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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. | |
| # | |
| from transformers import DPRContextEncoder, DPRContextEncoderTokenizer, DPRQuestionEncoder, DPRQuestionEncoderTokenizer | |
| from pyserini.encode import DocumentEncoder, QueryEncoder | |
| class DprDocumentEncoder(DocumentEncoder): | |
| def __init__(self, model_name, tokenizer_name=None, device='cuda:0'): | |
| self.device = device | |
| self.model = DPRContextEncoder.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = DPRContextEncoderTokenizer.from_pretrained(tokenizer_name or model_name) | |
| def encode(self, texts, titles=None, max_length=256, **kwargs): | |
| if titles: | |
| inputs = self.tokenizer( | |
| titles, | |
| text_pair=texts, | |
| max_length=max_length, | |
| padding='longest', | |
| truncation=True, | |
| add_special_tokens=True, | |
| return_tensors='pt' | |
| ) | |
| else: | |
| inputs = self.tokenizer( | |
| texts, | |
| max_length=max_length, | |
| padding='longest', | |
| truncation=True, | |
| add_special_tokens=True, | |
| return_tensors='pt' | |
| ) | |
| inputs.to(self.device) | |
| return self.model(inputs["input_ids"]).pooler_output.detach().cpu().numpy() | |
| class DprQueryEncoder(QueryEncoder): | |
| def __init__(self, model_name: str, tokenizer_name: str = None, device: str = 'cpu'): | |
| self.device = device | |
| self.model = DPRQuestionEncoder.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(tokenizer_name or model_name) | |
| def encode(self, query: str, **kwargs): | |
| input_ids = self.tokenizer(query, return_tensors='pt') | |
| input_ids.to(self.device) | |
| embeddings = self.model(input_ids["input_ids"]).pooler_output.detach().cpu().numpy() | |
| return embeddings.flatten() | |