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:
- 24fc5e8d829f0da4e88643d67d878308e009e3d950410d860c176657b05b5a0d
- Size of remote file:
- 268 MB
- SHA256:
- 2e6e367ebcec72f87f5431d4f37f2d2cf44245da3ba82faecab89ca8998a8260
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.