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# copy from https://github.com/huggingface/api-inference-community/blob/main/docker_images/sentence_transformers/app/pipelines/sentence_similarity.py
import os
from typing import Dict, List, Union
from sentence_transformers import SentenceTransformer, util
class PreTrainedPipeline:
def __init__(
self,
model_id: str,
):
self.model = SentenceTransformer(
model_id, use_auth_token=os.getenv("HF_API_TOKEN")
)
def __call__(self, inputs: Dict[str, Union[str, List[str]]]) -> List[float]:
"""
Args:
inputs (:obj:`dict`):
a dictionary containing two keys, 'source_sentence' mapping
to the sentence that will be compared against all the others,
and 'sentences', mapping to a list of strings to which the
source will be compared.
Return:
A :obj:`list` of floats: Cosine similarity between `source_sentence` and each sentence from `sentences`.
"""
embeddings1 = self.model.encode(
inputs["source_sentence"], convert_to_tensor=True
)
embeddings2 = self.model.encode(inputs["sentences"], convert_to_tensor=True)
similarities = util.pytorch_cos_sim(embeddings1, embeddings2).tolist()[0]
return similarities
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