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class MFAQLight(datasets.GeneratorBasedBuilder): "answer": datasets.Value("string"), "domain": datasets.Value("string"), "domain_index": datasets.Value("int32"), - "id": datasets.Value("string") + "id": datasets.Value("string"), + "negative": datasets.Value("string"), + "candidates": [datasets.Value("string")] } return datasets.DatasetInfo( description=_DESCRIPTION, @@ -105,5 +107,7 @@ class MFAQLight(datasets.GeneratorBasedBuilder): "answer": question["answer"], "domain": question["domain"], "domain_index": question["domain_index"], - "id": question["id"] + "id": question["id"], + "negative": question.get("negative_example", ""), + "candidates": question.get("candidates", []) } diff --git a/train_valid_split.py b/train_valid_split.py index eb40e22d9013e8629b595fd68642a565fad08b18..72ad0e7c0deecce7e9b2f553c512586277de73d1 100644 --- a/train_valid_split.py +++ b/train_valid_split.py @@ -4,6 +4,8 @@ import tqdm import functools import collections import multiprocessing +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import linear_kernel def extract_domains(filename): @@ -15,6 +17,34 @@ def extract_domains(filename): return filename, list(domains) +def filter_valid(questions): + answers = set() + new_questions = [] + for question in questions: + if question["answer"] not in answers: + new_questions.append(question) + answers.add(question["answer"]) + return new_questions + + +def format_to_valid(questions): + answers_txt = [e["answer"] for e in questions] + questions_txt = [e["question"] for e in questions] + vectorizer = TfidfVectorizer() + vectorizer.fit(answers_txt + questions_txt) + answer_vectors = vectorizer.transform(answers_txt) + for i, question in enumerate(questions): + similarities = linear_kernel(answer_vectors[[i]], answer_vectors).flatten() + answer_scores = [(j, sim) for j, sim in enumerate(similarities) if sim != 1] + answer_scores = sorted(answer_scores, key=lambda x: x[1], reverse=True) + sorted_answers = [questions[j]["answer"] for j, _ in answer_scores if questions[j]["answer"] != question["answer"]] + negative_answer = sorted_answers[0] + assert question["answer"] not in sorted_answers + question["candidates"] = [question["answer"]] + sorted_answers + question["negative_example"] = negative_answer + return questions + + def valid_train_split(filename, mapping=None): previous_domain = "" train = [] @@ -36,19 +66,24 @@ def valid_train_split(filename, mapping=None): elif len(domain_data["questions"]) < 15: train.extend(domain_data["questions"]) else: - valid.extend(domain_data["questions"]) + questions = filter_valid(domain_data["questions"]) + if len(questions) < 15: + train.extend(questions) + else: + questions = format_to_valid(questions) + valid.extend(questions) domain_data = {"questions": [], "pages": set()} - domain_data["questions"].append(line_txt.strip()) + domain_data["questions"].append(line) domain_data["pages"].add(line["domain_index"]) previous_domain = domain train.extend(domain_data["questions"]) - assert len(train) + len(valid) == counter return train, valid, filename +domain_count = collections.defaultdict(list) data = [f"data/{e}" for e in os.listdir("data") if e.endswith(".json")] -with multiprocessing.Pool(os.cpu_count()) as p: - domain_count = collections.defaultdict(list) +# with multiprocessing.Pool(os.cpu_count()) as p: +with multiprocessing.Pool(1) as p: for filename, domains in tqdm.tqdm(p.imap_unordered(extract_domains, data)): language = filename.split(".")[1] for domain in domains: @@ -59,6 +94,8 @@ with multiprocessing.Pool(os.cpu_count()) as p: fn = functools.partial(valid_train_split, mapping=domain_count) for train, valid, filename in tqdm.tqdm(p.imap_unordered(fn, data)): train_filename = filename.replace("data/", "data/train/") + train = [json.dumps(e, ensure_ascii=False) for e in train] + valid = [json.dumps(e, ensure_ascii=False) for e in valid] with open(train_filename, "w+") as f: train = "\n".join(train) f.write(train)