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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 numpy as np | |
| from sklearn.preprocessing import normalize | |
| from transformers import AutoModel, AutoTokenizer | |
| from pyserini.encode import DocumentEncoder, QueryEncoder | |
| class AutoDocumentEncoder(DocumentEncoder): | |
| def __init__(self, model_name, tokenizer_name=None, device='cuda:0', pooling='cls', l2_norm=False): | |
| self.device = device | |
| self.model = AutoModel.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| try: | |
| self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name) | |
| except: | |
| self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name, use_fast=False) | |
| self.has_model = True | |
| self.pooling = pooling | |
| self.l2_norm = l2_norm | |
| def encode(self, texts, titles=None, max_length=256, add_sep=False, **kwargs): | |
| shared_tokenizer_kwargs = dict( | |
| max_length=max_length, | |
| truncation=True, | |
| padding='longest', | |
| return_attention_mask=True, | |
| return_token_type_ids=False, | |
| return_tensors='pt', | |
| add_special_tokens=True, | |
| ) | |
| input_kwargs = {} | |
| if not add_sep: | |
| input_kwargs["text"] = [f'{title} {text}' for title, text in zip(titles, texts)] if titles is not None else texts | |
| else: | |
| if titles is not None: | |
| input_kwargs["text"] = titles | |
| input_kwargs["text_pair"] = texts | |
| else: | |
| input_kwargs["text"] = texts | |
| inputs = self.tokenizer(**input_kwargs, **shared_tokenizer_kwargs) | |
| inputs.to(self.device) | |
| outputs = self.model(**inputs) | |
| if self.pooling == "mean": | |
| embeddings = self._mean_pooling(outputs[0], inputs['attention_mask']).detach().cpu().numpy() | |
| else: | |
| embeddings = outputs[0][:, 0, :].detach().cpu().numpy() | |
| if self.l2_norm: | |
| embeddings = normalize(embeddings, axis=1) | |
| return embeddings | |
| class AutoQueryEncoder(QueryEncoder): | |
| def __init__(self, model_name: str, tokenizer_name: str = None, device: str = 'cpu', | |
| pooling: str = 'cls', l2_norm: bool = False, prefix=None): | |
| self.device = device | |
| self.model = AutoModel.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name) | |
| self.pooling = pooling | |
| self.l2_norm = l2_norm | |
| self.prefix = prefix | |
| def encode(self, query: str, **kwargs): | |
| if self.prefix: | |
| query = f'{self.prefix} {query}' | |
| inputs = self.tokenizer( | |
| query, | |
| add_special_tokens=True, | |
| return_tensors='pt', | |
| truncation='only_first', | |
| padding='longest', | |
| return_token_type_ids=False, | |
| ) | |
| inputs.to(self.device) | |
| outputs = self.model(**inputs)[0].detach().cpu().numpy() | |
| if self.pooling == "mean": | |
| embeddings = np.average(outputs, axis=-2) | |
| else: | |
| embeddings = outputs[:, 0, :] | |
| if self.l2_norm: | |
| embeddings = normalize(outputs, norm='l2') | |
| return embeddings.flatten() | |