Instructions to use bnabin/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnabin/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bnabin/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bnabin/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("bnabin/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bnabin/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/bnabin/tmp_trainer/resolve/main/training_args.bin
- Command line
-
hf download hf://bnabin/tmp_trainer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bnabin/tmp_trainer/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 6856171121a8cf0707f7c0da5ae6c0cdf1997c1362aac8a34b52c6916aed7587
- Size of remote file:
- 5.3 kB
- SHA256:
- 9fd24f92ad6d7b98ad66d3451a4802b1626c9a6877934ea654f9b3ab826c12c8
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