Instructions to use rsaketh02/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsaketh02/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rsaketh02/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rsaketh02/model") model = AutoModelForSequenceClassification.from_pretrained("rsaketh02/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rsaketh02/model: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/rsaketh02/model/resolve/main/training_args.bin
- Command line
-
hf download hf://rsaketh02/model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rsaketh02/model/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- ee7286212609f55392f71d5e6b192961e2c608dc4be515480fb2a28fa988b818
- Size of remote file:
- 4.92 kB
- SHA256:
- 83f4b3297322a144916d9db5d2563f4f9fa2e23a49134c2687de69776d472cdd
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