Instructions to use KalaiselvanD/albert_model__25_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__25_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__25_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__25_4") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__25_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/albert_model__25_4: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/albert_model__25_4/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/albert_model__25_4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/albert_model__25_4/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- edc7e219a7208f7345f33d42cf70c380639a8153cd90052c9338edc69410dbe3
- Size of remote file:
- 4.98 kB
- SHA256:
- 5a1b58fbea94e2c786da83239d46fc15344861b35bcb818870b2eed2fff0fffe
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