Source code for asreview.models.feature_extraction.sbert

# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

__all__ = ["SBERT"]

    from sentence_transformers import models
    from sentence_transformers.SentenceTransformer import SentenceTransformer
except ImportError:
    ST_AVAILABLE = False

from asreview.models.feature_extraction.base import BaseFeatureExtraction

def _check_st():
    if not ST_AVAILABLE:
        raise ImportError("Install sentence-transformers package to use Sentence BERT.")

[docs] class SBERT(BaseFeatureExtraction): """Sentence BERT feature extraction technique (``sbert``). By setting the ``transformer_model`` parameter, you can use other transformer models. For example, ``transformer_model='bert-base-nli-stsb- large'``. For a list of available models, see the `Sentence BERT documentation <>`__. Sentence BERT is a sentence embedding model that is trained on a large corpus of human written text. It is a fast and accurate model that can be used for many tasks. The huggingface library includes multilingual text classification models. If your dataset contains records with multiple languages, you can use the ``transformer_model`` parameter to select the model that is most suitable for your data. .. note:: This feature extraction technique requires ``sentence_transformers`` to be installed. Use ``pip install asreview[sentence_transformers]`` or install all optional ASReview dependencies with ``pip install asreview[all]`` to install the package. Parameters ---------- transformer_model : str, optional The transformer model to use. Default: 'all-mpnet-base-v2' is_pretrained_SBERT: boolean, optional Default: True pooling_mode: str, optional Pooling mode to get sentence embeddings from word embeddings Default: 'mean' Other options available are 'mean', 'max' and 'cls'. Only used if is_pretrained_SBERT=False mean: Uses mean pooling of word embeddings max: Uses max pooling of word embeddings cls: Uses embeddings of [CLS] token as sentence embeddings """ name = "sbert" label = "Sentence BERT" def __init__( self, *args, transformer_model="all-mpnet-base-v2", is_pretrained_sbert=True, pooling_mode="mean", **kwargs, ): super().__init__(*args, **kwargs) self.transformer_model = transformer_model self.is_pretrained_sbert = is_pretrained_sbert self.pooling_mode = pooling_mode
[docs] def transform(self, texts): _check_st() if self.is_pretrained_sbert: model = SentenceTransformer(self.transformer_model) else: # If transformer_model is not a pretrained sentence transformer model, # add a pooling layer to get the pooled sentence embeddings from the # word embeddings word_embedding_model = models.Transformer(self.transformer_model) pooling_layer = models.Pooling( word_embedding_model.get_word_embedding_dimension(), pooling_mode=self.pooling_mode, ) model = SentenceTransformer(modules=[word_embedding_model, pooling_layer]) print("Encoding texts using sbert, this may take a while...") X = model.encode(texts, show_progress_bar=True) return X