Instructions to use KalaiselvanD/model_albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_albert: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/KalaiselvanD/model_albert/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_albert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_albert/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 680187a40ef547f7c1b2cae171034c065179f6eed2e652c76ac91440250f2d76
- Size of remote file:
- 4.86 kB
- SHA256:
- 3391b09ea9abe18198118ae8d00c31c84948318dae4823888daeb254dbb4d71a
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