Instructions to use KalaiselvanD/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/output") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/output: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KalaiselvanD/output/resolve/c203e3b992e3ebdefe9d6f504b05ce7061ba67dd/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/output@c203e3b992e3ebdefe9d6f504b05ce7061ba67dd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/output/resolve/c203e3b992e3ebdefe9d6f504b05ce7061ba67dd/training_args.bin
4.92 kB
- Xet hash:
- 6f2be2df1f5e7b782a2a895c997c67e476e3fd422554b3b4a17c71af78085abb
- Size of remote file:
- 4.92 kB
- SHA256:
- 195fb90ae8620ee7fbc98a15d162362ff22e2bbfd4792a659c08ba8445b8d596
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