Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/qnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from henryscheible/qnli: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/qnli/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/qnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/henryscheible/qnli/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 55f83f0cdbe0d19324455a60f437829c7945912149ca112de02262d7a4ad068b
- Size of remote file:
- 3.38 kB
- SHA256:
- 347eb2f4653ff56ca9b9cb1337bc4071c97e1126730bc3f701649e09a0a4b303
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