diff --git a/NER/lstm_ner_qc.py b/NER/lstm_ner_qc.py new file mode 100644 index 0000000..50be877 --- /dev/null +++ b/NER/lstm_ner_qc.py @@ -0,0 +1,93 @@ +import json + +import numpy as np +from keras.models import Sequential +from keras.layers import ( + Embedding, + LSTM, + Dense, + TimeDistributed, + Bidirectional, + InputLayer, +) +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +from seqeval.metrics import classification_report +import pickle + + +with open("dataset/lstm_ner_dataset.json", "r", encoding="utf-8") as f: + data = json.load(f) + + +total_bLoc = 0 +total_o = 0 +total_b_per = 0 +total_i_per = 0 + +for idx, block in enumerate(data, start=1): + for token in block["labels"]: + if token == "B-LOC": + total_bLoc += 1 + elif token == "O": + total_o += 1 + elif token == "B-PER": + total_b_per += 1 + elif token == "I-PER": + total_i_per += 1 + +print("Total B-LOC:", total_bLoc) +print("Total O:", total_o) +print("Total B-PER:", total_b_per) +print("Total I-PER:", total_i_per) +print("Total B-PER + I-PER:", total_b_per + total_i_per) + +sentences = [[token.lower() for token in item["tokens"]] for item in data] +labels = [item["labels"] for item in data] + + +words = list(set(word for sentence in sentences for word in sentence)) +tags = list(set(tag for label_seq in labels for tag in label_seq)) + + +word2idx = {word: idx + 2 for idx, word in enumerate(words)} +word2idx["PAD"] = 0 +word2idx["UNK"] = 1 + +tag2idx = {tag: idx for idx, tag in enumerate(tags)} +idx2tag = {i: t for t, i in tag2idx.items()} + +X = [[word2idx.get(w, word2idx["UNK"]) for w in s] for s in sentences] +y = [[tag2idx[t] for t in ts] for ts in labels] + +maxlen = max(len(x) for x in X) +X = pad_sequences(X, maxlen=maxlen, padding="post", value=word2idx["PAD"]) +y = pad_sequences(y, maxlen=maxlen, padding="post", value=tag2idx["O"]) +y = [to_categorical(seq, num_classes=len(tag2idx)) for seq in y] + +model = Sequential() +model.add(InputLayer(input_shape=(maxlen,))) +model.add(Embedding(input_dim=len(word2idx), output_dim=64)) +model.add(Bidirectional(LSTM(units=64, return_sequences=True))) +model.add(TimeDistributed(Dense(len(tag2idx), activation="softmax"))) + +model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) +model.summary() + +model.fit(X, np.array(y), batch_size=2, epochs=10) + +model.save("NER/ner_bilstm_model.keras") + + +with open("NER/word2idx.pkl", "wb") as f: + pickle.dump(word2idx, f) + +with open("NER/tag2idx.pkl", "wb") as f: + pickle.dump(tag2idx, f) + + +y_true = [[idx2tag[np.argmax(token)] for token in seq] for seq in y] +y_pred = model.predict(X) +y_pred = [[idx2tag[np.argmax(token)] for token in seq] for seq in y_pred] + +print(classification_report(y_true, y_pred)) diff --git a/NER/ner_bilstm_model.keras b/NER/ner_bilstm_model.keras new file mode 100644 index 0000000..5932c29 Binary files /dev/null and b/NER/ner_bilstm_model.keras differ diff --git a/NER/tag2idx.pkl b/NER/tag2idx.pkl new file mode 100644 index 0000000..978ff9d Binary files /dev/null and b/NER/tag2idx.pkl differ diff --git a/NER/test_ner.py b/NER/test_ner.py new file mode 100644 index 0000000..52c850b --- /dev/null +++ b/NER/test_ner.py @@ -0,0 +1,39 @@ +import json +import numpy as np +import pickle + +from keras.models import load_model +from keras.preprocessing.sequence import pad_sequences + +model = load_model("NER/ner_bilstm_model.keras") + +with open("NER/word2idx.pkl", "rb") as f: + word2idx = pickle.load(f) + +with open("NER/tag2idx.pkl", "rb") as f: + tag2idx = pickle.load(f) + +idx2tag = {i: t for t, i in tag2idx.items()} + +maxlen = 100 + + +def predict_sentence(sentence): + tokens = sentence.strip().split() + x = [word2idx.get(w.lower(), word2idx["UNK"]) for w in tokens] + x = pad_sequences([x], maxlen=maxlen, padding="post", value=word2idx["PAD"]) + + preds = model.predict(x) + pred_labels = np.argmax(preds[0], axis=-1) + + print("Hasil prediksi NER:") + for token, label_idx in zip(tokens, pred_labels[: len(tokens)]): + print(f"{token}\t{idx2tag[label_idx]}") + + +if __name__ == "__main__": + try: + sentence = "dani datang ke indonesia" + predict_sentence(sentence) + except KeyboardInterrupt: + print("\n\nSelesai.") diff --git a/NER/word2idx.pkl b/NER/word2idx.pkl new file mode 100644 index 0000000..fc7e43e Binary files /dev/null and b/NER/word2idx.pkl differ diff --git a/combine_nlp_lstm.py b/combine_nlp_lstm.py new file mode 100644 index 0000000..44acbca --- /dev/null +++ b/combine_nlp_lstm.py @@ -0,0 +1,122 @@ +import numpy as np +import tensorflow as tf +import spacy +import nltk +from nltk.translate.bleu_score import sentence_bleu +from tensorflow.keras.layers import LSTM, Embedding, Dense, Input +from tensorflow.keras.models import Model +from transformers import TFT5ForConditionalGeneration, T5Tokenizer + +# === LOAD NLP MODEL === +nlp = spacy.load("en_core_web_sm") + + +# === PREPROCESSING FUNCTION === +def preprocess_text(text): + """Melakukan Named Entity Recognition dan Dependency Parsing""" + doc = nlp(text) + entities = {ent.text: ent.label_ for ent in doc.ents} + + # Print hasil Named Entity Recognition + print("\nNamed Entities Detected:") + for ent, label in entities.items(): + print(f"{ent}: {label}") + + return entities + + +# === LSTM MODEL (SEQUENCE-TO-SEQUENCE) === +embedding_dim = 128 +lstm_units = 256 +vocab_size = 5000 # Sesuaikan dengan dataset + +# Encoder +encoder_inputs = Input(shape=(None,)) +encoder_embedding = Embedding(vocab_size, embedding_dim, mask_zero=True)(encoder_inputs) +encoder_lstm = LSTM(lstm_units, return_state=True) +encoder_outputs, state_h, state_c = encoder_lstm(encoder_embedding) + +# Decoder +decoder_inputs = Input(shape=(None,)) +decoder_embedding = Embedding(vocab_size, embedding_dim, mask_zero=True)(decoder_inputs) +decoder_lstm = LSTM(lstm_units, return_sequences=True, return_state=True) +decoder_outputs, _, _ = decoder_lstm( + decoder_embedding, initial_state=[state_h, state_c] +) +decoder_dense = Dense(vocab_size, activation="softmax") +output = decoder_dense(decoder_outputs) + +# Model +lstm_model = Model([encoder_inputs, decoder_inputs], output) +lstm_model.compile(optimizer="adam", loss="sparse_categorical_crossentropy") + + +# === FUNCTION TO GENERATE QUESTION USING LSTM === +def generate_question_lstm(text, model, tokenizer, max_len=20): + """Generate soal menggunakan LSTM""" + input_seq = tokenizer.texts_to_sequences([text]) + input_seq = tf.keras.preprocessing.sequence.pad_sequences(input_seq, maxlen=max_len) + + generated_question = [] + start_token = tokenizer.word_index.get("", 1) + end_token = tokenizer.word_index.get("", 2) + + next_word = start_token + while next_word != end_token and len(generated_question) < max_len: + output = model.predict([input_seq, np.array([next_word])]) + next_word = np.argmax(output[0, -1, :]) + generated_question.append(tokenizer.index_word.get(next_word, "")) + + return " ".join(generated_question) + + +# === T5 TRANSFORMER MODEL (VERSI TENSORFLOW) === +t5_model_name = "t5-small" +t5_model = TFT5ForConditionalGeneration.from_pretrained(t5_model_name) +t5_tokenizer = T5Tokenizer.from_pretrained(t5_model_name) + + +def generate_question_t5(text): + """Generate soal menggunakan T5 Transformer versi TensorFlow""" + input_text = "generate question: " + text + input_ids = t5_tokenizer.encode( + input_text, return_tensors="tf" + ) # Gunakan TensorFlow + output = t5_model.generate(input_ids, max_length=50) + return t5_tokenizer.decode(output[0], skip_special_tokens=True) + + +# === BLEU SCORE EVALUATION === +def evaluate_bleu(reference, candidate): + """Menghitung BLEU Score antara pertanyaan asli dan yang dihasilkan""" + score = sentence_bleu([reference.split()], candidate.split()) + print(f"BLEU Score: {score:.4f}") + return score + + +# === MAIN EXECUTION === +if __name__ == "__main__": + paragraph = "Albert Einstein mengembangkan teori relativitas pada tahun 1905." + + # Preprocessing + print("\nπŸ› οΈ Preprocessing text...") + entities = preprocess_text(paragraph) + + # Generate soal menggunakan LSTM + print("\nπŸ”΅ Generating Question using LSTM (Dummy Model)...") + dummy_tokenizer = { + "texts_to_sequences": lambda x: [[1, 2, 3, 4]], + "index_word": {1: "apa", 2: "siapa", 3: "di", 4: "tahun"}, + } + question_lstm = generate_question_lstm(paragraph, lstm_model, dummy_tokenizer) + print(f"LSTM Generated Question: {question_lstm}") + + # Generate soal menggunakan T5 + print("\n🟒 Generating Question using T5 Transformer...") + question_t5 = generate_question_t5(paragraph) + print(f"T5 Generated Question: {question_t5}") + + # Evaluasi BLEU Score + reference_question = "Kapan teori relativitas dikembangkan?" + print("\nπŸ“Š Evaluating BLEU Score...") + evaluate_bleu(reference_question, question_t5) diff --git a/dataset/README.md b/dataset/README.md new file mode 100644 index 0000000..540cb3a --- /dev/null +++ b/dataset/README.md @@ -0,0 +1,34 @@ +NER + +B-PER -> person kata awal +I-PER -> person kata tengah dan akhir +B-LOC -> awal dari entitas lokasi +I-LOC -> tengah dan akhir dari entitas lokasi +B-ORG -> awal dari entitas organisasi +I-ORG -> tengah dan akhir dari entitas organisasi +B-MISC -> awal dari entitas lain lain Miscellaneous +I-MISC -> Lanjutan dari entitas lain-lain +B-DATE -> tanggal +B-TIME -> waktu +O -> token luar entitas + +Semantic Role Labeling (SRL) +ARG0 -> Agen (pelaku) – biasanya subjek +ARG1 -> Pasien atau tema – objek/yang dikenai aksi +ARG2 -> Arah, tujuan, hasil +ARG3 -> Lokasi awal (sumber) +ARG4 -> Penerima, tujuan akhir +ARG5 -> Alat atau instrumen + +ARGM-TMP -> Waktu (Temporal) +ARGM-LOC -> Lokasi (Spatial) +ARGM-MNR -> Cara (Manner) +ARGM-CAU -> Penyebab (Cause) +ARGM-EXT -> Derajat atau perbandingan (Extent) +ARGM-DIS -> Diskursus (Discourse) seperti β€œtetapi” +ARGM-NEG -> Negasi (Negation), misal "tidak" +ARGM-MOD -> Modality: bisa, harus, mungkin +ARGM-PRP -> Tujuan (Purpose) +ARGM-REC -> Penerima (Recipient, kadang mirip ARG4) +ARGM-COM -> Komitatif (dengan siapa) +ARGM-ADV -> Modifikasi umum diff --git a/dataset/dataset_combination.json b/dataset/dataset_combination.json new file mode 100644 index 0000000..7b6ea13 --- /dev/null +++ b/dataset/dataset_combination.json @@ -0,0 +1,56 @@ +[ + { + "tokens": [ + "Barack", + "Obama", + "melihat", + "bank", + "di", + "tepi", + "sungai", + "." + ], + "ner_labels": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC", "O"], + "srl_labels": [ + "B-ARG0", + "I-ARG0", + "B-V", + "B-ARG1", + "B-ARGM-LOC", + "I-ARGM-LOC", + "I-ARGM-LOC", + "O" + ], + "predicate": "melihat", + "wsd_targets": [ + { + "index": 3, + "word": "bank", + "sense": "river_bank", + "sense_id": "bank%1:17:00::" + } + ] + }, + { + "tokens": ["Dia", "pergi", "ke", "bank", "untuk", "menabung", "."], + "ner_labels": ["O", "O", "O", "O", "O", "O", "O"], + "srl_labels": [ + "B-ARG0", + "B-V", + "B-ARGM-DIR", + "I-ARGM-DIR", + "B-ARGM-PRP", + "I-ARGM-PRP", + "O" + ], + "predicate": "pergi", + "wsd_targets": [ + { + "index": 3, + "word": "bank", + "sense": "financial_institution", + "sense_id": "bank%1:14:00::" + } + ] + } +] diff --git a/dataset/dataset_ner_srl.json b/dataset/dataset_ner_srl.json new file mode 100644 index 0000000..9c5ee96 --- /dev/null +++ b/dataset/dataset_ner_srl.json @@ -0,0 +1,2018 @@ +[ + { + "tokens": ["Barack", "Obama", "adalah", "kanselir", "asal", "Hawaii"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"], + "labels_srl": [] + }, + { + "tokens": ["Greta", "Thunberg", "adalah", "pemain bola", "asal", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "datang", "dari", "Amerika"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "lahir", "di", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Taylor", "Swift", "datang", "dari", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Cristiano", + "Ronaldo", + "adalah", + "pemain bola", + "asal", + "Inggris" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "lahir", "di", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joe", "Biden", "adalah", "kanselir", "asal", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Elon", "Musk", "pernah", "tinggal", "di", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Taylor", "Swift", "datang", "dari", "Brazil"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joe", "Biden", "lahir", "di", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Cristiano", "Ronaldo", "adalah", "presiden", "asal", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "pernah", "tinggal", "di", "Amerika"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Joko", + "Widodo", + "bekerja", + "sebagai", + "presiden", + "di", + "Kanada" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Angela", + "Merkel", + "bekerja", + "sebagai", + "ilmuwan", + "di", + "Indonesia" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Lionel", "Messi", "lahir", "di", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Lionel", "Messi", "datang", "dari", "Perancis"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Emma", "Watson", "lahir", "di", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Cristiano", + "Ronaldo", + "adalah", + "ilmuwan", + "asal", + "Indonesia" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "datang", "dari", "Amerika"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Elon", "Musk", "pernah", "tinggal", "di", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "adalah", "ilmuwan", "asal", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "adalah", "aktivis", "asal", "Perancis"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Emma", "Watson", "pernah", "tinggal", "di", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Emma", "Watson", "datang", "dari", "Hawaii"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "pernah", "tinggal", "di", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Angela", + "Merkel", + "bekerja", + "sebagai", + "penyanyi", + "di", + "Brazil" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Elon", "Musk", "adalah", "aktivis", "asal", "Spanyol"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Emma", + "Watson", + "bekerja", + "sebagai", + "ilmuwan", + "di", + "Italia" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "lahir", "di", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Taylor", "Swift", "adalah", "presiden", "asal", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "lahir", "di", "Spanyol"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joe", "Biden", "pernah", "tinggal", "di", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Lionel", "Messi", "datang", "dari", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Emma", "Watson", "datang", "dari", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "datang", "dari", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Cristiano", "Ronaldo", "lahir", "di", "Brazil"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Elon", + "Musk", + "bekerja", + "sebagai", + "aktivis", + "di", + "Spanyol" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "pernah", "tinggal", "di", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Lionel", "Messi", "datang", "dari", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "datang", "dari", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "pernah", "tinggal", "di", "Amerika"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joe", "Biden", "lahir", "di", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Lionel", "Messi", "lahir", "di", "Jerman"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Emma", "Watson", "pernah", "tinggal", "di", "Hawaii"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Barack", + "Obama", + "bekerja", + "sebagai", + "kanselir", + "di", + "Kanada" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "datang", "dari", "Hawaii"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "lahir", "di", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Taylor", "Swift", "lahir", "di", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Barack", "Obama", "datang", "dari", "Perancis"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "adalah", "penyanyi", "asal", "Brazil"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "adalah", "aktivis", "asal", "Amerika"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "datang", "dari", "Kanada"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joko", "Widodo", "adalah", "penyanyi", "asal", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "lahir", "di", "Indonesia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": ["Barack", "Obama", "pernah", "tinggal", "di", "Hawaii"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "adalah", "pemain bola", "asal", "Italia"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "adalah", "pemain bola", "asal", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Taylor", + "Swift", + "bekerja", + "sebagai", + "aktivis", + "di", + "Brazil" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", "Merkel", "datang", "dari", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + "tokens": [ + "Joe", + "Biden", + "bekerja", + "sebagai", + "pemain bola", + "di", + "Perancis" + ], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Joe", "Biden", "adalah", "aktor", "asal", "Inggris"], + "labels_ner": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Angela", 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"O", + "O", + "O", + "O", + "O", + "B-LOC", + "I-LOC", + "O" + ] + }, + { + "tokens": ["dani", "pergi", "ke", "surabaya", "sore", "ini"], + "labels_ner": ["B-PER", "O", "O", "B-LOC", "B-TIME", "O"] + }, + { + "tokens": [ + "malam", + "nanti", + "jun", + "sedang", + "menonton", + "film", + "dengan", + "pacarnya" + ], + "labels_ner": ["B-TIME", "O", "B-PER", "O", "O", "O", "O", "B-PER"] + } +] diff --git a/dataset/lstm_ner_dataset.json b/dataset/lstm_ner_dataset.json new file mode 100644 index 0000000..7ff244b --- /dev/null +++ b/dataset/lstm_ner_dataset.json @@ -0,0 +1,1990 @@ +[ + { + "tokens": ["Barack", "Obama", "adalah", "kanselir", "asal", "Hawaii"], + "labels": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "adalah", "pemain bola", "asal", "Inggris"], + "labels": ["B-PER", "I-PER", "O", "O", "O", "B-LOC"] + }, + { + "tokens": ["Greta", "Thunberg", "datang", "dari", "Amerika"], + "labels": ["B-PER", "I-PER", "O", "O", "B-LOC"] + }, + { + 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"lebih", + "kuat" + ], + "labels": ["O", "O", "O", "O", "O", "B-MISC", "B-MISC", "O", "O", "O"] + }, + { + "tokens": [ + "sehingga", + "dengan", + "sendirinya", + "kehilangan", + "identitas", + "diri" + ], + "labels": ["O", "O", "O", "O", "B-MISC", "I-MISC"] + }, + { + "tokens": [ + "Jadi", + "bangsa", + "yang", + "gampang", + "meninggalkan", + "tradisi", + "nenek", + "moyangnya" + ], + "labels": ["O", "O", "O", "O", "O", "B-MISC", "O", "O"] + }, + { + "tokens": [ + "akan", + "mudah", + "didikte", + "oleh", + "budaya", + "dominan", + "dari", + "luar", + "yang", + "bukan", + "miliknya" + ], + "labels": [ + "O", + "O", + "O", + "O", + "B-MISC", + "I-MISC", + "O", + "B-LOC", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Kita", + "bisa", + "belajar", + "banyak", + "dari", + "keberhasilan", + "dan", + "capaian", + "prestasi", + "terbaik", + "dari", + "pendahulu", + "kita" + ], + "labels": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"] + }, + { + "tokens": [ + "Sebaliknya", + "kita", + "juga", + "belajar", + "dari", + "kegagalan", + "mereka", + "yang", + "telah", + "menimbulkan", + "malapetaka", + "bagi", + "dirinya", + "atau", + "bagi", + "banyak", + "orang" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": ["Untuk", "memetik", "pelajaran", "dari", "uraian", "ini"], + "labels": ["O", "O", "O", "O", "O", "O"] + }, + { + "tokens": [ + "dapat", + "kita", + "katakan", + "bahwa", + "nilai", + "terpenting", + "dalam", + "pembelajaran", + "sejarah", + "tentang", + "zaman", + "praaksara" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-TIME", + "I-TIME" + ] + }, + { + "tokens": [ + "dan", + "sesudahnya", + "ada", + "dua", + "yaitu", + "sebagai", + "inspirasi", + "untuk", + "pengembangan", + "nalar", + "kehidupan", + "dan", + "sebagai", + "peringatan" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Selebihnya", + "kecerdasan", + "dan", + "pikiran-pikiran", + "kritis", + "lah", + "yang", + "akan", + "menerangi", + "kehidupan", + "masa", + "kini", + "dan", + "masa", + "depan" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-TIME", + "I-TIME", + "O", + "B-TIME", + "I-TIME" + ] + }, + { + "tokens": [ + "Sekarang", + "muncul", + "pertanyaan", + "sejak", + "kapan", + "zaman", + "praaksara", + "berakhir" + ], + "labels": ["O", "O", "O", "O", "O", "B-TIME", "I-TIME", "O"] + }, + { + "tokens": [ + "Sudah", + "barang", + "tentu", + "zaman", + "praaksara", + "itu", + "berakhir", + "setelah", + "kehidupan", + "manusia", + "mulai", + "mengenal", + "tulisan" + ], + "labels": [ + "O", + "O", + "O", + "B-TIME", + "I-TIME", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Terkait", + "dengan", + "masa", + "berakhirnya", + "zaman", + "praaksara", + "masing-masing", + "tempat", + "akan", + "berbeda" + ], + "labels": ["O", "O", "O", "O", "B-TIME", "I-TIME", "O", "O", "O", "O"] + }, + { + "tokens": [ + "Penduduk", + "di", + "Kepulauan", + "Indonesia", + "baru", + "memasuki", + "masa", + "aksara", + "sekitar", + "abad", + "ke-5", + "M" + ], + "labels": [ + "O", + "O", + "B-LOC", + "I-LOC", + "O", + "O", + "B-TIME", + "I-TIME", + "O", + "B-TIME", + "I-TIME", + "I-TIME" + ] + }, + { + "tokens": [ + "Hal", + "ini", + "jauh", + "lebih", + "terlambat", + "bila", + "dibandingkan", + "di", + "tempat", + "lain", + "misalnya", + "Mesir", + "dan", + "Mesopotamia" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-LOC", + "O", + "B-LOC" + ] + }, + { + "tokens": [ + "yang", + "sudah", + "mengenal", + "tulisan", + "sejak", + "sekitar", + "tahun", + "3000", + "SM" + ], + "labels": ["O", "O", "O", "O", "O", "O", "B-TIME", "I-TIME", "I-TIME"] + }, + { + "tokens": [ + "Fakta-fakta", + "masa", + "aksara", + "di", + "Kepulauan", + "Indonesia", + "dihubungkan", + "dengan", + "temuan", + "prasasti", + "peninggalan", + "kerajaan", + "tua" + ], + "labels": [ + "O", + "B-TIME", + "I-TIME", + "O", + "B-LOC", + "I-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "seperti", + "Kerajaan", + "Kutai", + "di", + "Muara", + "Kaman", + "Kalimantan", + "Timur" + ], + "labels": ["O", "O", "B-ORG", "O", "B-LOC", "I-LOC", "I-LOC", "I-LOC"] + }, + + { + "tokens": [ + "Bumi", + "kita", + "yang", + "terhampar", + "luas", + "ini", + "diciptakan", + "Tuhan", + "Yang", + "Maha", + "Pencipta", + "untuk", + "kehidupan", + "dan", + "kepentingan", + "hidup", + "manusia" + ], + "labels": [ + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "B-PER", + "I-PER", + "I-PER", + "I-PER", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Di", + "bumi", + "ini", + "hidup", + "berbagai", + "flora", + "dan", + "fauna", + "serta", + "tempat", + "bersemainya", + "manusia", + "dengan", + "keturunannya" + ], + "labels": [ + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Di", + "bumi", + "ini", + "kita", + "bisa", + "menyaksikan", + "keindahan", + "alam", + "kita", + "bisa", + "beraktivitas", + "dan", + "berikhtiar", + "memenuhi", + "kebutuhan", + "hidup", + "kita" + ], + "labels": [ + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Namun", + "harus", + "dipahami", + "bahwa", + "bumi", + "kita", + "juga", + "sering", + "menimbulkan", + "bencana" + ], + "labels": ["O", "O", "O", "O", "B-LOC", "O", "O", "O", "O", "O"] + }, + { + "tokens": [ + "Sebagai", + "contoh", + "munculnya", + "aktivitas", + "lempeng", + "bumi", + "yang", + "kemudian", + "melahirkan", + "gempa", + "baik", + "tektonis", + "maupun", + "vulkanis", + "bahkan", + "sampai", + "menimbulkan", + "tsunami" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Sebagai", + "contoh", + "tentu", + "kamu", + "masih", + "ingat", + "gempa", + "dan", + "tsunami", + "yang", + "terjadi", + "di", + "Aceh" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-LOC" + ] + }, + { + "tokens": [ + "gempa", + "di", + "Yogyakarta", + "di", + "Papua", + "dan", + "beberapa", + "daerah", + "lain", + "termasuk", + "beberapa", + "gunung", + "api", + "meletus" + ], + "labels": [ + "O", + "O", + "B-LOC", + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ] + }, + { + "tokens": [ + "Bencana", + "tersebut", + "telah", + "mengakibatkan", + "ribuan", + "nyawa", + "hilang", + "dan", + "harta", + "benda", + "melayang" + ], + "labels": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"] + }, + { + "tokens": [ + "Fenomena", + "alam", + "yang", + "terjadi", + "itu", + "merupakan", + "bagian", + "tak", + "terpisahkan", + "dari", + "aktivitas", + "panjang", + "bumi", + "kita", + "sejak", + "proses", + "terjadinya", + "alam", + "semesta", + "ratusan", + "ribuan", + "bahkan", + "juta", + "tahun", + "yang", + "lalu" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "B-TIME", + "I-TIME", + "I-TIME", + "I-TIME", + "I-TIME", + "I-TIME", + "O", + "O" + ] + }, + { + "tokens": [ + "Proses", + "tersebut", + "secara", + "geologis", + "mengalami", + "beberapa", + "tahapan", + "atau", + "pembabakan", + "waktu" + ], + "labels": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"] + }, + { + "tokens": [ + "Berikut", + "ini", + "kita", + "mencoba", + "menelaah", + "tentang", + "pembabakan", + "waktu", + "alam", + "secara", + "geologis", + "dan", + "terbentuknya", + "Kepulauan", + "Indonesia", + "terbentuk" + ], + "labels": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "B-LOC", + "I-LOC", + "O" + ] + }, + { + "tokens": ["dani", "pergi", "ke", "surabaya", "sore", "ini"], + "labels": ["B-PER", "O", "O", "B-LOC", "B-TIME", "O"] + }, + { + "tokens": [ + "malam", + "nanti", + "jun", + "sedang", + "menonton", + "film", + "dengan", + "pacarnya" + ], + "labels": ["B-TIME", "O", "B-PER", "O", "O", "O", "O", "B-PER"] + } +] diff --git a/dataset/lstm_sentiment_analys.json b/dataset/lstm_sentiment_analys.json new file mode 100644 index 0000000..e69de29 diff --git a/lstm_multi_output_model.keras b/lstm_multi_output_model.keras index 00a6876..f9f13fa 100644 Binary files a/lstm_multi_output_model.keras and b/lstm_multi_output_model.keras differ diff --git a/lstm_multi_output_ner_model.keras b/lstm_multi_output_ner_model.keras new file mode 100644 index 0000000..5d1ef4c Binary files /dev/null and b/lstm_multi_output_ner_model.keras differ diff --git a/ner_lstm.ipynb b/ner_lstm.ipynb new file mode 100644 index 0000000..9c43f2b --- /dev/null +++ b/ner_lstm.ipynb @@ -0,0 +1,640 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[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 averaged_perceptron_tagger to\n", + "[nltk_data] /home/akeon/nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n", + "[nltk_data] Downloading package maxent_ne_chunker to\n", + "[nltk_data] /home/akeon/nltk_data...\n", + "[nltk_data] Package maxent_ne_chunker is already up-to-date!\n", + "[nltk_data] Downloading package words to /home/akeon/nltk_data...\n", + "[nltk_data] Package words is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "import string, re, pickle\n", + "import numpy as np\n", + "from nltk.tokenize import word_tokenize\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Embedding, LSTM, Bidirectional, TimeDistributed, Dense\n", + "import spacy\n", + "import nltk\n", + "\n", + "\n", + "nltk.download ('punkt')\n", + "nltk.download ('averaged_perceptron_tagger')\n", + "nltk.download ('maxent_ne_chunker')\n", + "nltk.download ('words')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kerajaan Aceh PER\n", + "Iskandar Muda PER\n", + "ke-17 MISC\n", + "Islam MISC\n", + "Nusantara LOC\n" + ] + } + ], + "source": [ + "import spacy\n", + "\n", + "# Load model multibahasa yang mendukung Indonesia\n", + "nlp = spacy.load(\"xx_ent_wiki_sm\")\n", + "\n", + "# Contoh teks\n", + "text = \"Kerajaan Aceh mencapai puncak kejayaannya di bawah pemerintahan Sultan Iskandar Muda pada abad ke-17. Aceh menjadi pusat perdagangan dan kebudayaan Islam di wilayah Nusantara.\"\n", + "\n", + "# Proses teks dengan model\n", + "doc = nlp(text)\n", + "\n", + "# Cetak entitas yang dikenali\n", + "for ent in doc.ents:\n", + " print(ent.text, ent.label_)\n", + " \n", + "\n", + "# def generate_ner_context(text):\n", + "# # Load the pretrained spaCy model (small Indo model or use multilingual model if needed)\n", + "# nlp = spacy.load(\"xx_ent_wiki_sm\") # Load multilingual model\n", + " \n", + "# # Process the text\n", + "# doc = nlp(text)\n", + " \n", + "# # Tokenization and Named Entity Recognition (NER)\n", + "# tokens = [token.text for token in doc]\n", + "# ner_tags = []\n", + "# for token in doc:\n", + "# if token.ent_type_:\n", + "# ner_tags.append(f\"B-{token.ent_type_}\")\n", + "# else:\n", + "# ner_tags.append(\"O\")\n", + " \n", + "# return tokens, ner_tags\n", + "\n", + "# # Example input context\n", + "# context = \"Perang Diponegoro berlangsung dari tahun 1825 hingga 1830. Perang ini dipimpin oleh Pangeran Diponegoro melawan pemerintah kolonial Belanda di Jawa Tengah.\"\n", + "\n", + "# # Generate NER and tokens\n", + "# tokens, ner_tags = generate_ner_context(context)\n", + "\n", + "# # Construct the JSON result\n", + "# result = {\n", + "# \"context\": context,\n", + "# \"context_tokens\": tokens,\n", + "# \"context_ner\": ner_tags,\n", + "# \"question_posibility\": [\n", + "# {\n", + "# \"type\": \"true_false\",\n", + "# \"question\": \"Perang Diponegoro berlangsung selama lima tahun.\",\n", + "# \"answer\": \"True\"\n", + "# },\n", + "# {\n", + "# \"type\": \"true_false\",\n", + "# \"question\": \"Perang Diponegoro berakhir pada tahun 1850.\",\n", + "# \"answer\": \"False\"\n", + "# }\n", + "# ]\n", + "# }\n", + "\n", + "# # Output the result\n", + "# import json\n", + "# print(json.dumps(result, indent=4, ensure_ascii=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = [\n", + " {\n", + " \"context\": \"Pertempuran Surabaya terjadi pada 10 November 1945 antara pasukan Indonesia melawan pasukan sekutu Inggris yang berusaha mengambil alih kota setelah Jepang menyerah dalam Perang Dunia II. Pertempuran ini dikenang sebagai Hari Pahlawan di Indonesia.\",\n", + " \"context_tokens\": [\n", + " \"Pertempuran\", \"Surabaya\", \"terjadi\", \"pada\", \"10\", \"November\", \"1945\",\n", + " \"antara\", \"pasukan\", \"Indonesia\", \"melawan\", \"pasukan\", \"sekutu\", \"Inggris\",\n", + " \"yang\", \"berusaha\", \"mengambil\", \"alih\", \"kota\", \"setelah\", \"Jepang\", \"menyerah\",\n", + " \"dalam\", \"Perang\", \"Dunia\", \"II\", \".\", \"Pertempuran\", \"ini\", \"dikenang\", \"sebagai\",\n", + " \"Hari\", \"Pahlawan\", \"di\", \"Indonesia\", \".\"\n", + " ],\n", + " \"context_ner\": [\n", + " \"O\", \"B-LOC\", \"O\", \"O\", \"B-DATE\", \"I-DATE\", \"I-DATE\",\n", + " \"O\", \"O\", \"B-LOC\", \"O\", \"O\", \"O\", \"B-LOC\",\n", + " \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"B-LOC\", \"O\",\n", + " \"O\", \"B-MISC\", \"I-MISC\", \"I-MISC\", \"O\", \"O\", \"O\", \"O\", \"O\",\n", + " \"O\", \"O\", \"O\", \"B-LOC\", \"O\"\n", + " ],\n", + " \"question_posibility\": [\n", + " {\n", + " \"type\": \"fill_in_the_blank\",\n", + " \"question\": \"Pertempuran Surabaya terjadi pada tanggal _______.\",\n", + " \"answer\": \"10 November 1945\"\n", + " },\n", + " {\n", + " \"type\": \"multiple_choice\",\n", + " \"question\": \"Pasukan yang dihadapi Indonesia dalam Pertempuran Surabaya berasal dari negara apa?\",\n", + " \"options\": [\"Jepang\", \"Belanda\", \"Inggris\", \"Australia\"],\n", + " \"answer\": \"Inggris\"\n", + " },\n", + " {\n", + " \"type\": \"true_false\",\n", + " \"question\": \"Pertempuran Surabaya diperingati sebagai Hari Pahlawan di Indonesia.\",\n", + " \"answer\": \"True\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"context\": \"Perang Diponegoro berlangsung dari tahun 1825 hingga 1830. Perang ini dipimpin oleh Pangeran Diponegoro melawan pemerintah kolonial Belanda di Jawa Tengah.\",\n", + " \"context_tokens\": [\n", + " \"Perang\", \"Diponegoro\", \"berlangsung\", \"dari\", \"tahun\", \"1825\", \"hingga\", \"1830\", \".\",\n", + " \"Perang\", \"ini\", \"dipimpin\", \"oleh\", \"Pangeran\", \"Diponegoro\", \"melawan\",\n", + " \"pemerintah\", \"kolonial\", \"Belanda\", \"di\", \"Jawa\", \"Tengah\", \".\"\n", + " ],\n", + " \"context_ner\": [\n", + " \"O\", \"B-PER\", \"O\", \"O\", \"O\", \"B-DATE\", \"O\", \"B-DATE\", \"O\",\n", + " \"O\", \"O\", \"O\", \"O\", \"B-PER\", \"I-PER\", \"O\",\n", + " \"O\", \"O\", \"B-LOC\", \"O\", \"O\", \"B-LOC\", \"O\"\n", + " ],\n", + " \"question_posibility\": [\n", + " {\n", + " \"type\": \"true_false\",\n", + " \"question\": \"Perang Diponegoro berlangsung selama lima tahun.\",\n", + " \"answer\": \"True\"\n", + " },\n", + " {\n", + " \"type\": \"true_false\",\n", + " \"question\": \"Perang Diponegoro berakhir pada tahun 1850.\",\n", + " \"answer\": \"False\"\n", + " }\n", + " ]\n", + " }\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "contexts_tokens = []\n", + "contexts_ner = []\n", + "questions = []\n", + "answers = []\n", + "qtypes = []\n", + "\n", + "for entry in dataset:\n", + " contexts_tokens.append(entry[\"context_tokens\"])\n", + " contexts_ner.append(entry[\"context_ner\"])\n", + " qa = entry[\"question_posibility\"][0] # pilih soal pertama\n", + " questions.append(qa[\"question\"])\n", + " answers.append(qa[\"answer\"])\n", + " qtypes.append(qa[\"type\"]) # misalnya \"fill_in_the_blank\"\n", + "\n", + "# ----------------------------\n", + "# Tokenisasi untuk Kata\n", + "# ----------------------------\n", + "# Kita gabungkan semua teks dari context (dari tokens), pertanyaan, dan jawaban\n", + "all_texts = []\n", + "for tokens in contexts_tokens:\n", + " all_texts.append(\" \".join(tokens))\n", + "all_texts += questions\n", + "all_texts += answers\n", + "\n", + "tokenizer = Tokenizer(oov_token=\"\")\n", + "tokenizer.fit_on_texts(all_texts)\n", + "\n", + "# Ubah context_tokens menjadi sequence angka\n", + "context_sequences = [tokenizer.texts_to_sequences([\" \".join(tokens)])[0] for tokens in contexts_tokens]\n", + "question_sequences = tokenizer.texts_to_sequences(questions)\n", + "answer_sequences = tokenizer.texts_to_sequences(answers)\n", + "\n", + "# Padding sequence ke panjang tetap\n", + "MAX_LENGTH = 50 # sesuaikan dengan panjang teks maksimum yang diinginkan\n", + "context_padded = pad_sequences(context_sequences, maxlen=MAX_LENGTH, padding=\"post\", truncating=\"post\")\n", + "question_padded = pad_sequences(question_sequences, maxlen=MAX_LENGTH, padding=\"post\", truncating=\"post\")\n", + "answer_padded = pad_sequences(answer_sequences, maxlen=MAX_LENGTH, padding=\"post\", truncating=\"post\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# ----------------------------\n", + "# Tokenisasi untuk Label NER\n", + "# ----------------------------\n", + "# Kumpulkan semua label NER untuk membangun mapping label ke indeks\n", + "all_ner_labels = []\n", + "for ner_seq in contexts_ner:\n", + " all_ner_labels += ner_seq\n", + "\n", + "ner_labels_set = sorted(list(set(all_ner_labels)))\n", + "# Contoh: ['B-DATE', 'B-LOC', 'B-MISC', 'B-PER', 'I-DATE', 'I-MISC', 'I-PER', 'O']\n", + "ner2idx = {label: idx for idx, label in enumerate(ner_labels_set)}\n", + "idx2ner = {idx: label for label, idx in ner2idx.items()}\n", + "\n", + "# Ubah label NER ke dalam bentuk sequence angka\n", + "ner_sequences = []\n", + "for ner_seq in contexts_ner:\n", + " seq = [ner2idx[label] for label in ner_seq]\n", + " ner_sequences.append(seq)\n", + "\n", + "# Padding sequence label NER (gunakan nilai default misal label \"O\")\n", + "ner_padded = pad_sequences(ner_sequences, maxlen=MAX_LENGTH, padding=\"post\", truncating=\"post\", value=ner2idx[\"O\"])\n", + "\n", + "# ----------------------------\n", + "# Label Tipe Soal\n", + "# ----------------------------\n", + "qtype_dict = {\"fill_in_the_blank\": 0, \"true_false\": 1, \"multiple_choice\": 2}\n", + "qtype_labels = np.array([qtype_dict[q] for q in qtypes])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# ----------------------------\n", + "# Split Data Training dan Validation\n", + "# ----------------------------\n", + "(context_train, context_val, \n", + " question_train, question_val, \n", + " answer_train, answer_val, \n", + " qtype_train, qtype_val,\n", + " ner_train, ner_val) = train_test_split(\n", + " context_padded, question_padded, answer_padded, qtype_labels, ner_padded,\n", + " test_size=0.2, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-23 15:06:59.338033: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] 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",
+       "β”‚ context_input       β”‚ (None, 50)        β”‚          0 β”‚ -                 β”‚\n",
+       "β”‚ (InputLayer)        β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ question_decoder_i… β”‚ (None, 50)        β”‚          0 β”‚ -                 β”‚\n",
+       "β”‚ (InputLayer)        β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ context_embedding   β”‚ (None, 50, 300)   β”‚     15,600 β”‚ context_input[0]… β”‚\n",
+       "β”‚ (Embedding)         β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ not_equal           β”‚ (None, 50)        β”‚          0 β”‚ context_input[0]… β”‚\n",
+       "β”‚ (NotEqual)          β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ question_embedding  β”‚ (None, 50, 300)   β”‚     15,600 β”‚ question_decoder… β”‚\n",
+       "β”‚ (Embedding)         β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ encoder_lstm (LSTM) β”‚ [(None, 256),     β”‚    570,368 β”‚ context_embeddin… β”‚\n",
+       "β”‚                     β”‚ (None, 256),      β”‚            β”‚ not_equal[0][0]   β”‚\n",
+       "β”‚                     β”‚ (None, 256)]      β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ question_lstm       β”‚ [(None, 50, 256), β”‚    570,368 β”‚ question_embeddi… β”‚\n",
+       "β”‚ (LSTM)              β”‚ (None, 256),      β”‚            β”‚ encoder_lstm[0][… β”‚\n",
+       "β”‚                     β”‚ (None, 256)]      β”‚            β”‚ encoder_lstm[0][… β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ answer_lstm (LSTM)  β”‚ [(None, 50, 256), β”‚    570,368 β”‚ context_embeddin… β”‚\n",
+       "β”‚                     β”‚ (None, 256),      β”‚            β”‚ encoder_lstm[0][… β”‚\n",
+       "β”‚                     β”‚ (None, 256)]      β”‚            β”‚ encoder_lstm[0][… β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ dense (Dense)       β”‚ (None, 128)       β”‚     32,896 β”‚ encoder_lstm[0][… β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ ner_lstm            β”‚ (None, 50, 512)   β”‚  1,140,736 β”‚ context_embeddin… β”‚\n",
+       "β”‚ (Bidirectional)     β”‚                   β”‚            β”‚ not_equal[0][0]   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ question_output     β”‚ (None, 50, 52)    β”‚     13,364 β”‚ question_lstm[0]… β”‚\n",
+       "β”‚ (Dense)             β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ answer_output       β”‚ (None, 50, 52)    β”‚     13,364 β”‚ answer_lstm[0][0] β”‚\n",
+       "β”‚ (Dense)             β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ question_type_outp… β”‚ (None, 3)         β”‚        387 β”‚ dense[0][0]       β”‚\n",
+       "β”‚ (Dense)             β”‚                   β”‚            β”‚                   β”‚\n",
+       "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n",
+       "β”‚ ner_output          β”‚ (None, 50, 8)     β”‚      4,104 β”‚ ner_lstm[0][0],   β”‚\n",
+       "β”‚ (TimeDistributed)   β”‚                   β”‚            β”‚ not_equal[0][0]   β”‚\n",
+       "β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\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", + "┑━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", + "β”‚ context_input β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) β”‚ \u001b[38;5;34m0\u001b[0m β”‚ - β”‚\n", + "β”‚ (\u001b[38;5;33mInputLayer\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ question_decoder_i… β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) β”‚ \u001b[38;5;34m0\u001b[0m β”‚ - β”‚\n", + "β”‚ (\u001b[38;5;33mInputLayer\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ context_embedding β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m300\u001b[0m) β”‚ \u001b[38;5;34m15,600\u001b[0m β”‚ context_input[\u001b[38;5;34m0\u001b[0m]… β”‚\n", + "β”‚ (\u001b[38;5;33mEmbedding\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ not_equal β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) β”‚ \u001b[38;5;34m0\u001b[0m β”‚ context_input[\u001b[38;5;34m0\u001b[0m]… β”‚\n", + "β”‚ (\u001b[38;5;33mNotEqual\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ question_embedding β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m300\u001b[0m) β”‚ \u001b[38;5;34m15,600\u001b[0m β”‚ question_decoder… β”‚\n", + "β”‚ (\u001b[38;5;33mEmbedding\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ encoder_lstm (\u001b[38;5;33mLSTM\u001b[0m) β”‚ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ \u001b[38;5;34m570,368\u001b[0m β”‚ context_embeddin… β”‚\n", + "β”‚ β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ β”‚ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] β”‚\n", + "β”‚ β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ question_lstm β”‚ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ \u001b[38;5;34m570,368\u001b[0m β”‚ question_embeddi… β”‚\n", + "β”‚ (\u001b[38;5;33mLSTM\u001b[0m) β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ β”‚ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m β”‚\n", + "β”‚ β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] β”‚ β”‚ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ answer_lstm (\u001b[38;5;33mLSTM\u001b[0m) β”‚ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ \u001b[38;5;34m570,368\u001b[0m β”‚ context_embeddin… β”‚\n", + "β”‚ β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), β”‚ β”‚ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m β”‚\n", + "β”‚ β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] β”‚ β”‚ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ dense (\u001b[38;5;33mDense\u001b[0m) β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) β”‚ \u001b[38;5;34m32,896\u001b[0m β”‚ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ ner_lstm β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m512\u001b[0m) β”‚ \u001b[38;5;34m1,140,736\u001b[0m β”‚ context_embeddin… β”‚\n", + "β”‚ (\u001b[38;5;33mBidirectional\u001b[0m) β”‚ β”‚ β”‚ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ question_output β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m52\u001b[0m) β”‚ \u001b[38;5;34m13,364\u001b[0m β”‚ question_lstm[\u001b[38;5;34m0\u001b[0m]… β”‚\n", + "β”‚ (\u001b[38;5;33mDense\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ answer_output β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m52\u001b[0m) β”‚ \u001b[38;5;34m13,364\u001b[0m β”‚ answer_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] β”‚\n", + "β”‚ (\u001b[38;5;33mDense\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ question_type_outp… β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) β”‚ \u001b[38;5;34m387\u001b[0m β”‚ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] β”‚\n", + "β”‚ (\u001b[38;5;33mDense\u001b[0m) β”‚ β”‚ β”‚ β”‚\n", + "β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\n", + "β”‚ ner_output β”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m8\u001b[0m) β”‚ \u001b[38;5;34m4,104\u001b[0m β”‚ ner_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], β”‚\n", + "β”‚ (\u001b[38;5;33mTimeDistributed\u001b[0m) β”‚ β”‚ β”‚ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] β”‚\n", + "β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 2,947,155 (11.24 MB)\n",
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 Trainable params: 2,947,155 (11.24 MB)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-23 15:07:06.004502: E tensorflow/core/util/util.cc:131] oneDNN supports DT_BOOL only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - answer_output_accuracy: 0.0000e+00 - answer_output_loss: 3.9473 - loss: 11.0828 - ner_output_accuracy: 0.0800 - ner_output_loss: 2.0766 - question_output_accuracy: 0.0400 - question_output_loss: 3.9452 - question_type_output_accuracy: 0.0000e+00 - question_type_output_loss: 1.1138 - val_answer_output_accuracy: 0.3200 - val_answer_output_loss: 3.9260 - val_loss: 11.0343 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 2.0489 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9441 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.1153\n", + "Epoch 2/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - answer_output_accuracy: 0.6800 - answer_output_loss: 3.8844 - loss: 10.8637 - ner_output_accuracy: 0.7800 - ner_output_loss: 2.0194 - question_output_accuracy: 0.0800 - question_output_loss: 3.9047 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 1.0550 - val_answer_output_accuracy: 0.5800 - val_answer_output_loss: 3.8962 - val_loss: 10.9915 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 2.0227 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9453 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.1273\n", + "Epoch 3/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - answer_output_accuracy: 0.9000 - answer_output_loss: 3.8076 - loss: 10.6189 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.9522 - question_output_accuracy: 0.0800 - question_output_loss: 3.8585 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 1.0005 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 3.8543 - val_loss: 10.9334 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.9867 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9469 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.1455\n", + "Epoch 4/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340ms/step - answer_output_accuracy: 0.9400 - answer_output_loss: 3.6877 - loss: 10.2657 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.8489 - question_output_accuracy: 0.0600 - question_output_loss: 3.8010 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.9281 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 3.7881 - val_loss: 10.8457 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.9324 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9492 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.1760\n", + "Epoch 5/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - answer_output_accuracy: 0.9400 - answer_output_loss: 3.4683 - loss: 9.6920 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.6792 - question_output_accuracy: 0.0600 - question_output_loss: 3.7188 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.8258 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 3.6742 - val_loss: 10.7083 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.8475 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9535 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.2331\n", + "Epoch 6/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344ms/step - answer_output_accuracy: 0.9400 - answer_output_loss: 2.9986 - loss: 8.6406 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.3997 - question_output_accuracy: 0.0400 - question_output_loss: 3.5829 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.6593 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 3.4580 - val_loss: 10.4731 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.7102 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9628 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.3420\n", + "Epoch 7/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - answer_output_accuracy: 0.9400 - answer_output_loss: 1.9364 - loss: 6.7078 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.0844 - question_output_accuracy: 0.0400 - question_output_loss: 3.3048 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.3822 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 2.9410 - val_loss: 10.0188 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.5038 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 3.9871 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 1.5870\n", + "Epoch 8/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318ms/step - answer_output_accuracy: 0.9600 - answer_output_loss: 0.9184 - loss: 4.9883 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.3771 - question_output_accuracy: 0.0400 - question_output_loss: 2.6239 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.0690 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 1.7714 - val_loss: 9.6522 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.4667 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 4.0805 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 2.3336\n", + "Epoch 9/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - answer_output_accuracy: 0.9600 - answer_output_loss: 0.4511 - loss: 3.5983 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.3641 - question_output_accuracy: 0.0400 - question_output_loss: 1.7815 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 0.0015 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 0.8089 - val_loss: 11.4588 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.5131 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 4.6062 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 4.5306\n", + "Epoch 10/10\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304ms/step - answer_output_accuracy: 0.9400 - answer_output_loss: 0.3244 - loss: 2.9690 - ner_output_accuracy: 0.7800 - ner_output_loss: 1.1538 - question_output_accuracy: 0.0600 - question_output_loss: 1.4906 - question_type_output_accuracy: 1.0000 - question_type_output_loss: 1.7498e-04 - val_answer_output_accuracy: 0.9800 - val_answer_output_loss: 0.3998 - val_loss: 16.2049 - val_ner_output_accuracy: 0.8600 - val_ner_output_loss: 1.5880 - val_question_output_accuracy: 0.0000e+00 - val_question_output_loss: 6.0197 - val_question_type_output_accuracy: 0.0000e+00 - val_question_type_output_loss: 8.1974\n" + ] + } + ], + "source": [ + "# ----------------------------\n", + "# Parameter Model\n", + "# ----------------------------\n", + "VOCAB_SIZE = len(tokenizer.word_index) + 1\n", + "EMBEDDING_DIM = 300\n", + "LSTM_UNITS = 256\n", + "BATCH_SIZE = 16\n", + "EPOCHS = 10\n", + "NUM_NER_TAGS = len(ner2idx)\n", + "\n", + "# ----------------------------\n", + "# Arsitektur Model Multi-Output\n", + "# ----------------------------\n", + "\n", + "# Encoder: Input context\n", + "context_input = Input(shape=(MAX_LENGTH,), name=\"context_input\")\n", + "context_embedding = Embedding(input_dim=VOCAB_SIZE, output_dim=EMBEDDING_DIM, mask_zero=True, name=\"context_embedding\")(context_input)\n", + "encoder_lstm = LSTM(LSTM_UNITS, return_state=True, name=\"encoder_lstm\")\n", + "encoder_output, state_h, state_c = encoder_lstm(context_embedding)\n", + "\n", + "# Branch untuk pembuatan soal (Question Decoder)\n", + "question_decoder_input = Input(shape=(MAX_LENGTH,), name=\"question_decoder_input\")\n", + "question_embedding = Embedding(input_dim=VOCAB_SIZE, output_dim=EMBEDDING_DIM, mask_zero=True, name=\"question_embedding\")(question_decoder_input)\n", + "question_lstm = LSTM(LSTM_UNITS, return_sequences=True, return_state=True, name=\"question_lstm\")\n", + "question_output, _, _ = question_lstm(question_embedding, initial_state=[state_h, state_c])\n", + "question_dense = Dense(VOCAB_SIZE, activation=\"softmax\", name=\"question_output\")(question_output)\n", + "\n", + "# Branch untuk pembuatan jawaban (Answer Decoder)\n", + "answer_lstm = LSTM(LSTM_UNITS, return_sequences=True, return_state=True, name=\"answer_lstm\")\n", + "answer_output, _, _ = answer_lstm(context_embedding, initial_state=[state_h, state_c])\n", + "answer_dense = Dense(VOCAB_SIZE, activation=\"softmax\", name=\"answer_output\")(answer_output)\n", + "\n", + "# Branch untuk klasifikasi tipe soal\n", + "type_dense = Dense(128, activation=\"relu\")(encoder_output)\n", + "question_type_output = Dense(3, activation=\"softmax\", name=\"question_type_output\")(type_dense)\n", + "\n", + "# Branch untuk NER: Menggunakan context_embedding untuk melakukan sequence tagging\n", + "ner_lstm = Bidirectional(LSTM(LSTM_UNITS, return_sequences=True, recurrent_dropout=0.1), name=\"ner_lstm\")(context_embedding)\n", + "ner_output = TimeDistributed(Dense(NUM_NER_TAGS, activation=\"softmax\"), name=\"ner_output\")(ner_lstm)\n", + "\n", + "# Gabungkan semua branch dalam satu model multi-output\n", + "model = Model(\n", + " inputs=[context_input, question_decoder_input],\n", + " outputs=[question_dense, answer_dense, question_type_output, ner_output]\n", + ")\n", + "\n", + "model.compile(\n", + " optimizer=\"adam\",\n", + " loss={\n", + " \"question_output\": \"sparse_categorical_crossentropy\",\n", + " \"answer_output\": \"sparse_categorical_crossentropy\",\n", + " \"question_type_output\": \"sparse_categorical_crossentropy\",\n", + " \"ner_output\": \"sparse_categorical_crossentropy\"\n", + " },\n", + " metrics={\n", + " \"question_output\": [\"accuracy\"],\n", + " \"answer_output\": [\"accuracy\"],\n", + " \"question_type_output\": [\"accuracy\"],\n", + " \"ner_output\": [\"accuracy\"]\n", + " }\n", + ")\n", + "\n", + "model.summary()\n", + "\n", + "# ----------------------------\n", + "# Training Model\n", + "# ----------------------------\n", + "model.fit(\n", + " [context_train, question_train],\n", + " {\n", + " \"question_output\": question_train,\n", + " \"answer_output\": answer_train,\n", + " \"question_type_output\": qtype_train,\n", + " \"ner_output\": ner_train\n", + " },\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(\n", + " [context_val, question_val],\n", + " {\n", + " \"question_output\": question_val,\n", + " \"answer_output\": answer_val,\n", + " \"question_type_output\": qtype_val,\n", + " \"ner_output\": ner_val\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Simpan model dan tokenizer bila diperlukan\n", + "model.save(\"lstm_multi_output_ner_model.keras\")\n", + "with open(\"tokenizer.pkl\", \"wb\") as handle:\n", + " pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": 2 +} diff --git a/tokenizer.pkl b/tokenizer.pkl index 8e5c4a3..a0e3be5 100644 Binary files a/tokenizer.pkl and b/tokenizer.pkl differ diff --git a/training_model.ipynb b/training_model.ipynb index 23827d8..f01174a 100644 --- a/training_model.ipynb +++ b/training_model.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 112, + "execution_count": 162, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ "True" ] }, - "execution_count": 113, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -187,7 +187,6 @@ "tokenizer = Tokenizer(oov_token=\"\")\n", "tokenizer.fit_on_texts(contexts + questions + correct_answers)\n", "\n", - "\n", "context_sequences = tokenizer.texts_to_sequences(contexts)\n", "question_sequences = tokenizer.texts_to_sequences(questions)\n", "answer_sequences = tokenizer.texts_to_sequences(correct_answers)\n", @@ -206,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -237,33 +236,53 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 167, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 1s/step - answer_output_accuracy: 0.0344 - answer_output_loss: 6.2090 - loss: 13.5239 - question_output_accuracy: 0.0000e+00 - question_output_loss: 6.2154 - question_type_output_accuracy: 0.3004 - question_type_output_loss: 1.0991 - val_answer_output_accuracy: 0.2287 - val_answer_output_loss: 6.1669 - val_loss: 13.4815 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.2101 - val_question_type_output_accuracy: 0.3125 - val_question_type_output_loss: 1.1046\n", - "Epoch 2/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 526ms/step - answer_output_accuracy: 0.2277 - answer_output_loss: 6.1421 - loss: 13.4196 - question_output_accuracy: 0.0113 - question_output_loss: 6.1984 - question_type_output_accuracy: 0.6445 - question_type_output_loss: 1.0780 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 6.0462 - val_loss: 13.3570 - val_question_output_accuracy: 0.0081 - val_question_output_loss: 6.2031 - val_question_type_output_accuracy: 0.3125 - val_question_type_output_loss: 1.1076\n", - "Epoch 3/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 528ms/step - answer_output_accuracy: 0.9837 - answer_output_loss: 5.9539 - loss: 13.1879 - question_output_accuracy: 0.0171 - question_output_loss: 6.1802 - question_type_output_accuracy: 0.5799 - question_type_output_loss: 1.0503 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 5.5439 - val_loss: 12.8565 - val_question_output_accuracy: 0.0087 - val_question_output_loss: 6.1941 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.1185\n", - "Epoch 4/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 533ms/step - answer_output_accuracy: 0.9839 - answer_output_loss: 5.1228 - loss: 12.2985 - question_output_accuracy: 0.0137 - question_output_loss: 6.1532 - question_type_output_accuracy: 0.5164 - question_type_output_loss: 1.0060 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 3.2875 - val_loss: 10.6708 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.1772 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.2060\n", - "Epoch 5/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 520ms/step - answer_output_accuracy: 0.9835 - answer_output_loss: 2.7939 - loss: 9.9397 - question_output_accuracy: 0.0056 - question_output_loss: 6.0862 - question_type_output_accuracy: 0.5263 - question_type_output_loss: 1.0473 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 1.1028 - val_loss: 9.0601 - val_question_output_accuracy: 0.0012 - val_question_output_loss: 6.1277 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.8296\n", - "Epoch 6/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 541ms/step - answer_output_accuracy: 0.9828 - answer_output_loss: 1.2315 - loss: 8.3718 - question_output_accuracy: 0.0016 - question_output_loss: 5.8773 - question_type_output_accuracy: 0.5055 - question_type_output_loss: 1.2478 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.6227 - val_loss: 8.6339 - val_question_output_accuracy: 0.0012 - val_question_output_loss: 6.0831 - val_question_type_output_accuracy: 0.1250 - val_question_type_output_loss: 1.9281\n", - "Epoch 7/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 492ms/step - answer_output_accuracy: 0.9842 - answer_output_loss: 0.7375 - loss: 7.4714 - question_output_accuracy: 9.6824e-04 - question_output_loss: 5.5770 - question_type_output_accuracy: 0.4612 - question_type_output_loss: 1.1578 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5788 - val_loss: 7.9850 - val_question_output_accuracy: 0.0012 - 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5.0887 - question_type_output_accuracy: 0.4841 - question_type_output_loss: 1.1123 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.6056 - val_loss: 8.1353 - val_question_output_accuracy: 0.0019 - val_question_output_loss: 6.4616 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.0682\n", - "Epoch 10/10\n", - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 454ms/step - answer_output_accuracy: 0.9847 - answer_output_loss: 0.6727 - loss: 6.6312 - question_output_accuracy: 0.0018 - question_output_loss: 4.9620 - question_type_output_accuracy: 0.5258 - question_type_output_loss: 1.0078 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5869 - val_loss: 8.5074 - val_question_output_accuracy: 0.0037 - val_question_output_loss: 6.6207 - val_question_type_output_accuracy: 0.3750 - val_question_type_output_loss: 1.2998\n" + "Epoch 1/20\n", + "\u001b[1m2/2\u001b[0m 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val_loss: 13.4025 - val_question_output_accuracy: 0.0112 - val_question_output_loss: 6.2026 - val_question_type_output_accuracy: 0.3125 - val_question_type_output_loss: 1.1115\n", + "Epoch 3/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 316ms/step - answer_output_accuracy: 0.9831 - answer_output_loss: 6.0118 - loss: 13.2454 - question_output_accuracy: 0.0183 - question_output_loss: 6.1792 - question_type_output_accuracy: 0.6341 - question_type_output_loss: 1.0521 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 5.7557 - val_loss: 13.0783 - val_question_output_accuracy: 0.0106 - val_question_output_loss: 6.1923 - val_question_type_output_accuracy: 0.3125 - val_question_type_output_loss: 1.1303\n", + "Epoch 4/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 318ms/step - answer_output_accuracy: 0.9831 - answer_output_loss: 5.4126 - loss: 12.5932 - question_output_accuracy: 0.0159 - question_output_loss: 6.1526 - question_type_output_accuracy: 0.6132 - question_type_output_loss: 1.0133 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 4.0785 - val_loss: 11.4385 - val_question_output_accuracy: 0.0087 - val_question_output_loss: 6.1729 - val_question_type_output_accuracy: 0.4375 - val_question_type_output_loss: 1.1871\n", + "Epoch 5/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 321ms/step - answer_output_accuracy: 0.9833 - answer_output_loss: 3.5350 - loss: 10.6302 - question_output_accuracy: 0.0109 - question_output_loss: 6.0941 - question_type_output_accuracy: 0.5482 - question_type_output_loss: 0.9777 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 1.4486 - val_loss: 9.1339 - val_question_output_accuracy: 0.0069 - val_question_output_loss: 6.1108 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.5745\n", + "Epoch 6/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 324ms/step - answer_output_accuracy: 0.9830 - answer_output_loss: 1.3763 - loss: 8.3790 - question_output_accuracy: 0.0050 - question_output_loss: 5.8928 - question_type_output_accuracy: 0.5596 - question_type_output_loss: 1.0961 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.6301 - val_loss: 8.8752 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.0297 - val_question_type_output_accuracy: 0.1250 - val_question_type_output_loss: 2.2154\n", + "Epoch 7/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 320ms/step - answer_output_accuracy: 0.9827 - answer_output_loss: 0.8154 - loss: 7.6408 - question_output_accuracy: 0.0030 - question_output_loss: 5.5596 - question_type_output_accuracy: 0.5596 - question_type_output_loss: 1.2471 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5587 - val_loss: 7.8821 - val_question_output_accuracy: 0.0044 - val_question_output_loss: 6.0440 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.2793\n", + "Epoch 8/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 315ms/step - answer_output_accuracy: 0.9845 - answer_output_loss: 0.6699 - loss: 7.0558 - question_output_accuracy: 0.0025 - question_output_loss: 5.2922 - question_type_output_accuracy: 0.5159 - question_type_output_loss: 1.0964 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5644 - val_loss: 7.7566 - val_question_output_accuracy: 0.0044 - val_question_output_loss: 6.1598 - val_question_type_output_accuracy: 0.4375 - val_question_type_output_loss: 1.0324\n", + "Epoch 9/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 319ms/step - answer_output_accuracy: 0.9837 - answer_output_loss: 0.7007 - loss: 6.7585 - question_output_accuracy: 0.0021 - question_output_loss: 5.0688 - question_type_output_accuracy: 0.5804 - question_type_output_loss: 0.9895 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5536 - val_loss: 8.3754 - val_question_output_accuracy: 0.0044 - val_question_output_loss: 6.3426 - val_question_type_output_accuracy: 0.1250 - val_question_type_output_loss: 1.4793\n", + "Epoch 10/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 316ms/step - answer_output_accuracy: 0.9841 - answer_output_loss: 0.6571 - loss: 6.6996 - question_output_accuracy: 0.0020 - question_output_loss: 4.9654 - question_type_output_accuracy: 0.3769 - question_type_output_loss: 1.0726 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5333 - val_loss: 8.2096 - val_question_output_accuracy: 0.0044 - val_question_output_loss: 6.5258 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.1504\n", + "Epoch 11/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 313ms/step - answer_output_accuracy: 0.9846 - answer_output_loss: 0.5896 - loss: 6.3947 - question_output_accuracy: 0.0029 - question_output_loss: 4.8274 - question_type_output_accuracy: 0.5367 - question_type_output_loss: 0.9851 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5115 - val_loss: 8.4411 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.6733 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.2564\n", + "Epoch 12/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 287ms/step - answer_output_accuracy: 0.9832 - answer_output_loss: 0.6274 - loss: 6.3656 - question_output_accuracy: 0.0030 - question_output_loss: 4.7141 - question_type_output_accuracy: 0.4950 - question_type_output_loss: 1.0145 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5007 - val_loss: 8.7380 - val_question_output_accuracy: 0.0037 - val_question_output_loss: 6.7743 - val_question_type_output_accuracy: 0.1875 - val_question_type_output_loss: 1.4630\n", + "Epoch 13/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 294ms/step - answer_output_accuracy: 0.9841 - answer_output_loss: 0.5365 - loss: 6.0931 - question_output_accuracy: 0.0028 - question_output_loss: 4.6340 - question_type_output_accuracy: 0.6330 - question_type_output_loss: 0.9268 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5095 - val_loss: 8.8004 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.8402 - val_question_type_output_accuracy: 0.1875 - val_question_type_output_loss: 1.4508\n", + "Epoch 14/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 293ms/step - answer_output_accuracy: 0.9839 - answer_output_loss: 0.5214 - loss: 5.9535 - question_output_accuracy: 0.0038 - question_output_loss: 4.5068 - question_type_output_accuracy: 0.6023 - question_type_output_loss: 0.9284 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5292 - val_loss: 8.5136 - val_question_output_accuracy: 0.0050 - val_question_output_loss: 6.8903 - val_question_type_output_accuracy: 0.5000 - val_question_type_output_loss: 1.0940\n", + "Epoch 15/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 294ms/step - answer_output_accuracy: 0.9838 - answer_output_loss: 0.5345 - loss: 5.8897 - question_output_accuracy: 0.0041 - question_output_loss: 4.4544 - question_type_output_accuracy: 0.5596 - question_type_output_loss: 0.9003 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5447 - val_loss: 8.5770 - val_question_output_accuracy: 0.0056 - val_question_output_loss: 6.9331 - val_question_type_output_accuracy: 0.3125 - val_question_type_output_loss: 1.0993\n", + "Epoch 16/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 292ms/step - answer_output_accuracy: 0.9832 - answer_output_loss: 0.5705 - loss: 5.8373 - question_output_accuracy: 0.0048 - question_output_loss: 4.3814 - question_type_output_accuracy: 0.6351 - question_type_output_loss: 0.8800 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5496 - val_loss: 8.8434 - val_question_output_accuracy: 0.0062 - val_question_output_loss: 6.9745 - val_question_type_output_accuracy: 0.2500 - val_question_type_output_loss: 1.3193\n", + "Epoch 17/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 288ms/step - answer_output_accuracy: 0.9832 - answer_output_loss: 0.5433 - loss: 5.6367 - question_output_accuracy: 0.0053 - question_output_loss: 4.2834 - question_type_output_accuracy: 0.6773 - question_type_output_loss: 0.8080 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5488 - val_loss: 8.9683 - val_question_output_accuracy: 0.0062 - val_question_output_loss: 7.0182 - val_question_type_output_accuracy: 0.1875 - val_question_type_output_loss: 1.4014\n", + "Epoch 18/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 292ms/step - answer_output_accuracy: 0.9843 - answer_output_loss: 0.4771 - loss: 5.4290 - question_output_accuracy: 0.0060 - question_output_loss: 4.1923 - question_type_output_accuracy: 0.6877 - question_type_output_loss: 0.7646 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5510 - val_loss: 9.0373 - val_question_output_accuracy: 0.0062 - val_question_output_loss: 7.0739 - val_question_type_output_accuracy: 0.2500 - val_question_type_output_loss: 1.4124\n", + "Epoch 19/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 305ms/step - answer_output_accuracy: 0.9846 - answer_output_loss: 0.4586 - loss: 5.3489 - question_output_accuracy: 0.0053 - question_output_loss: 4.1443 - question_type_output_accuracy: 0.6668 - question_type_output_loss: 0.7466 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5583 - val_loss: 9.1426 - val_question_output_accuracy: 0.0062 - val_question_output_loss: 7.1137 - val_question_type_output_accuracy: 0.1875 - val_question_type_output_loss: 1.4707\n", + "Epoch 20/20\n", + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 305ms/step - answer_output_accuracy: 0.9830 - answer_output_loss: 0.5251 - loss: 5.2352 - question_output_accuracy: 0.0066 - question_output_loss: 4.0488 - question_type_output_accuracy: 0.7298 - question_type_output_loss: 0.6596 - val_answer_output_accuracy: 0.9856 - val_answer_output_loss: 0.5674 - val_loss: 9.4190 - val_question_output_accuracy: 0.0062 - val_question_output_loss: 7.1243 - val_question_type_output_accuracy: 0.1250 - val_question_type_output_loss: 1.7272\n" ] } ], @@ -273,7 +292,7 @@ "EMBEDDING_DIM = 300\n", "LSTM_UNITS = 256\n", "BATCH_SIZE = 32\n", - "EPOCHS = 10\n", + "EPOCHS = 20\n", "\n", "context_input = Input(shape=(MAX_LENGTH,), name=\"context_input\")\n", "context_embedding = Embedding(input_dim=VOCAB_SIZE, output_dim=EMBEDDING_DIM, mask_zero=True, name=\"context_embedding\")(context_input)\n", @@ -325,12 +344,12 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 168, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -375,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -383,65 +402,7 @@ "output_type": "stream", "text": [ "\n", - "=== Evaluation on Test Data ===\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 389ms/step\n", - "Classification Report for Question Type (Test Set):\n", - " precision recall f1-score support\n", - "\n", - " 0 0.00 0.00 0.00 4\n", - " 1 0.40 0.67 0.50 3\n", - " 2 0.20 0.33 0.25 3\n", - "\n", - " accuracy 0.30 10\n", - " macro avg 0.20 0.33 0.25 10\n", - "weighted avg 0.18 0.30 0.23 10\n", - "\n", - "Test Accuracy: 0.3\n", - "Test Precision: 0.18000000000000002\n", - "Test Recall: 0.3\n", - "BLEU Score for first test sample (question generation): 0.02664466031983166\n", - "BLEU Score for first test sample (answer generation): 0\n", - "\n", - "=== Evaluation on Validation Data ===\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step\n", - "Classification Report for Question Type (Validation Set):\n", - " precision recall f1-score support\n", - "\n", - " 0 0.00 0.00 0.00 4\n", - " 1 0.50 1.00 0.67 3\n", - " 2 0.25 0.33 0.29 3\n", - "\n", - " accuracy 0.40 10\n", - " macro avg 0.25 0.44 0.32 10\n", - "weighted avg 0.23 0.40 0.29 10\n", - "\n", - "Validation Accuracy: 0.4\n", - "Validation Precision: 0.225\n", - "Validation Recall: 0.4\n", - "BLEU Score for first validation sample (question generation): 0.008991061769415444\n", - "BLEU Score for first validation sample (answer generation): 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n", - "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n" + "=== Evaluation on Test Data ===\n" ] } ], diff --git a/uji.py b/uji.py index aabe8c7..e26256a 100644 --- a/uji.py +++ b/uji.py @@ -1,163 +1,163 @@ -import numpy as np -import pickle -import tensorflow as tf -from tensorflow.keras.preprocessing.sequence import pad_sequences -import nltk -import random -import string -import re -from nltk.tokenize import word_tokenize -from nltk.corpus import stopwords +# import numpy as np +# import pickle +# import tensorflow as tf +# from tensorflow.keras.preprocessing.sequence import pad_sequences +# import nltk +# import random +# import string +# import re +# from nltk.tokenize import word_tokenize +# from nltk.corpus import stopwords -# Ensure NLTK resources are available -nltk.download("punkt") -nltk.download("stopwords") +# # Ensure NLTK resources are available +# nltk.download("punkt") +# nltk.download("stopwords") -class QuestionGenerator: - def __init__( - self, model_path="lstm_multi_output_model.keras", tokenizer_path="tokenizer.pkl" - ): - """ - Initializes the QuestionGenerator by loading the trained model and tokenizer. - """ - # Load trained model - self.model = tf.keras.models.load_model(model_path) +# class QuestionGenerator: +# def __init__( +# self, model_path="lstm_multi_output_model.keras", tokenizer_path="tokenizer.pkl" +# ): +# """ +# Initializes the QuestionGenerator by loading the trained model and tokenizer. +# """ +# # Load trained model +# self.model = tf.keras.models.load_model(model_path) - # Load tokenizer - with open(tokenizer_path, "rb") as handle: - self.tokenizer = pickle.load(handle) +# # Load tokenizer +# with open(tokenizer_path, "rb") as handle: +# self.tokenizer = pickle.load(handle) - # Define question type mapping - self.question_type_dict = { - 0: "fill_in_the_blank", - 1: "true_false", - 2: "multiple_choice", - } +# # Define question type mapping +# self.question_type_dict = { +# 0: "fill_in_the_blank", +# 1: "true_false", +# 2: "multiple_choice", +# } - # Load Indonesian stopwords - self.stop_words = set(stopwords.words("indonesian")) +# # Load Indonesian stopwords +# self.stop_words = set(stopwords.words("indonesian")) - # Custom word normalization dictionary - self.normalization_dict = { - "yg": "yang", - "gokil": "kocak", - "kalo": "kalau", - "gue": "saya", - "elo": "kamu", - "nih": "ini", - "trs": "terus", - "tdk": "tidak", - "gmna": "bagaimana", - "tp": "tapi", - "jd": "jadi", - "aja": "saja", - "krn": "karena", - "blm": "belum", - "dgn": "dengan", - "skrg": "sekarang", - "msh": "masih", - "lg": "lagi", - "sy": "saya", - "sm": "sama", - "bgt": "banget", - "dr": "dari", - "kpn": "kapan", - "hrs": "harus", - "cm": "cuma", - "sbnrnya": "sebenarnya", - } +# # Custom word normalization dictionary +# self.normalization_dict = { +# "yg": "yang", +# "gokil": "kocak", +# "kalo": "kalau", +# "gue": "saya", +# "elo": "kamu", +# "nih": "ini", +# "trs": "terus", +# "tdk": "tidak", +# "gmna": "bagaimana", +# "tp": "tapi", +# "jd": "jadi", +# "aja": "saja", +# "krn": "karena", +# "blm": "belum", +# "dgn": "dengan", +# "skrg": "sekarang", +# "msh": "masih", +# "lg": "lagi", +# "sy": "saya", +# "sm": "sama", +# "bgt": "banget", +# "dr": "dari", +# "kpn": "kapan", +# "hrs": "harus", +# "cm": "cuma", +# "sbnrnya": "sebenarnya", +# } - def preprocess_text(self, text): - """ - Preprocesses the input text by: - - Converting to lowercase - - Removing punctuation - - Tokenizing - - Normalizing words - - Removing stopwords - """ - text = text.lower() - text = text.translate( - str.maketrans("", "", string.punctuation) - ) # Remove punctuation - text = re.sub(r"\s+", " ", text).strip() # Remove extra spaces - tokens = word_tokenize(text) # Tokenization - tokens = [ - self.normalization_dict.get(word, word) for word in tokens - ] # Normalize words - tokens = [ - word for word in tokens if word not in self.stop_words - ] # Remove stopwords - return " ".join(tokens) +# def preprocess_text(self, text): +# """ +# Preprocesses the input text by: +# - Converting to lowercase +# - Removing punctuation +# - Tokenizing +# - Normalizing words +# - Removing stopwords +# """ +# text = text.lower() +# text = text.translate( +# str.maketrans("", "", string.punctuation) +# ) # Remove punctuation +# text = re.sub(r"\s+", " ", text).strip() # Remove extra spaces +# tokens = word_tokenize(text) # Tokenization +# tokens = [ +# self.normalization_dict.get(word, word) for word in tokens +# ] # Normalize words +# tokens = [ +# word for word in tokens if word not in self.stop_words +# ] # Remove stopwords +# return " ".join(tokens) - def sequence_to_text(self, sequence): - """ - Converts a tokenized sequence back into readable text. - """ - return " ".join( - [ - self.tokenizer.index_word.get(idx, "") - for idx in sequence - if idx != 0 - ] - ) +# def sequence_to_text(self, sequence): +# """ +# Converts a tokenized sequence back into readable text. +# """ +# return " ".join( +# [ +# self.tokenizer.index_word.get(idx, "") +# for idx in sequence +# if idx != 0 +# ] +# ) - def generate_qa_from_paragraph(self, paragraph): - """ - Generates a question, answer, and question type from the given paragraph. - If it's a multiple-choice question, it also returns answer options. - """ - # Preprocess the input paragraph - processed_paragraph = self.preprocess_text(paragraph) +# def generate_qa_from_paragraph(self, paragraph): +# """ +# Generates a question, answer, and question type from the given paragraph. +# If it's a multiple-choice question, it also returns answer options. +# """ +# # Preprocess the input paragraph +# processed_paragraph = self.preprocess_text(paragraph) - # Convert text to sequence - input_seq = self.tokenizer.texts_to_sequences([processed_paragraph]) - input_seq = pad_sequences(input_seq, maxlen=100, padding="post") +# # Convert text to sequence +# input_seq = self.tokenizer.texts_to_sequences([processed_paragraph]) +# input_seq = pad_sequences(input_seq, maxlen=100, padding="post") - # Predict question, answer, and type - pred_question, pred_answer, pred_qtype = self.model.predict( - [input_seq, input_seq] - ) +# # Predict question, answer, and type +# pred_question, pred_answer, pred_qtype = self.model.predict( +# [input_seq, input_seq] +# ) - # Decode predictions - generated_question = self.sequence_to_text(np.argmax(pred_question[0], axis=-1)) - generated_answer = self.sequence_to_text(np.argmax(pred_answer[0], axis=-1)) - question_type_index = np.argmax(pred_qtype[0]) - generated_qtype = self.question_type_dict[question_type_index] +# # Decode predictions +# generated_question = self.sequence_to_text(np.argmax(pred_question[0], axis=-1)) +# generated_answer = self.sequence_to_text(np.argmax(pred_answer[0], axis=-1)) +# question_type_index = np.argmax(pred_qtype[0]) +# generated_qtype = self.question_type_dict[question_type_index] - # Handle multiple-choice options - options = None - if generated_qtype == "multiple_choice": - words = processed_paragraph.split() - random.shuffle(words) - distractors = [ - word for word in words if word.lower() != generated_answer.lower() - ] - options = [generated_answer] + distractors[:3] - random.shuffle(options) # Shuffle options +# # Handle multiple-choice options +# options = None +# if generated_qtype == "multiple_choice": +# words = processed_paragraph.split() +# random.shuffle(words) +# distractors = [ +# word for word in words if word.lower() != generated_answer.lower() +# ] +# options = [generated_answer] + distractors[:3] +# random.shuffle(options) # Shuffle options - # Return the generated data - return { - "generated_question": generated_question, - "generated_answer": generated_answer, - "question_type": generated_qtype, - "options": options if generated_qtype == "multiple_choice" else None, - } +# # Return the generated data +# return { +# "generated_question": generated_question, +# "generated_answer": generated_answer, +# "question_type": generated_qtype, +# "options": options if generated_qtype == "multiple_choice" else None, +# } -# Initialize the question generator -qg = QuestionGenerator() +# # Initialize the question generator +# qg = QuestionGenerator() -# Example input paragraph -sample_paragraph = "Samudra Pasifik adalah yang terbesar dan terdalam di antara divisi samudra di Bumi. Samudra ini membentang dari Samudra Arktik di utara hingga Samudra Selatan di selatan dan berbatasan dengan Asia dan Australia di barat serta Amerika di timur." +# # Example input paragraph +# sample_paragraph = "Samudra Pasifik adalah yang terbesar dan terdalam di antara divisi samudra di Bumi. Samudra ini membentang dari Samudra Arktik di utara hingga Samudra Selatan di selatan dan berbatasan dengan Asia dan Australia di barat serta Amerika di timur." -# Generate question, answer, and type -generated_result = qg.generate_qa_from_paragraph(sample_paragraph) +# # Generate question, answer, and type +# generated_result = qg.generate_qa_from_paragraph(sample_paragraph) -# Print output -print("Generated Question:", generated_result["generated_question"]) -print("Generated Answer:", generated_result["generated_answer"]) -print("Question Type:", generated_result["question_type"]) -if generated_result["options"]: - print("Options:", generated_result["options"]) +# # Print output +# print("Generated Question:", generated_result["generated_question"]) +# print("Generated Answer:", generated_result["generated_answer"]) +# print("Question Type:", generated_result["question_type"]) +# if generated_result["options"]: +# print("Options:", generated_result["options"])