LaTa

The paper Exploring Language Models for Classical Philology 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.

Further information can be found in our paper or in our GitHub repository.

Usage

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, 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.

Citation

@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"
}