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MoritzLaurer
/
mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

Zero-Shot Classification
Transformers
PyTorch
ONNX
Safetensors
deberta-v2
text-classification
nli
Eval Results (legacy)
Model card Files Files and versions
xet
Community
11

Instructions to use MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
    model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

License Conflict: MIT vs CC BY-NC 4.0

1
#11 opened 12 months ago by
qiuqiu666

Interpretation Text/Sec , Speed Decreases with Text Length and Speed impact of Increase of number of labels (multi lable)

2
#10 opened about 1 year ago by
thedamsch

How to load onnxx model

#6 opened over 2 years ago by
erickdp

fine tune

4
#4 opened over 2 years ago by
mansoorhamidzadeh

changing to num_labels 2

1
#2 opened over 3 years ago by
3r1c
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