Instructions to use KalaiselvanD/kalai_bert_model_test_4_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/kalai_bert_model_test_4_out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/kalai_bert_model_test_4_out")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/kalai_bert_model_test_4_out") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/kalai_bert_model_test_4_out", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/kalai_bert_model_test_4_out: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/kalai_bert_model_test_4_out/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/kalai_bert_model_test_4_out/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/kalai_bert_model_test_4_out/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 9b81da7d7c0e8a59a815c4e5ddfa49005a9b148eb5cf628f2daf31c54e3e3ae8
- Size of remote file:
- 4.98 kB
- SHA256:
- e186b6b5e59fb5be9de5caf710d8cd66487520a54fdd18e060f4d4815d2ba8ab
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