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brahmairesearch
/
TFS-ZS-MNLI

Zero-Shot Classification
Transformers
ONNX
Safetensors
deberta-v2
text-classification
mnli
nlp
tfs
thinking-fast-and-slow
router-model
brahmai
reasoning
Model card Files Files and versions
xet
Community
1

Instructions to use brahmairesearch/TFS-ZS-MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use brahmairesearch/TFS-ZS-MNLI with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-classification", model="brahmairesearch/TFS-ZS-MNLI")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("brahmairesearch/TFS-ZS-MNLI")
    model = AutoModelForSequenceClassification.from_pretrained("brahmairesearch/TFS-ZS-MNLI", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
TFS-ZS-MNLI
2.26 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
mav23's picture
mav23
Adding ONNX file of this model (#1)
49d7453 verified about 1 year ago
  • onnx
    Adding ONNX file of this model (#1) about 1 year ago
  • .gitattributes
    1.63 kB
    Adding ONNX file of this model (#1) about 1 year ago
  • README.md
    6.52 kB
    Adding ONNX file of this model (#1) about 1 year ago
  • config.json
    1.07 kB
    Upload config.json about 1 year ago
  • model.safetensors
    1.12 GB
    xet
    __init__ upload about 1 year ago
  • special_tokens_map.json
    1.02 kB
    __init__ upload about 1 year ago
  • tokenizer.json
    16.3 MB
    xet
    __init__ upload about 1 year ago
  • tokenizer_config.json
    1.35 kB
    __init__ upload about 1 year ago