Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/wnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from henryscheible/wnli: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/wnli/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/wnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/henryscheible/wnli/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 46c51cf086b9a6b469302215f343f15a369e08005ee071e918c30f7a5caeebd3
- Size of remote file:
- 3.38 kB
- SHA256:
- 8db9d4697016c2abe2e4213df297a2c9d03164c94d3dbc7a627e58627be33e17
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