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 pytorch_model.bin from henryscheible/qnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/qnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/qnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/qnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 047355a7a46448a55e311d05820972b93144f2077f2e88b75c633968607f9b79
- Size of remote file:
- 438 MB
- SHA256:
- 381c6759c33afed8380901c2052d5ae1d46e5811df22f9f76a1cff3267c21d86
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