Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_v2_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_v2_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_v2_wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_v2_wnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_v2_wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from henryscheible/eval_v2_wnli: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/eval_v2_wnli/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/eval_v2_wnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/henryscheible/eval_v2_wnli/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 2540d26f07de47e8a147ccece9b933dff98c8cb126039d3c347e065bb09a808d
- Size of remote file:
- 3.38 kB
- SHA256:
- 99681700cce1c539a685672853a41f7a1cb27ef463c17fa642580cfe65965840
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