Instructions to use dnrso/koBART_Sum_Review_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnrso/koBART_Sum_Review_finetuning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dnrso/koBART_Sum_Review_finetuning") model = AutoModelForSeq2SeqLM.from_pretrained("dnrso/koBART_Sum_Review_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dnrso/koBART_Sum_Review_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 309 Bytes
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https://huggingface.co/dnrso/koBART_Sum_Review_finetuning/resolve/main/README.md
- Command line
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hf download hf://dnrso/koBART_Sum_Review_finetuning/README.md
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curl -L -o README.md https://huggingface.co/dnrso/koBART_Sum_Review_finetuning/resolve/main/README.md
309 Bytes
metadata
language:
- ko
tags:
- bart
license: mit
koBART Review Summarization
finetuning BASE
https://huggingface.co/gogamza/kobart-summarization
dataset and code
https://github.com/dnrso/review_summary_using_KoBART
Demo Space
https://huggingface.co/spaces/dnrso/koBART_Sum_Review_finetuning