Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/natural-language-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/natural-language-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/natural-language-inference")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/natural-language-inference") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/natural-language-inference") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24103a26f677e819aae1f0d162de22508c8b352f3167cb0a7e25c30bcc4c8ed5
- Size of remote file:
- 3.44 kB
- SHA256:
- 2d580001df000aa0ef5c2c63e9066b47fd223dd274ffc9351a1e4e9fcfb772c7
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