| --- |
| license: apache-2.0 |
| base_model: lightonai/LightOnOCR-3-4B |
| library_name: transformers |
| pipeline_tag: image-to-text |
| tags: |
| - ocr |
| - table-recognition |
| - document-understanding |
| datasets: |
| - lschiemanowski/scannedtables |
| --- |
| |
| # tablereader-lightonocr-v0 |
|
|
| [LightOnOCR-3-4B](https://huggingface.co/lightonai/LightOnOCR-3-4B) fine-tuned to transcribe a crop of a printed |
| table into a fixed HTML format: a `# Title` line when a title is printed, a table with `thead` for printed |
| column headings, `data-level` for the printed indentation of row labels, `<sup>` for footnote markers and |
| `tfoot` for footnote lines ([transcription format](https://github.com/lschiemanowski/tablereader/blob/main/docs/transcription-format.md), |
| contract revision 1). |
|
|
| It is the model behind [tablereader](https://github.com/lschiemanowski/tablereader), which detects the tables |
| in a PDF, crops them over their titles and footnotes, runs this model, and converts the transcription into |
| table JSON. `release.json` records the settings the model was trained with: crop preparation, renderer, |
| detector, image size and generation settings. Use it through tablereader so crops are prepared the same way: |
|
|
| ```bash |
| uv run tablereader read report.pdf --pages 13-20 |
| ``` |
|
|
| ## Prompt |
|
|
| The image alone, with no system prompt or text, scaled so its longest edge is at most 2048 pixels; thinking |
| disabled, temperature 0, up to 16,000 new tokens. |
|
|
| ## Training |
|
|
| LoRA (rank 16, alpha 32) on every language-model projection, the vision encoder frozen; 3 epochs at learning |
| rate 1e-4 on 995 table crops from 107 publication series (UK public-sector reports, US federal and historical |
| statistics, German budget annexes and others), digital and scanned. The targets are GPT-6.1 Terra |
| transcriptions that parse into tables without blocking issues; they were not reviewed by a person. The training |
| crops include some that the [scannedtables](https://huggingface.co/datasets/lschiemanowski/scannedtables) |
| dataset holds back for rights or privacy reasons. The adapter is merged into the bf16 weights. |
|
|
| ## Evaluation |
|
|
| 1,006 validation crops from series not seen in training, scored as the share of printed values whose row path, |
| column path and text match the reference transcription exactly (metric `records-1`): |
|
|
| | All | Digital PDFs | Scans | |
| | ---: | ---: | ---: | |
| | 0.797 | 0.835 | 0.511 | |
|
|
| 72% of crops are read without a difference. The model does not yet answer `NOT_A_TABLE` for crops without a |
| table, and scans read much worse than digital PDFs. A self-training round on 7,698 of its own |
| transcriptions did not improve these scores. |
|
|
| ## License |
|
|
| Apache 2.0, as the base model. |
|
|
