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
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from typing import Dict, List, Any
from InstructorEmbedding import INSTRUCTOR
import torch
class EndpointHandler():
def __init__(self, path=""):
model = INSTRUCTOR(path)
self.model = model
if torch.cuda.is_available():
self.device = torch.device("cuda")
self.model.to(self.device)
else:
self.device = torch.device("cpu")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
instruction = data.pop("instruction",data)
text = data.pop("text", data)
inputs = [[s, instruction] for s in text]
if (self.device):
inputs = torch.tensor(inputs).to(self.device) # Move inputs to the GPU
embeddings = self.model.encode(inputs)
return embeddings.tolist() |