Instructions to use Nara-Lab/History_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use Nara-Lab/History_LM with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("Nara-Lab/History_LM") - Notebooks
- Google Colab
- Kaggle
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license: cc-by-nc-4.0
library_name: flair
pipeline_tag: text-generation
language:
- ko
- zh
tags:
- history
widget:
- text "春坊日記"
---
Citation
If you apply this library or model to any project and research, please cite our code:
```python
@misc{Nara-Lab: History_LM_2023,
title = {Nara-Lab : History LM (Classical Chinese) Text Generation},
author = {Sojung Lucia Kim (Lucia), Tea Hong Jang (Ted), Joon Mo Ahn (Joon), Jae Hyuk Lee (Jake)},
year = {2023},
howpublished = {\url{https://huggingface.co/Nara-Lab/History_LM}},
}
```
## Contact
This is released as pre-trained NLP model in the hope that it will be helpful to many history research institutes and other entities. We look forward to contacting us from various places who wish to cooperate with us.
sojung.kim@gmail.com
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