Instructions to use KalaiselvanD/albert_model__29_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/albert_model__29_2: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/albert_model__29_2/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/albert_model__29_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/albert_model__29_2/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 73bf783ac16a3a10a8c6ccd6ed26a6fe179f38e5ea368110f7314a50ba2eebaf
- Size of remote file:
- 4.98 kB
- SHA256:
- 3619f14b978204c5cf8b6a56363f0ee56ea88d1225658cd61e465a0fdf4d5306
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