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: 893 Bytes
345a2f0 cc0dbd1 345a2f0 6cf0f91 345a2f0 3550d34 345a2f0 d286efc 5761c76 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 |
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() |