0.30 12\n",
+ " PER 0.71 0.58 0.64 26\n",
+ " QUANT 0.77 0.77 0.77 43\n",
+ " RES 0.00 0.00 0.00 5\n",
+ " TERM 0.00 0.00 0.00 2\n",
+ " TIME 0.47 0.42 0.44 19\n",
+ "\n",
+ " micro avg 0.62 0.64 0.63 295\n",
+ " macro avg 0.43 0.43 0.42 295\n",
+ "weighted avg 0.60 0.64 0.62 295\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, msg_start, len(result))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# evaluation\n",
+ "\n",
+ "print(\"Metrics names:\", model.metrics_names)\n",
+ "\n",
+ "\n",
+ "results = model.evaluate(\n",
+ " X_test,\n",
+ " {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test},\n",
+ " verbose=0\n",
+ ")\n",
+ "for name, value in zip(model.metrics_names, results):\n",
+ " print(f\"{name}: {value}\")\n",
+ " \n",
+ "def token_level_accuracy(y_true, y_pred):\n",
+ " total, correct = 0, 0\n",
+ " for true_seq, pred_seq in zip(y_true, y_pred):\n",
+ " for t, p in zip(true_seq, pred_seq):\n",
+ " if t.sum() == 0: # skip PAD\n",
+ " continue\n",
+ " total += 1\n",
+ " if t.argmax() == p.argmax():\n",
+ " correct += 1\n",
+ " return correct / total\n",
+ "\n",
+ "\n",
+ "def decode(pred, true, idx2tag):\n",
+ " out_true, out_pred = [], []\n",
+ " for p_seq, t_seq in zip(pred, true):\n",
+ " t_labels, p_labels = [], []\n",
+ " for p_tok, t_tok in zip(p_seq, t_seq):\n",
+ " if t_tok.sum() == 0: # token PAD → lewati\n",
+ " continue\n",
+ " t_labels.append(idx2tag[t_tok.argmax()])\n",
+ " p_labels.append(idx2tag[p_tok.argmax()])\n",
+ " out_true.append(t_labels)\n",
+ " out_pred.append(p_labels)\n",
+ " return out_true, out_pred\n",
+ "\n",
+ "# prediksi hanya pada test set\n",
+ "y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
+ "\n",
+ "true_ner, pred_ner = decode(y_pred_ner, y_ner_test, idx2tag_ner)\n",
+ "\n",
+ "acc_ner = token_level_accuracy(y_ner_test, y_pred_ner)\n",
+ "acc_srl = token_level_accuracy(y_srl_test, y_pred_srl)\n",
+ "\n",
+ "print(f\"[NER] Token-level Accuracy: {acc_ner:.2f}\")\n",
+ "print(f\"[SRL] Token-level Accuracy: {acc_srl:.2f}\")\n",
+ "# print(idx2tag_ner)\n",
+ "print(\"[NER] Classification Report (test set):\")\n",
+ "print(classification_report(true_ner, pred_ner, digits=2))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "547d1533",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[SRL] Classification Report (test set):\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " ADV 0.00 0.00 0.00 6\n",
+ " BNF 0.00 0.00 0.00 1\n",
+ " CAU 0.10 0.17 0.12 12\n",
+ " COM 0.00 0.00 0.00 4\n",
+ " DIR 0.00 0.00 0.00 0\n",
+ " LOC 0.42 0.57 0.49 61\n",
+ " MNR 0.28 0.36 0.32 36\n",
+ " MOD 0.60 0.71 0.65 17\n",
+ " NEG 0.50 0.75 0.60 4\n",
+ " PRD 1.00 0.50 0.67 2\n",
+ " PRP 0.00 0.00 0.00 3\n",
+ " RG0 0.68 0.69 0.68 93\n",
+ " RG1 0.52 0.59 0.55 256\n",
+ " RG2 0.07 0.06 0.07 47\n",
+ " SRC 0.00 0.00 0.00 2\n",
+ " TIME 0.00 0.00 0.00 1\n",
+ " TMP 0.38 0.45 0.41 44\n",
+ " _ 0.83 0.69 0.75 166\n",
+ "\n",
+ " micro avg 0.52 0.55 0.53 755\n",
+ " macro avg 0.30 0.31 0.29 755\n",
+ "weighted avg 0.54 0.55 0.54 755\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRP seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MOD seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-CAU seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-TMP seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-COM seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-ADV seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MNR seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-NEG seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRD seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-SRC seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-BNF seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-DIR seems not to be NE tag.\n",
+ " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, msg_start, len(result))\n"
+ ]
+ }
+ ],
+ "source": [
+ "true_srl, pred_srl = decode(y_pred_srl, y_srl_test, idx2tag_srl)\n",
+ "# print(idx2tag_srl)\n",
+ "print(\"[SRL] Classification Report (test set):\")\n",
+ "print(classification_report(true_srl, pred_srl, digits=2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "8d44d51e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "flat_true_ner = [t for seq in true_ner for t in seq]\n",
+ "flat_pred_ner = [p for seq in pred_ner for p in seq]\n",
+ "\n",
+ "labels_ner = sorted(list(set(flat_true_ner + flat_pred_ner)))\n",
+ "cm_ner = confusion_matrix(flat_true_ner, flat_pred_ner, labels=labels_ner)\n",
+ "\n",
+ "plt.figure(figsize=(12, 10)) \n",
+ "\n",
+ "disp = ConfusionMatrixDisplay(confusion_matrix=cm_ner, display_labels=labels_ner)\n",
+ "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues, ax=plt.gca()) \n",
+ "\n",
+ "plt.title(\"Confusion Matrix NER\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "918dbefb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "flat_true_srl = [t for seq in true_srl for t in seq]\n",
+ "flat_pred_srl = [p for seq in pred_srl for p in seq]\n",
+ "\n",
+ "labels_srl = sorted(list(set(flat_true_srl + flat_pred_srl)))\n",
+ "cm_srl = confusion_matrix(flat_true_srl, flat_pred_srl, labels=labels_srl)\n",
+ "\n",
+ "plt.figure(figsize=(11, 9)) \n",
+ "\n",
+ "disp = ConfusionMatrixDisplay(confusion_matrix=cm_srl, display_labels=labels_srl)\n",
+ "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues, ax=plt.gca())\n",
+ "plt.title(\"Confusion Matrix SRL\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/normalize_text/normalize.json b/old/normalize_text/normalize.json
similarity index 100%
rename from normalize_text/normalize.json
rename to old/normalize_text/normalize.json
diff --git a/test.py b/old/test.py
similarity index 100%
rename from test.py
rename to old/test.py
diff --git a/testing.py b/old/testing.py
similarity index 100%
rename from testing.py
rename to old/testing.py
diff --git a/old/train.py b/old/train.py
new file mode 100644
index 0000000..f139e8d
--- /dev/null
+++ b/old/train.py
@@ -0,0 +1,107 @@
+import json, pickle
+import numpy as np
+from keras.models import Model
+from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense
+from keras.preprocessing.sequence import pad_sequences
+from keras.utils import to_categorical
+from seqeval.metrics import classification_report
+
+# ---------- 1. Muat data ----------
+with open("dataset/dataset_ner_srl.json", encoding="utf-8") as f:
+ data = json.load(f)
+
+sentences = [[tok.lower() for tok in item["tokens"]] for item in data]
+labels_ner = [item["labels_ner"] for item in data]
+labels_srl = [item["labels_srl"] for item in data]
+
+for i, label_seq in enumerate(labels_ner):
+ if "V" in label_seq:
+ print(f"Label 'V' ditemukan di index {i}: {label_seq}")
+
+# ---------- 2. Bangun vocab & label map ----------
+words = sorted({w for s in sentences for w in s})
+ner_tags = sorted({t for seq in labels_ner for t in seq})
+srl_tags = sorted({t for seq in labels_srl for t in seq})
+
+word2idx = {w: i + 2 for i, w in enumerate(words)}
+word2idx["PAD"], word2idx["UNK"] = 0, 1
+
+tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}
+tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}
+idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}
+idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}
+
+# ---------- 3. Encoding token & label ----------
+X = [[word2idx.get(w, word2idx["UNK"]) for w in s] for s in sentences]
+y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]
+y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]
+
+maxlen = max(len(seq) for seq in X)
+
+X = pad_sequences(X, maxlen=maxlen, padding="post", value=word2idx["PAD"])
+y_ner = pad_sequences(y_ner, maxlen=maxlen, padding="post", value=tag2idx_ner["O"])
+y_srl = pad_sequences(y_srl, maxlen=maxlen, padding="post", value=tag2idx_srl["O"])
+
+y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]
+y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]
+
+# cast ke np.array biar Keras happy
+X = np.array(X)
+y_ner = np.array(y_ner)
+y_srl = np.array(y_srl)
+
+# ---------- 4. Arsitektur BiLSTM multi‑task ----------
+input_layer = Input(shape=(maxlen,))
+embed = Embedding(len(word2idx), 64)(input_layer)
+bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)
+
+ner_output = TimeDistributed(
+ Dense(len(tag2idx_ner), activation="softmax"), name="ner_output"
+)(bilstm)
+srl_output = TimeDistributed(
+ Dense(len(tag2idx_srl), activation="softmax"), name="srl_output"
+)(bilstm)
+
+model = Model(inputs=input_layer, outputs=[ner_output, srl_output])
+model.compile(
+ optimizer="adam",
+ loss={
+ "ner_output": "categorical_crossentropy",
+ "srl_output": "categorical_crossentropy",
+ },
+ metrics={"ner_output": "accuracy", "srl_output": "accuracy"},
+)
+model.summary()
+
+# ---------- 5. Training ----------
+model.fit(
+ X, {"ner_output": y_ner, "srl_output": y_srl}, batch_size=2, epochs=10, verbose=1
+)
+
+# ---------- 6. Simpan artefak ----------
+model.save("NER_SRL/multi_task_bilstm_model.keras")
+with open("NER_SRL/word2idx.pkl", "wb") as f:
+ pickle.dump(word2idx, f)
+with open("NER_SRL/tag2idx_ner.pkl", "wb") as f:
+ pickle.dump(tag2idx_ner, f)
+with open("NER_SRL/tag2idx_srl.pkl", "wb") as f:
+ pickle.dump(tag2idx_srl, f)
+
+# ---------- 7. Evaluasi ----------
+y_pred_ner, y_pred_srl = model.predict(X, verbose=0)
+
+
+def decode(pred, true, idx2tag):
+ true_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in true]
+ pred_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in pred]
+ return true_tags, pred_tags
+
+
+true_ner, pred_ner = decode(y_pred_ner, y_ner, idx2tag_ner)
+true_srl, pred_srl = decode(y_pred_srl, y_srl, idx2tag_srl)
+
+print("\n📊 [NER] Classification Report:")
+print(classification_report(true_ner, pred_ner))
+
+print("\n📊 [SRL] Classification Report:")
+print(classification_report(true_srl, pred_srl))
diff --git a/train_multitask_lstm.py b/old/train_multitask_lstm.py
similarity index 100%
rename from train_multitask_lstm.py
rename to old/train_multitask_lstm.py
diff --git a/training_model.ipynb b/old/training_model.ipynb
similarity index 100%
rename from training_model.ipynb
rename to old/training_model.ipynb
diff --git a/uji.py b/old/uji.py
similarity index 100%
rename from uji.py
rename to old/uji.py
diff --git a/tokenizer.pkl b/tokenizer.pkl
deleted file mode 100644
index a0e3be5..0000000
Binary files a/tokenizer.pkl and /dev/null differ