Instructions to use Mahmoud8/bigbird-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/bigbird-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/bigbird-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/bigbird-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/bigbird-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/bigbird-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 512 MB
-
https://huggingface.co/Mahmoud8/bigbird-roberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/bigbird-roberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/bigbird-roberta-base/resolve/main/pytorch_model.bin
512 MB
- Xet hash:
- 3adee4402c20a8bd2f7d491f94f100253de28282174e5d786be4d03f9334ed43
- Size of remote file:
- 512 MB
- SHA256:
- 07310d9485478676dde76b55aa9de224db3f9f1c35ae0fa6e849b10337dd62c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.