| ## RoBERTa Latin model, version 2 --> model card not finished yet | |
| This is a Latin RoBERTa-based LM model, version 2. | |
| The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results; on the other, it should be used as a decoder for the TrOCR architecture. | |
| The training data is more or less the same data as has been used by [Bamman and Burns (2020)](https://arxiv.org/pdf/2009.10053.pdf), although more heavily filtered (see below). There are several digital-born texts from online Latin archives. Other Latin texts have been crawled by [Bamman and Smith](https://www.cs.cmu.edu/~dbamman/latin.html) and thus contain many OCR errors. | |
| The overall downsampled corpus contains 577M of text data. | |
| ### Preprocessing | |
| I undertook the following preprocessing steps: | |
| - Normalisation of all lines with [CLTK](http://www.cltk.org) incl. sentence splitting. | |
| - Language identification with [langid](https://github.com/saffsd/langid.py) | |
| - Compute the ratio of Latin vocabulary in each sentence (against the digital-born vocab of the corpus) | |
| - Retain only sentences with a Latin vocabulary ratio of > 85%. | |
| - Exclude all lines containing '^' --> hints at the presence of OCR errors. | |
| The result is a corpus of ~100 million tokens. | |
| The dataset used to train this will be available on Hugging Face later [HERE (does not work yet)](). | |
| ### Contact | |
| For contact, reach out to Phillip Ströbel [via mail](mailto:[email protected]) or [via Twitter](https://twitter.com/CLingophil). | |
| ### How to cite | |
| If you use this model, pleas cite it as: | |
| @online{stroebel-roberta-base-latin-cased2, | |
| author = {Ströbel, Phillip Benjamin}, | |
| title = {RoBERTa Base Latin Cased V2}, | |
| year = 2022, | |
| url = {https://huggingface.co/pstroe/roberta-base-latin-cased2}, | |
| urldate = {YYYY-MM-DD} | |
| } |