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| import argparse | |
| import random | |
| import json | |
| import re | |
| from sentence_transformers import SentenceTransformer | |
| from sentence_transformers.util import semantic_search, cos_sim | |
| from tqdm.auto import tqdm | |
| from datasets import load_dataset | |
| from common import clean_answer, clean_question | |
| def find_hard_negative_ctxs(dataset, dataset_embeddings, embedding_index: int, | |
| exclude_answer_patterns, similarity_threshold=[0.5, 0.6], k=25, min_count=3): | |
| hard_negative_ctxs = [] | |
| results = semantic_search(dataset_embeddings[embedding_index], dataset_embeddings, top_k=k, | |
| score_function=cos_sim) | |
| # list if dicts | |
| # [{'corpus_id': 8, 'score': -0.019427383318543434}, | |
| # ... | |
| # {'corpus_id': 10, 'score': -0.09040290117263794}] | |
| # hard negative are most similar and negatives are most disimilar to embedding_index | |
| hard_negative_results = results[0][1:k + 1] | |
| assert len(hard_negative_results) > min_count * 2 | |
| for r in hard_negative_results: | |
| example = dataset[r["corpus_id"]] | |
| if similarity_threshold[0] < r["score"] <= similarity_threshold[1]: | |
| for a in example["answers"]["text"]: | |
| hard_negative_ctxs.append({"title": "", "text": clean_answer(a)}) | |
| if len(hard_negative_ctxs) > min_count: | |
| break | |
| return hard_negative_ctxs[:min_count] | |
| def find_negative_ctxs(dataset, dataset_embeddings, embedding_index: int, | |
| exclude_answer_patterns, similarity_threshold=0.1, k=7, min_count=3): | |
| negative_ctxs = [] | |
| random_sample = random.sample(range(len(dataset_embeddings)), k * 20) | |
| similarities = cos_sim(dataset_embeddings[embedding_index], dataset_embeddings[random_sample])[0].tolist() | |
| for idx, score in enumerate(similarities): | |
| if score < similarity_threshold: | |
| example = dataset[random_sample[idx]] | |
| for a in example["answers"]["text"]: | |
| negative_ctxs.append({"title": "", "text": clean_answer(a)}) | |
| if len(negative_ctxs) > min_count: | |
| break | |
| return negative_ctxs[:min_count] | |
| def generate_dpr_training_file(args): | |
| embedder = SentenceTransformer(args.embedding_model) | |
| eli5_train_set = load_dataset("vblagoje/lfqa", split="train") | |
| eli5_validation_set = load_dataset("vblagoje/lfqa", split="validation") | |
| eli5_test_set = load_dataset("vblagoje/lfqa", split="test") | |
| train_set = embedder.encode([example["title"] for example in eli5_train_set], convert_to_tensor=True, | |
| show_progress_bar=True) | |
| validation_set = embedder.encode([example["title"] for example in eli5_validation_set], convert_to_tensor=True, | |
| show_progress_bar=True) | |
| test_set = embedder.encode([example["title"] for example in eli5_test_set], convert_to_tensor=True, | |
| show_progress_bar=True) | |
| exclude_answer_patterns = [re.compile("not sure what you"), re.compile("\n\n >")] | |
| for dataset_name, dataset, dataset_embeddings in zip(["train", "validation", "test"], | |
| [eli5_train_set, eli5_validation_set, eli5_test_set], | |
| [train_set, validation_set, test_set]): | |
| min_elements = 3 | |
| skip_count = 0 | |
| progress_bar = tqdm(range(len(dataset)), desc="Creating DPR formatted question/passage docs") | |
| with open('eli5-dpr-' + dataset_name + '.jsonl', 'w') as fp: | |
| for idx, example in enumerate(dataset): | |
| negative_ctxs = find_negative_ctxs(dataset, dataset_embeddings, idx, exclude_answer_patterns) | |
| hard_negative_ctxs = find_hard_negative_ctxs(dataset, dataset_embeddings, idx, exclude_answer_patterns) | |
| positive_context = [{"text": clean_answer(a), "title": ""} for a in example["answers"]["text"] if | |
| not any([p.search(a) for p in exclude_answer_patterns])] | |
| if not positive_context: | |
| positive_context = [{"text": clean_answer(a), "title": ""} for a in example["answers"]["text"]] | |
| if len(positive_context) > 0 and len(negative_ctxs) > 0 and len(hard_negative_ctxs) >= min_elements: | |
| json.dump({"id": example["q_id"], | |
| "question": clean_question(example["title"]), | |
| "positive_ctxs": positive_context[:min_elements], | |
| "negative_ctxs": negative_ctxs[:min_elements], | |
| "hard_negative_ctxs": hard_negative_ctxs[:min_elements]}, fp) | |
| fp.write("\n") | |
| else: | |
| skip_count += 1 | |
| progress_bar.update(1) | |
| print(f"Skipped {skip_count} questions") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Creates DPR training file from LFQA dataset") | |
| parser.add_argument( | |
| "--embedding_model", | |
| default="all-mpnet-base-v2", | |
| help="Embedding model to use for question encoding and semantic search", | |
| ) | |
| main_args, _ = parser.parse_known_args() | |
| generate_dpr_training_file(main_args) | |