Instructions to use KalaiselvanD/bigbert_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/bigbert_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/bigbert_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/bigbert_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/bigbert_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/bigbert_1: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/bigbert_1/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/bigbert_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/bigbert_1/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 0b4be7fcd02916c52620c2e57bc05cda08933bd044fd7c0c721d3da0f1018cdc
- Size of remote file:
- 4.98 kB
- SHA256:
- d2a69f8e475e6a9e9fe1a14dc1623eae192fa5e461980a10a47fd8fc3dbfd6f6
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