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 (legacy)
Instructions to use Geolumina/instructor-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Geolumina/instructor-xl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Geolumina/instructor-xl") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Geolumina/instructor-xl with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geolumina/instructor-xl") model = AutoModel.from_pretrained("Geolumina/instructor-xl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 867 Bytes
de7f614 46ee5d9 e11605b 46ee5d9 de7f614 46ee5d9 de7f614 46ee5d9 | 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 28 29 | 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)
# make sure texts is a list
if not isinstance(inputs, list):
inputs = [inputs]
instruction = 'Retrieve documents that can help answer the question:'
instructions = [[instruction, text] for text in inputs]
embeddings = self.model.encode(instructions)
return embeddings.tolist() |