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--- |
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license: cc-by-4.0 |
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language: |
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- la |
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- fr |
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- it |
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tags: |
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- medieval |
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pretty_name: CoMMA |
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size_categories: |
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- 1B<n<10B |
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--- |
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# Dataset Card for CoMMA JSON-L |
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CoMMA is a large-scale corpus of digitized medieval manuscripts |
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transcribed using Handwritten Text Recognition (HTR). It |
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contains over 2.5 billion tokens from more than 23,000 manuscripts in |
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Latin and Old French (801–1600 CE). Unlike most existing resources, the corpus |
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provides raw, non-normalized text. |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Curated by:** Thibault Clérice |
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- **Funded by:** Inria, [COLaF](https://colaf.huma-num.fr/), [ParamHTRs](https://www.bnf.fr/fr/les-projets-de-recherche-bnf-datalab) |
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- **Language(s) (NLP):** Latin, Old French, Italian |
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- **License:** CC-BY 4.0 |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** ARCA, Gallica, Biblissima + (Metadata) |
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- **Paper:** [More Information Needed] |
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- **Browser:** [More Information Needed] |
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## Uses |
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### Direct Use |
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- Training and evaluation of NLP models on medieval Latin and French. |
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- Historical linguistics and corpus linguistics research. |
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- Digital humanities applications (script analysis, layout studies, philology). |
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- Pretraining embeddings for downstream semantic tasks. |
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### Out-of-Scope Use |
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- Modern language processing tasks. |
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- Sensitive/identity analysis (texts are historical and not linked to personal data). |
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## Dataset Structure |
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The dataset is in JSON-L format, one line = one digitization of a manuscript |
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(manuscript can be represented by more than one digitization). Columns are: |
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- **biblissima_id**: Unique identifier of the manuscript, with metadata. .e.g https://data.biblissima.fr/entity/Q237292 |
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- **shelfmark**: Human readable identifier |
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- **iiif_manifest**: Source of our data |
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- **biblissima_language**: Biblissima provided language metadata |
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- **biblissima_simplified_language**: Denoising field for **biblissima_language** |
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- **language_fasttext**: Categorization in 5 languages (Latin, French, Bilingual, Other, Ambiguous), with two levels of details for Latin, French and Bilingual (e.g. Massively French, Truely French) |
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- **notBefore**: Minimal date of production. Some provider use 800 for stating 9th century instead of 801, be careful with the date. |
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- **notAfter**: When provided, maximum date of production. Mostly *null*. |
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- **lines**: Number of transcribed lines. |
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- **pages**: Number of treated pages. |
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- **tokens**: Number of whitespace delimited tokens. |
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- **scopecontent**: Free-text field description of the content of the manuscript, provided by Biblissima and the original curating institution. |
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- **text**: The main body of text, in its plain text representation. |
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## Dataset Creation |
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### Curation Rationale |
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To provide the first large-scale, open, raw-text corpus of medieval manuscripts enabling both computational linguistics and digital humanities research at scale. |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### Data Collection and Processing |
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- Harvested via IIIF from Gallica (BnF), ARCA, Bodleian, e-codices, etc. |
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- Downloaded in batch respecting institutional constraints. |
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- Segmentation: YOLOv11 + SegmOnto vocabulary. |
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- Recognition: Kraken with CATMuS models. |
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- Post-processed into ALTO and TEI. |
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#### Who are the source data producers? |
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Medieval scribes and copyists (8th–16th c. CE), preserved in institutional digitizations. |
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#### Annotation process |
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- Automated segmentation and transcription. |
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- Manual evaluation of CER on roughly 700 manuscripts single pages. |
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- TEI structuring for zones (marginalia, main text, etc.). |
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#### Personal and Sensitive Information |
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The dataset contains no personal or sensitive modern data. |
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## Bias, Risks, and Limitations |
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- Recognition quality varies by script type (Caroline/Textualis better, Cursiva and Beneventan worse). |
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- Language metadata may be noisy (e.g. mixed Latin/French glosses). |
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- Manuscripts are primarily from libraries, underrepresenting archives (e.g. charters). |
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- Errors in segmentation (skewed lines, faint text) persist. |
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### Recommendations |
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Users should evaluate CER for their subcorpus and be aware of biases in script and manuscript type coverage. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Dataset Card Contact |
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Thibault Clerice |
