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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 pandas as pd | |
| from tqdm import tqdm | |
| from pyserini.query_iterator import get_query_iterator, TopicsFormat | |
| from transformers import BertModel, BertTokenizerFast | |
| import torch | |
| class DkrrDprQueryEncoder(): | |
| def __init__(self, encoder: str = None, device: str = 'cpu', prefix: str = "question:"): | |
| self.device = device | |
| self.model = BertModel.from_pretrained(encoder) | |
| self.model.to(self.device) | |
| self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") | |
| self.prefix = prefix | |
| def _mean_pooling(model_output, attention_mask): | |
| model_output = model_output[0].masked_fill(1 - attention_mask[:, :, None], 0.) | |
| model_output = torch.sum(model_output, dim=1) / torch.clamp(torch.sum(attention_mask, dim=1), min=1e-9)[:, None] | |
| return model_output.flatten() | |
| def encode(self, query: str): | |
| if self.prefix: | |
| query = f'{self.prefix} {query}' | |
| inputs = self.tokenizer(query, return_tensors='pt', max_length=40, padding="max_length") | |
| inputs.to(self.device) | |
| outputs = self.model(input_ids=inputs["input_ids"], | |
| attention_mask=inputs["attention_mask"]) | |
| embeddings = self._mean_pooling(outputs, inputs['attention_mask']).detach().cpu().numpy() | |
| return embeddings.flatten() | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--topics', type=str, metavar='topic_name', required=True, | |
| help="Name of topics.") | |
| parser.add_argument('--encoder', type=str, help='encoder name or path', | |
| default='facebook/dpr-question_encoder-multiset-base', 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() | |
| query_iterator = get_query_iterator(args.topics, TopicsFormat(TopicsFormat.DEFAULT.value)) | |
| topics = query_iterator.topics | |
| encoder = DkrrDprQueryEncoder(args.encoder, args.device) | |
| embeddings = {'id': [], 'text': [], 'embedding': []} | |
| for index, (topic_id, text) in enumerate(tqdm(query_iterator, total=len(topics.keys()))): | |
| embeddings['id'].append(topic_id) | |
| embeddings['text'].append(text) | |
| embeddings['embedding'].append(encoder.encode(text)) | |
| embeddings = pd.DataFrame(embeddings) | |
| embeddings.to_pickle(args.output) | |