Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Matjac5/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M3_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
How to use Matjac5/MNLP_M3_document_encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Matjac5/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("Matjac5/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Matjac5/MNLP_M3_document_encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Matjac5/MNLP_M3_document_encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://Matjac5/MNLP_M3_document_encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Matjac5/MNLP_M3_document_encoder/resolve/main/model.safetensors
438 MB
- Xet hash:
- fa137f83faf75206c68cc96266525ff0a91cdcf35f2c7ac25e1fb838f0065fb6
- Size of remote file:
- 438 MB
- SHA256:
- 745fea2f7ed71c2c046e5ee6171d50ddc475603615e56e27f33aed9024b43189
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