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")# 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
- Xet hash:
- 3fca76a2caaf9028a51949829513cc0af8782e36e9719289e23c108d200e9312
- Size of remote file:
- 499 MB
- SHA256:
- 85cf3d22c5ddf332ef20a787904d9f9e91c003a948fe68bae406c94abaa9515d
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