Instructions to use LeonKogler/trainedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeonKogler/trainedModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LeonKogler/trainedModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LeonKogler/trainedModel") model = AutoModelForSequenceClassification.from_pretrained("LeonKogler/trainedModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LeonKogler/trainedModel: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/LeonKogler/trainedModel/resolve/main/training_args.bin
- Command line
-
hf download hf://LeonKogler/trainedModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/LeonKogler/trainedModel/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 8bc264c453f0d16ee1a51f091a0e5ae29bd5734d6a8e1da755f5b6bb330cc8c0
- Size of remote file:
- 5.05 kB
- SHA256:
- 25d3127b7db48c5d77e6f9ee2875278259582122425a287f0b2f5326f76e4ef4
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