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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() | |