Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7efc6f8474d0>'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 967, in xopen
return open(main_hop, mode, *args, **kwargs)
FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7efc6f8474d0>'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
EHRI Polish OCR dataset + modele Kraken
Strony archiwalne EHRI (zydowskie instytuty historyczne, polskie kolekcje) z adnotacjami ALTO XML (baseline'y + transkrypcje). Modele wytrenowane Kraken 7.1.1 (ketos) na Kaggle T4.
Dane
- 15 stron .tif + ALTO XML: split 12 train / 3 validation (seed 42).
Modele (models/)
| plik | co to | trening | score walidacyjny |
|---|---|---|---|
| polish_nfd_9313.mlmodel | recognizer bazowy | - | - |
| polish_seg_best.safetensors | segmenter (fine-tune blla.mlmodel, --resize new) |
ketos segtrain, 50 epok, --augment |
val_metric 0.5277 |
| polish_nfd_finetuned.safetensors | recognizer (fine-tune polish_nfd_9313, --resize union) |
ketos train, 50 epok, --augment |
val score 0.9567 |
Wyniki e2e (3 strony walidacyjne, kraken.tasks API, CER/WER z jiwer)
| konfiguracja | CER | WER |
|---|---|---|
| default segmenter + polish_nfd_9313 | 13.45% | 47.40% |
| polish_seg_best + polish_nfd_9313 | 13.09% | 45.60% |
| polish_seg_best + polish_nfd_finetuned | 7.11% | 33.30% |
Fine-tuning rozpoznawania dal ~18x wiekszy przyrost CER niz tuning segmentacji (-5.98 pp vs -0.36 pp) - segmentacja nie jest watkim gardlem.
Uzycie (kraken 7.1.1)
from kraken.tasks import SegmentationTaskModel, RecognitionTaskModel
from kraken.configs import SegmentationInferenceConfig, RecognitionInferenceConfig
from PIL import Image
seg = SegmentationTaskModel.load_model('polish_seg_best.safetensors')
rec = RecognitionTaskModel.load_model('polish_nfd_finetuned.safetensors')
img = Image.open('strona.tif').convert('L')
segmentation = seg.predict(img, SegmentationInferenceConfig(accelerator='cuda', device=[0]))
pred = rec.predict(img, segmentation, RecognitionInferenceConfig(accelerator='cuda', device=[0]))
for record in pred:
print(record.prediction)
Trening / notebook
https://github.com/PiotrStyla/OCR_engine/blob/main/training/kaggle_kraken_segtrain.ipynb
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