Instructions to use Ayham/bert_gpt2_summarization_cnndm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ayham/bert_gpt2_summarization_cnndm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ayham/bert_gpt2_summarization_cnndm") model = AutoModelForSeq2SeqLM.from_pretrained("Ayham/bert_gpt2_summarization_cnndm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ayham/bert_gpt2_summarization_cnndm: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/Ayham/bert_gpt2_summarization_cnndm/resolve/main/training_args.bin
- Command line
-
hf download hf://Ayham/bert_gpt2_summarization_cnndm/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ayham/bert_gpt2_summarization_cnndm/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 57b3b5aeae666a5861a71d71d0274abfd759a7fe2497d28ce6e7ac4a15af1f6b
- Size of remote file:
- 2.99 kB
- SHA256:
- 341cc372c0ed93413a69f69575ccb22318fcf315a8c1f1d471b9d60d23a17e9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.