Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results
Inference Endpoints
File size: 1,058 Bytes
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from typing import Dict, List, Any
from InstructorEmbedding import INSTRUCTOR
INSTRUCTION_SEPARATOR = "|||"
class EndpointHandler:
def __init__(self, path=""):
# load model
self.model = INSTRUCTOR(path, device="cuda")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs: dict = data.pop("inputs", data)
texts = inputs.pop("texts", None)
instruction = inputs.pop("instruction", None)
# if isinstance(inputs, str):
# inputs = [inputs]
# run normal prediction
# scores = self.model.predict_proba(inputs)[0]
# return [{"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)]
instructions = [[instruction, text] for text in texts]
embeddings = self.model.encode(instructions)
return embeddings.tolist()
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