"""
sBERT for Sentence Similarity
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
from textattack.constraints.semantics.sentence_encoders import SentenceEncoder
from textattack.shared import utils
sentence_transformers = utils.LazyLoader(
"sentence_transformers", globals(), "sentence_transformers"
)
[docs]class SBERT(SentenceEncoder):
"""Constraint using similarity between sentence encodings of x and x_adv
where the text embeddings are created using BERT, trained on NLI data, and
fine- tuned on the STS benchmark dataset.
Available models can be found here: https://huggingface.co/sentence-transformers
"""
def __init__(
self,
threshold=0.7,
metric="cosine",
model_name="bert-base-nli-stsb-mean-tokens",
**kwargs
):
super().__init__(threshold=threshold, metric=metric, **kwargs)
self.model = sentence_transformers.SentenceTransformer(model_name)
self.model.to(utils.device)
[docs] def encode(self, sentences):
return self.model.encode(sentences)