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 | |
class EndpointHandler: | |
def __init__(self, path=""): | |
# load model on gpu | |
self.model = INSTRUCTOR(path, device="cuda") | |
def __call__(self, data: Dict[str, Any]) -> List[List[float]]: | |
""" | |
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 not texts or not instruction: | |
raise ValueError("Please provide texts and instruction") | |
# make sure texts is a list | |
if not isinstance(texts, list): | |
texts = [texts] | |
instructions = [[instruction, text] for text in texts] | |
embeddings = self.model.encode(instructions) | |
return embeddings.tolist() | |