Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use inesaltemir/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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:
# 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) - 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('sentence-similarity', 'inesaltemir/MNLP_M2_document_encoder'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65faff9ae2931b8d075b78d1d2be1e21122ee0debf74ab4bb8f1c49e79b75a0a
- Size of remote file:
- 547 MB
- SHA256:
- 47e396424a085a613034450cd4bf9e8acfb568b19089ae1c5c4e7051ae286877
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.