Instructions to use Greg1901/BertSummaDev_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Greg1901/BertSummaDev_summariser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Greg1901/BertSummaDev_summariser")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Greg1901/BertSummaDev_summariser") model = AutoModelForSequenceClassification.from_pretrained("Greg1901/BertSummaDev_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Greg1901/BertSummaDev_summariser: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Greg1901/BertSummaDev_summariser/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Greg1901/BertSummaDev_summariser/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Greg1901/BertSummaDev_summariser/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b789b751617353e98b638db8d42793b9e65d2da281d051b9d1469c5d11f1acf0
- Size of remote file:
- 438 MB
- SHA256:
- 138b1b30122393d8ece719c83f7f73c9a738781198d63d67a1909aaede10c82a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.