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 spiece.model from KalaiselvanD/bigbert_1: direct link, hf CLI and curl.
- Browser
- Download file 846 kB
-
https://huggingface.co/KalaiselvanD/bigbert_1/resolve/main/spiece.model
- Command line
-
hf download hf://KalaiselvanD/bigbert_1/spiece.model
-
curl -L -o spiece.model https://huggingface.co/KalaiselvanD/bigbert_1/resolve/main/spiece.model
846 kB
- Xet hash:
- 865c5bb1ab7feef5206326bba00b9da85b0d6394e84b447b0fdd97a24b223cf3
- Size of remote file:
- 846 kB
- SHA256:
- fdc81e1fc9d42e0c08b86d5b280d05d7c5e9747c4231c648f2b56b8e1d893c82
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.