Instructions to use sheoran95/augmented_data_without_edge_document_level_baseBART_run1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sheoran95/augmented_data_without_edge_document_level_baseBART_run1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sheoran95/augmented_data_without_edge_document_level_baseBART_run1") model = AutoModelForSeq2SeqLM.from_pretrained("sheoran95/augmented_data_without_edge_document_level_baseBART_run1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sheoran95/augmented_data_without_edge_document_level_baseBART_run1: direct link, hf CLI and curl.
- Browser
- Download file 3.84 kB
-
https://huggingface.co/sheoran95/augmented_data_without_edge_document_level_baseBART_run1/resolve/main/training_args.bin
- Command line
-
hf download hf://sheoran95/augmented_data_without_edge_document_level_baseBART_run1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sheoran95/augmented_data_without_edge_document_level_baseBART_run1/resolve/main/training_args.bin
3.84 kB
- Xet hash:
- 8d5b97ff0f61c529353938ebb1803f8439723b008a59116c30037478a2b927e3
- Size of remote file:
- 3.84 kB
- SHA256:
- 213708a2a5147c221c115cf45d0921c262d6fbdba7860816527555bac64e1eda
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.