Sentence Similarity
Transformers
Safetensors
sentence-transformers
Transformers.js
English
modernbert
feature-extraction
mteb
embedding
text-embeddings-inference
Instructions to use anasse15/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anasse15/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anasse15/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("anasse15/MNLP_M2_document_encoder") - sentence-transformers
How to use anasse15/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anasse15/MNLP_M2_document_encoder") 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.js
How to use anasse15/MNLP_M2_document_encoder with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'anasse15/MNLP_M2_document_encoder'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13bd38825c0aaebba21d47f0f4a752ee9140f0327c988e8b3b112426084d4d97
- Size of remote file:
- 596 MB
- SHA256:
- 0f9247027e7d57e8b36440b5b3d10a785ded92c7c9f4a313ff7f54a549967290
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