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:
- e7c00322448f39c1f14d0a5c5824fac7e9865b767f734405cfb3f7f8f6fdbf6d
- Size of remote file:
- 2.24 GB
- SHA256:
- 947e0cebe09808e3279f38ceeca58af60c1fcebefbdfa54642285f35a0f8ec57
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