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Parent(s):
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Update README.md
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README.md
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@@ -104,23 +104,334 @@ Each example represents a single page of OCR'd text.
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A single example of the dataset is as follows:
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```python
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'language': 'fr',
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'language_confidence': 0
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'ppn': '646426230',
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```
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@@ -132,6 +443,11 @@ A single example of the dataset is as follows:
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- 'ppn': 'Pica production numbers' an internal ID used by the library. See [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2702544.svg)](https://doi.org/10.5281/zenodo.2702544) for more details.
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'language': language predicted by `langid.py` (see above for more details)
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-'language_confidence': confidence score given by `langid.py`
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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#### Initial Data Collection and Normalization
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This dataset includes text content produced through running Optical Character Recognition across 153,942 digitized works held by the Berlin State Library.
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[More Information Needed]
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#### Who are the source language producers?
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- the confidence scores the OCR engines used to produce the full-text materials.
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- the predicted languages and associated confidence scores produced by `langid.py`
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#### Who are the annotators?
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### Personal and Sensitive Information
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This dataset contains historical material,
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[More Information Needed]
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### Dataset Curators
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Labusch, Kai; Zellhöfer, David
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### Licensing Information
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A single example of the dataset is as follows:
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```python
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{'aut': 'Doré, Henri',
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'date': '1912',
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'file name': '00000218.xml',
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'language': 'fr',
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'language_confidence': 1.0,
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'place': 'Chang-hai',
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'ppn': '646426230',
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'publisher': 'Imprimerie de la Mission Catholique',
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'text': "— 338 — Cela fait, on enterre la statuette qu’on vient d’outrager, atten dant la réalisation sur la personne elle-même. C’est l’outrage en effigie. Un deuxième moyen, c’est de représenter l’Esprit Vengeur sous la figure d’un fier-à-bras, armé d’un sabre, ou d’une pique, et de lui confier tout le soin de sa vengeance. On multiplie les incantations et les offrandes en son honneur, pour le porter au paroxysme de la fureur, et inspirer à l’Esprit malin l’idée de l’exécution de ses désirs : en un mot, on fait tout pour faire passer en son cœur la rage de vengeance qui consume le sien propre. C’est une invention diabolique imaginée pour assouvir sa haine sur l’ennemi qu’on a en horreur. Ailleurs, ce n’est qu’une figurine en bois ou en papier, qui est lancée contre l’ennemi; elle se dissimule, ou prend des formes fantastiques pour acomplir son œuvre de vengeance. Qu’on se rappelle la panique qui régna dans la ville de Nan- king ifâ ffl, et ailleurs, l’année où de méchantes gens répandirent le bruit que des hommes de papier volaient en l’air et coupaient les tresses de cheveux des Chinois. Ce fut une véritable terreur, tous étaient affolés, et il y eut à cette occasion de vrais actes de sauvagerie. Voir historiettes sur les envoûtements : Wieger Folk-Lore, N os 50, 128, 157, 158, 159. Corollaire. Les Tao-niu jift fx ou femmes “ Tao-clie'’. A cette super stition peut se rapporter la pratique des magiciennes du Kiang- sou ■n: m, dans les environs de Chang-hai ± m, par exemple. Ces femmes portent constamment avec- elles une statue réputée merveilleuse : elle n’a que quatre ou cinq pouces de hauteur ordinairement. A force de prières, d’incantations, elles finissent par la rendre illuminée, vivante et parlante, ou plutôt piaillarde, car elle ne répond que par des petits cris aigus et répétés aux demandes qu’on lui adressé; elle paraît comme animée, sautille,",
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'title': 'Les pratiques superstitieuses',
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'wc': [1.0,
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0.4199999869,
|
347 |
+
0.8216666579,
|
348 |
+
1.0,
|
349 |
+
0.7563636303,
|
350 |
+
0.3000000119,
|
351 |
+
0.8579999804,
|
352 |
+
0.6679999828,
|
353 |
+
0.7099999785,
|
354 |
+
0.7875000238,
|
355 |
+
0.9499999881,
|
356 |
+
0.5799999833,
|
357 |
+
0.9150000215,
|
358 |
+
0.6600000262,
|
359 |
+
0.8066666722,
|
360 |
+
0.729090929,
|
361 |
+
0.6999999881,
|
362 |
+
0.7400000095,
|
363 |
+
0.8066666722,
|
364 |
+
0.2866666615,
|
365 |
+
0.6700000167,
|
366 |
+
0.9225000143,
|
367 |
+
1.0,
|
368 |
+
0.7599999905,
|
369 |
+
0.75,
|
370 |
+
0.6899999976,
|
371 |
+
0.3600000143,
|
372 |
+
0.224999994,
|
373 |
+
0.5799999833,
|
374 |
+
0.8874999881,
|
375 |
+
1.0,
|
376 |
+
0.8066666722,
|
377 |
+
0.8985714316,
|
378 |
+
0.8827272654,
|
379 |
+
0.8460000157,
|
380 |
+
0.8880000114,
|
381 |
+
0.9533333182,
|
382 |
+
0.7966666818,
|
383 |
+
0.75,
|
384 |
+
0.8941666484,
|
385 |
+
1.0,
|
386 |
+
0.8450000286,
|
387 |
+
0.8666666746,
|
388 |
+
0.9533333182,
|
389 |
+
0.5883333087,
|
390 |
+
0.5799999833,
|
391 |
0.6549999714,
|
392 |
+
0.8600000143,
|
393 |
+
1.0,
|
394 |
+
0.7585714459,
|
395 |
+
0.7114285827,
|
396 |
+
1.0,
|
397 |
+
0.8519999981,
|
398 |
+
0.7250000238,
|
399 |
+
0.7437499762,
|
400 |
+
0.6639999747,
|
401 |
+
0.8939999938,
|
402 |
+
0.8877778053,
|
403 |
+
0.7300000191,
|
404 |
+
1.0,
|
405 |
+
0.8766666651,
|
406 |
+
0.8019999862,
|
407 |
+
0.8928571343,
|
408 |
+
1.0,
|
409 |
+
0.853333354,
|
410 |
+
0.5049999952,
|
411 |
+
0.5416666865,
|
412 |
+
0.7963636518,
|
413 |
+
0.5600000024,
|
414 |
+
0.8774999976,
|
415 |
+
0.6299999952,
|
416 |
+
0.5749999881,
|
417 |
+
0.8199999928,
|
418 |
+
0.7766666412,
|
419 |
+
1.0,
|
420 |
+
0.9850000143,
|
421 |
+
0.5674999952,
|
422 |
+
0.6240000129,
|
423 |
+
1.0,
|
424 |
+
0.9485714436,
|
425 |
+
1.0,
|
426 |
+
0.8174999952,
|
427 |
+
0.7919999957,
|
428 |
+
0.6266666651,
|
429 |
+
0.7887499928,
|
430 |
+
0.7825000286,
|
431 |
+
0.5366666913,
|
432 |
+
0.65200001,
|
433 |
+
0.832857132,
|
434 |
+
0.7488889098]}
|
435 |
```
|
436 |
|
437 |
|
|
|
443 |
- 'ppn': 'Pica production numbers' an internal ID used by the library. See [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2702544.svg)](https://doi.org/10.5281/zenodo.2702544) for more details.
|
444 |
'language': language predicted by `langid.py` (see above for more details)
|
445 |
-'language_confidence': confidence score given by `langid.py`
|
446 |
+
- publisher: publisher of the item in which the text appears
|
447 |
+
- place: place of publication of the item in which the text appears
|
448 |
+
- date: date of the item in which the text appears
|
449 |
+
- title: title of the item in which the text appears
|
450 |
+
- aut: author of the item in which the text appears
|
451 |
|
452 |
[More Information Needed]
|
453 |
|
|
|
457 |
|
458 |
## Dataset Creation
|
459 |
|
460 |
+
The dataset is created from [OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB)](https://doi.org/10.5281/zenodo.3257041) hosted on Zenodo.
|
461 |
+
|
462 |
### Curation Rationale
|
463 |
|
464 |
[More Information Needed]
|
|
|
467 |
|
468 |
#### Initial Data Collection and Normalization
|
469 |
|
470 |
+
The dataset is created from [OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB)](https://doi.org/10.5281/zenodo.3257041) hosted on Zenodo. This dataset includes text content produced through running Optical Character Recognition across 153,942 digitized works held by the Berlin State Library.
|
471 |
+
|
472 |
+
The [dataprep.ipynb](https://huggingface.co/datasets/biglam/berlin_state_library_ocr/blob/main/dataprep.ipynb) was used to create this dataset.
|
473 |
+
|
474 |
+
To make the dataset more useful for training language models, the following steps were carried out:
|
475 |
+
- the CSV `xml2csv_alto.csv`, which contains the full text corpus per document page (incl.OCR word confidences) was loaded using the `datasets` library
|
476 |
+
- this CSV was augmented with language information from `corpus-language.pkl` **note** some examples don't find a match for this. Sometimes this is because a text is blank, but some actual text may be missing predicted language information
|
477 |
+
- the CSV was further augmented by trying to map the PPN to fields in a metadata download created using [https://github.com/elektrobohemian/StabiHacks/blob/master/oai-analyzer/oai-analyzer.py](https://github.com/elektrobohemian/StabiHacks/blob/master/oai-analyzer/oai-analyzer.py). **note** not all examples are successfully matched to this metadata download.
|
478 |
|
|
|
479 |
|
480 |
#### Who are the source language producers?
|
481 |
|
|
|
490 |
- the confidence scores the OCR engines used to produce the full-text materials.
|
491 |
- the predicted languages and associated confidence scores produced by `langid.py`
|
492 |
|
493 |
+
The dataset also contains metadata for the following fields:
|
494 |
+
|
495 |
+
- author
|
496 |
+
- publisher
|
497 |
+
- the place of publication
|
498 |
+
- title
|
499 |
+
|
500 |
|
501 |
#### Who are the annotators?
|
502 |
|
|
|
504 |
|
505 |
### Personal and Sensitive Information
|
506 |
|
507 |
+
This dataset contains historical material, potentially including names, addresses etc., but these are not likely to refer to living individuals.
|
508 |
|
509 |
[More Information Needed]
|
510 |
|
|
|
528 |
|
529 |
### Dataset Curators
|
530 |
|
531 |
+
Initial data created by: Labusch, Kai; Zellhöfer, David
|
532 |
|
533 |
### Licensing Information
|
534 |
|