Instructions to use jahb57/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jahb57/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jahb57/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jahb57/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("jahb57/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jahb57/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/jahb57/test_trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jahb57/test_trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jahb57/test_trainer/resolve/main/pytorch_model.bin
498 MB
- Xet hash:
- 35fc606dcec37e6432171094208c02bcc1d55c84891a52ebc58eaca092b99aa8
- Size of remote file:
- 498 MB
- SHA256:
- 776457dc7d806156770658e8457dcbd08a48384e69945a6e100acf3d84995449
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