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 pytorch_model.bin from henryscheible/wnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/wnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/wnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/wnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 46f35167863432f94e43ec6fd8dbed99f8f248f5b6eb469df276d45e6eeec548
- Size of remote file:
- 438 MB
- SHA256:
- 3a1451025650c3de2345df7f435be446eaec6122cabb934ad7ded65d9b763ce4
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