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nthakur
/
mMiniLMv2-L12-H384-ms-marco-all-epoch-40

Feature Extraction
Transformers
PyTorch
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40")
    model = AutoModelForMultimodalLM.from_pretrained("nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40")
  • Notebooks
  • Google Colab
  • Kaggle
mMiniLMv2-L12-H384-ms-marco-all-epoch-40
493 MB
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  • 1 contributor
History: 3 commits
nthakur's picture
nthakur
Upload tokenizer.json
ae98e13 almost 4 years ago
  • .gitattributes
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    Upload tokenizer.json almost 4 years ago
  • config.json
    793 Bytes
    initial model add almost 4 years ago
  • pytorch_model.bin
    471 MB
    xet
    initial model add almost 4 years ago
  • sentencepiece.bpe.model
    5.07 MB
    xet
    initial model add almost 4 years ago
  • special_tokens_map.json
    280 Bytes
    initial model add almost 4 years ago
  • tokenizer.json
    17.1 MB
    xet
    Upload tokenizer.json almost 4 years ago
  • tokenizer_config.json
    488 Bytes
    initial model add almost 4 years ago