Instructions to use Ayham/xlmroberta_gpt2_summarization_xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ayham/xlmroberta_gpt2_summarization_xsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ayham/xlmroberta_gpt2_summarization_xsum") model = AutoModelForSeq2SeqLM.from_pretrained("Ayham/xlmroberta_gpt2_summarization_xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ayham/xlmroberta_gpt2_summarization_xsum: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/Ayham/xlmroberta_gpt2_summarization_xsum/resolve/main/training_args.bin
- Command line
-
hf download hf://Ayham/xlmroberta_gpt2_summarization_xsum/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ayham/xlmroberta_gpt2_summarization_xsum/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- f169988637bf273220e80dfbff7a263302a59c01da99a3f0e2d6cee1110ed3e7
- Size of remote file:
- 2.99 kB
- SHA256:
- 106db9b02896e1c2c26e1ba04277d06eb5ef25176d02ef50b5d6a69b16683c8a
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