Instructions to use KalaiselvanD/model_albert_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert_5000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert_5000") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert_5000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_albert_5000: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KalaiselvanD/model_albert_5000/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_albert_5000/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_albert_5000/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 7ed465a2dfa49235e9f205aef0dffacc97d5be6ecf57b05645848443aa6d0c73
- Size of remote file:
- 4.92 kB
- SHA256:
- cd9287da04db6994756c8d9e00ce00b031a5c7055de7608cb96bdf3c9447e166
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