Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use k4black/roberta-base-e-snli-classification-nli-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k4black/roberta-base-e-snli-classification-nli-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="k4black/roberta-base-e-snli-classification-nli-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("k4black/roberta-base-e-snli-classification-nli-base") model = AutoModelForSequenceClassification.from_pretrained("k4black/roberta-base-e-snli-classification-nli-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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update model card README.md
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Model save
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