Instructions to use KalaiselvanD/albert_model__29_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/albert_model__29_1: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/albert_model__29_1/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/albert_model__29_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/albert_model__29_1/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 3e913acb37359f3e31d67a4dd4f9a1930d1cd0c346fbfec896dbdc8090a7b1d9
- Size of remote file:
- 4.98 kB
- SHA256:
- 068bf9fb9340f0d4b2f8d157d637f0e3cb41eb3f6a57ca0a21ac767e29b7902b
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