README.md

---
language: la
license: apache-2.0
inference: false
---
# LaTa

The paper [Exploring Language Models for Classical Philology](https://todo.com) is the first effort to systematically provide state-of-the-art language models for Classical Philology. LaTa is a T5-base sized, monolingual, encoder-decoder variant.  

This model was trained on the [Corpus Corporum](https://mlat.uzh.ch/).

Further information can be found in our paper or in our [GitHub repository](https://github.com/Heidelberg-NLP/ancient-language-models).

## Usage
```python
from transformers import AutoTokenizer, AutoModelForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained('bowphs/LaTa')
model = AutoModelForConditionalGeneration.from_pretrained('bowphs/LaTa')
```
Please check out the awesome Hugging Face tutorials on how to fine-tune our models.

## Evaluation Results
When fine-tuned on lemmatization data from [EvaLatin 2022](https://universaldependencies.org/), LaTa achieves the following results:

| Task | Classical | Cross-genre  | Cross-time | 
|:--:|:--:|:--:|:--:|
|      |97.30|93.95|92.26|

## Contact
If you have any questions or problems, feel free to [reach out](mailto:riemenschneider@cl.uni-heidelberg.de).

## Citation
```bibtex
@incollection{riemenschneiderfrank:2023,
    address = "Toronto, Canada",
    author = "Riemenschneider, Frederick and Frank, Anette",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL’23)",
    note = "to appear",
    pubType = "incollection",
    publisher = "Association for Computational Linguistics",
    title = "Exploring Large Language Models for Classical Philology",
    url = "https://arxiv.org/abs/2305.13698",
    year = "2023",
    key = "riemenschneiderfrank:2023"
}
```