Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use k4black/roberta-large-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-large-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-large-e-snli-classification-nli-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("k4black/roberta-large-e-snli-classification-nli-base") model = AutoModelForSequenceClassification.from_pretrained("k4black/roberta-large-e-snli-classification-nli-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d2dd68ebae7921aafa992569728d73e31aeb073a1dec40a92066d9bc68b6312
- Size of remote file:
- 3.7 kB
- SHA256:
- 3c226ff8a79fc8c43647efdd29cbdbc642d1c7fa8c770b0a81535e6a61aee411
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