Datasets:

Safak11
/

lilly-data

Tasks:

Translation

Image-to-Text

Modalities:

Formats:

Languages:

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lilly-data / README.md

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metadata

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. 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).