Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_qnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from henryscheible/eval_qnli: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/eval_qnli/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/eval_qnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/henryscheible/eval_qnli/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- c6261baa01b8bfc2c5b2ea488645617a71df6fdf9c09c5bf8de405d5d9e7a409
- Size of remote file:
- 3.38 kB
- SHA256:
- 664ba78880c1c80a0d3ec84756752dc952c519df8df6038836db1f4d05d37dc4
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