- Libraries
- sentence-transformers
How to use inesaltemir/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True)
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 inesaltemir/MNLP_M2_document_encoder with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True)
model = AutoModel.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True, device_map="auto") - Transformers.js
How to use inesaltemir/MNLP_M2_document_encoder with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('feature-extraction', 'inesaltemir/MNLP_M2_document_encoder'); - Notebooks
- Google Colab
- Kaggle
