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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()
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