| --- |
| license: other |
| task_categories: |
| - translation |
| - image-to-text |
| language: |
| - bs |
| - en |
| pretty_name: Lilly evaluation and training bytes |
| --- |
| |
| # Lilly data (the bytes git does not hold) |
|
|
| Code, answer keys, credits and results stay in |
| [ssaaffaakk/Lilly](https://github.com/ssaaffaakk/Lilly). This dataset is the |
| photographs and the cleaned parallel sentences those files name. |
|
|
| | File | What it is | |
| |---|---| |
| | `translation-clean.zip` | `train.tsv` (313,612 pairs), `valid.tsv`, `test.tsv`, `ntrex-holdout.tsv` — WikiMatrix / SETIMES / TED2020 / Tatoeba after the project's filters. The base model already saw this material (`docs/WHITE-PAPER.md` §3.1). | |
| | `ocr-the-40.zip` | The 40 Wikimedia Commons photographs the product OCR number (67.0%) was measured on, plus `commons40-sha1.json`. | |
| | `ocr-test-v2.zip` | test-v2: 280 Commons photographs (132 with text), credits, the two blind passes, the answer key. | |
| | `ocr-harvest.zip` | Harvested Commons sign photographs and `CREDITS.tsv`. | |
| | `ocr-crops.zip` | Crops cut from the harvest, with `labels-human.tsv`. | |
|
|
| Photograph licences are per-file in each archive's `CREDITS.tsv` (Wikimedia Commons). |
| The parallel sentences come from OPUS (SETIMES, WikiMatrix, TED2020, Tatoeba) and |
| NTREX-128; credit those sources, not this page. |
|
|
| Not here: FLORES-200 (fetch from Meta), FLEURS (fetch from Google), the 20,240 |
| Mapillary frames (`afaksrmeli/lilly-mapillary-photos` on Kaggle — not a train set). |
|
|
