readme: ## 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:pstroebel@cl.uzh.ch) 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}
      }