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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 json | |
| import os | |
| import faiss | |
| import numpy as np | |
| from tqdm import tqdm | |
| from transformers import BertModel, BertTokenizer | |
| def encode_passage(text, tokenizer, model, device='cuda:0'): | |
| max_length = 154 # hardcode for now | |
| inputs = tokenizer( | |
| '[CLS] [D] ' + text, | |
| max_length=max_length, | |
| truncation=True, | |
| add_special_tokens=False, | |
| return_tensors='pt' | |
| ) | |
| inputs.to(device) | |
| outputs = model(**inputs) | |
| embeddings = outputs.last_hidden_state.detach().cpu().numpy() | |
| return np.mean(embeddings[:, 4:, :], axis=-2).flatten() | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--encoder', type=str, help='encoder name or path', required=True) | |
| parser.add_argument('--dimension', type=int, help='dimension of passage embeddings', required=False, default=768) | |
| parser.add_argument('--corpus', type=str, | |
| help='directory that contains corpus files to be encoded, in jsonl format.', required=True) | |
| parser.add_argument('--index', type=str, help='directory to store brute force index of corpus', required=True) | |
| parser.add_argument('--device', type=str, help='device cpu or cuda [cuda:0, cuda:1...]', default='cuda:0') | |
| args = parser.parse_args() | |
| tokenizer = BertTokenizer.from_pretrained(args.encoder) | |
| model = BertModel.from_pretrained(args.encoder) | |
| model.to(args.device) | |
| index = faiss.IndexFlatIP(args.dimension) | |
| if not os.path.exists(args.index): | |
| os.mkdir(args.index) | |
| with open(os.path.join(args.index, 'docid'), 'w') as id_file: | |
| for file in sorted(os.listdir(args.corpus)): | |
| file = os.path.join(args.corpus, file) | |
| if file.endswith('json'): | |
| print(f'Encoding {file}') | |
| with open(file, 'r') as corpus: | |
| for idx, line in enumerate(tqdm(corpus.readlines())): | |
| info = json.loads(line) | |
| docid = info['id'] | |
| text = info['contents'] | |
| id_file.write(f'{docid}\n') | |
| embedding = encode_passage(text, tokenizer, model, args.device) | |
| index.add(np.array([embedding])) | |
| faiss.write_index(index, os.path.join(args.index, 'index')) |