Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

Mass-14
/
MNLP_M2_document_encoder

Feature Extraction
Transformers
Safetensors
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Mass-14/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Mass-14/MNLP_M2_document_encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Mass-14/MNLP_M2_document_encoder")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("Mass-14/MNLP_M2_document_encoder")
    model = AutoModel.from_pretrained("Mass-14/MNLP_M2_document_encoder")
  • Notebooks
  • Google Colab
  • Kaggle
MNLP_M2_document_encoder
1.34 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Mass-14's picture
Mass-14
Upload tokenizer
65c7135 verified 12 months ago
  • .gitattributes
    1.52 kB
    initial commit 12 months ago
  • README.md
    5.17 kB
    Upload of document encoder (gte-large) 12 months ago
  • config.json
    619 Bytes
    Upload of document encoder (gte-large) 12 months ago
  • model.safetensors
    1.34 GB
    xet
    Upload of document encoder (gte-large) 12 months ago
  • special_tokens_map.json
    695 Bytes
    Upload tokenizer 12 months ago
  • tokenizer.json
    712 kB
    Upload tokenizer 12 months ago
  • tokenizer_config.json
    1.44 kB
    Upload tokenizer 12 months ago
  • vocab.txt
    232 kB
    Upload tokenizer 12 months ago