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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| import argparse | |
| import csv | |
| import json | |
| import pandas as pd | |
| from tqdm import tqdm | |
| from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer | |
| def parse_qa_csv_file(location): | |
| with open(location) as file: | |
| reader = csv.reader(file, delimiter='\t') | |
| for row in reader: | |
| question = row[0] | |
| answers = eval(row[1]) | |
| yield question, answers | |
| def parse_qa_json_file(location): | |
| with open(location) as file: | |
| for line in file: | |
| qa = json.loads(line) | |
| question = qa['question'] | |
| answers = qa['answer'] | |
| yield question, answers | |
| def encode_query(text, tokenizer, model, device='cpu'): | |
| input_ids = tokenizer(text, return_tensors='pt') | |
| input_ids.to(device) | |
| embeddings = model(input_ids["input_ids"]).pooler_output.detach().cpu().numpy() | |
| return embeddings.flatten() | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--encoder', type=str, help='encoder name or path', | |
| default='facebook/dpr-question_encoder-multiset-base', required=False) | |
| parser.add_argument('--input', type=str, help='qas file, json file by default', required=True) | |
| parser.add_argument('--format', type=str, help='qas file format', default='json', required=False) | |
| parser.add_argument('--output', type=str, help='path to store query embeddings', required=True) | |
| parser.add_argument('--device', type=str, | |
| help='device cpu or cuda [cuda:0, cuda:1...]', default='cpu', required=False) | |
| args = parser.parse_args() | |
| tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(args.encoder) | |
| model = DPRQuestionEncoder.from_pretrained(args.encoder) | |
| model.to(args.device) | |
| embeddings = {'id': [], 'text': [], 'embedding': []} | |
| qa_parser = None | |
| if args.format == 'csv': | |
| qa_parser = parse_qa_csv_file | |
| elif args.format == 'json': | |
| qa_parser = parse_qa_json_file | |
| if qa_parser is None: | |
| print(f'No QA parser defined for file format: {args.format}, or format not match') | |
| for qid, (question, answers) in enumerate(tqdm(list(qa_parser(args.input)))): | |
| embeddings['id'].append(qid) | |
| embeddings['text'].append(question) | |
| embeddings['embedding'].append(encode_query(question, tokenizer, model, args.device)) | |
| embeddings = pd.DataFrame(embeddings) | |
| embeddings.to_pickle(args.output) | |