Instructions to use RDCPP/bart_en2de_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RDCPP/bart_en2de_training with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RDCPP/bart_en2de_training") model = AutoModelForSeq2SeqLM.from_pretrained("RDCPP/bart_en2de_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RDCPP/bart_en2de_training: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/RDCPP/bart_en2de_training/resolve/main/training_args.bin
- Command line
-
hf download hf://RDCPP/bart_en2de_training/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RDCPP/bart_en2de_training/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- 9ad6ddc0b9589ae2060ae472261abc6741716d17b2dde02fb5a316aa31b03b25
- Size of remote file:
- 3.77 kB
- SHA256:
- 0dcfb40844cff69c21d789bb7adb263168d22215f3e8ae145e4316f7bcb6f1f1
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