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
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] | |
embeddings = self.model.encode(inputs) | |
return embeddings.tolist() |