Instructions to use sgugger/debug-example2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/debug-example2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/debug-example2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/debug-example2") model = AutoModelForSequenceClassification.from_pretrained("sgugger/debug-example2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sgugger/debug-example2: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/sgugger/debug-example2/resolve/main/training_args.bin
- Command line
-
hf download hf://sgugger/debug-example2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sgugger/debug-example2/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- cd3dcf95b685bcc005ad64512c297df66d5c7211e8ac7dbff1768b92927d1efe
- Size of remote file:
- 2.99 kB
- SHA256:
- b8cf6c34fa6f6cf3ca7ab879352370443d46866f5bcc0b1bc2b2e71a5b13dfe5
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