Instructions to use KalaiselvanD/model_bert_check_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_check_1: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/model_bert_check_1/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_check_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_check_1/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 4e2b7a361d2f60484f6b59ec9d4f8229747b1657593e111aa3d08e1f3af7033f
- Size of remote file:
- 4.98 kB
- SHA256:
- 8d2ae27bdd93bd941a6ae81a6c2e15150f47c46f7a6403383a88bb8cb28d15dd
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