McCATMuS - Transcription model for handwritten, printed and typewritten documents from the 16th century to the 21st century
Bibliographic record
Abstract
Built upon datasets from institutions and projects committed to Open Science, McCATMuS provides an interoperable dataset encompassing over 180 manuscripts in 7 different languages (French, Latin, Spanish, English, German, Italian and Occitan). It includes more than 118,000 lines of text and nearly 4 million characters, covering a period from the early 16th century to the present day. All the datasets were automatically or, when precised, manually corrected to correspond to the CATMuS transcription guidelines, available here: https://catmus-guidelines.github.io/ The annotations in the dataset result for layout extraction, line extraction, typing and transcription, from the original creators of the dataset in most cases, or from automatic or manual corrections by the curator of the CATMuS modern dataset. The alignment of the dataset with CATMuS' guidelines was performed by the curator of the dataset. The curated dataset can be accessed via HuggingFace: https://huggingface.co/datasets/CATMuS/modern This model was trained on the McCATMuS Dataset, with Kraken v.4.3.13, with NFD Unicode normalization and a batch size of 32 over 157 epochs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".