Instructions to use KalaiselvanD/model_bert_check_new_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_new_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_new_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_new_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_new_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_check_new_1: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/model_bert_check_new_1/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_check_new_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_check_new_1/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 32bdf72d65d5e2b5030eda8ec93e6a79700ee2b90116abf5682274276abb9168
- Size of remote file:
- 4.98 kB
- SHA256:
- 2956bbeb0e346a487f2ff6f78ead365d9b49baa802d49289c334b2bea3b106e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.