Zero-Shot Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
text-classification
tensorflow
nli
natural-language-inference
Eval Results (legacy)
Instructions to use nahiar/zero-shot-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/zero-shot-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="nahiar/zero-shot-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/zero-shot-classification") model = AutoModelForSequenceClassification.from_pretrained("nahiar/zero-shot-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae6bfa47786d8f8c98f504251c244782f0e3034752daa232e20bb8291e63d034
- Size of remote file:
- 2.24 GB
- SHA256:
- 8869b0c99ad35ec8a8c92434b54383d2dfd7db8cd460e28b9944a407e3a423e4
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