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texonom
/
e5-base-multilingual-4096

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

Instructions to use texonom/e5-base-multilingual-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use texonom/e5-base-multilingual-4096 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="texonom/e5-base-multilingual-4096", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("texonom/e5-base-multilingual-4096", trust_remote_code=True)
    model = AutoModel.from_pretrained("texonom/e5-base-multilingual-4096", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
e5-base-multilingual-4096 / onnx
1.4 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
seonglae's picture
seonglae
Upload folder using huggingface_hub
dea3d7f over 2 years ago
  • model.onnx
    1.12 GB
    xet
    Upload folder using huggingface_hub over 2 years ago
  • model_quantized.onnx
    281 MB
    xet
    Upload folder using huggingface_hub over 2 years ago