Instructions to use KalaiselvanD/model_bert_check_new_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_new_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_new_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_new_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_new_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_check_new_2: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/model_bert_check_new_2/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_check_new_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_check_new_2/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- cdc3f4ff814db7e741ff1e368d69ddf1167573cb3d40b3475eb9868c6a79d431
- Size of remote file:
- 4.98 kB
- SHA256:
- 02b1f6bba04b27a8ee03e88cd98c59ebe33d2bcc9d2c9d11f7b62ae05fe2c936
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