Instructions to use rooftopcoder/tst-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rooftopcoder/tst-summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rooftopcoder/tst-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("rooftopcoder/tst-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rooftopcoder/tst-summarization: direct link, hf CLI and curl.
- Browser
- Download file 3.76 kB
-
https://huggingface.co/rooftopcoder/tst-summarization/resolve/af88798922d9fbc76216dc80812a3a4db5e3c1c0/training_args.bin
- Command line
-
hf download hf://rooftopcoder/tst-summarization@af88798922d9fbc76216dc80812a3a4db5e3c1c0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rooftopcoder/tst-summarization/resolve/af88798922d9fbc76216dc80812a3a4db5e3c1c0/training_args.bin
3.76 kB
- Xet hash:
- c3746dd8a3e8324aae6d11d605086fe102434ac104f7eceaa2afd3b9c94b28cd
- Size of remote file:
- 3.76 kB
- SHA256:
- b7a1c45dff495df70a19b6a8f1da8e348c699ac0532911efe0429351936f44a6
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