Instructions to use KalaiselvanD/albert_model__29_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_5") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/albert_model__29_5: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/albert_model__29_5/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/albert_model__29_5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/albert_model__29_5/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 468e07c2ad2ffaee6e011dfe198c61a86207c58411ffa80e1d5846d0dff941c3
- Size of remote file:
- 4.98 kB
- SHA256:
- 76cf6a6ef413e5dd6ced6abe7172b5dfd9987840da5082b08fc4fa10ef2d1c5e
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