Instructions to use jx7789/kobart_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jx7789/kobart_summary with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jx7789/kobart_summary") model = AutoModelForSeq2SeqLM.from_pretrained("jx7789/kobart_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jx7789/kobart_summary: direct link, hf CLI and curl.
- Browser
- Download file 469 Bytes
-
https://huggingface.co/jx7789/kobart_summary/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jx7789/kobart_summary/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jx7789/kobart_summary/resolve/main/tokenizer_config.json
469 Bytes
| { | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "/content/drive/MyDrive/인공지능/생성요약프로젝트/Model/KoBART/checkpoint/kobart_summary/checkpoint-61252", | |
| "special_tokens_map_file": "/root/.cache/huggingface/hub/models--gogamza--kobart-base-v2/snapshots/f9f2ec35d3c32a1ecc7a3281f9626b7ec1913fed/special_tokens_map.json", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |