Datasets:
Tasks:
Other
Modalities:
Text
Sub-tasks:
part-of-speech
Languages:
Polish
Size:
10K - 100K
Tags:
structure-prediction
License:
Albert Sawczyn
commited on
Commit
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Parent(s):
b42580d
add README.md
Browse files
README.md
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1 |
+
---
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annotations_creators:
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- expert-generated
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language_creators:
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- other
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languages:
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- pl
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licenses:
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- gpl-3.0
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multilinguality:
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- monolingual
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pretty_name: 'nkjp-pos'
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size_categories:
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- unknown
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+
source_datasets:
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- original
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task_categories:
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- structure-prediction
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task_ids:
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- part-of-speech-tagging
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---
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# nkjp-pos
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## Description
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+
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+
NKJP-POS is a part the National Corpus of Polish (*Narodowy Korpus Języka Polskiego*). Its objective is part-of-speech tagging, e.g. nouns, verbs, adjectives, adverbs, etc. During the creation of corpus, texts of were annotated by humans from various sources, covering many domains and genres.
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## Tasks (input, output and metrics)
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Part-of-speech tagging (POS tagging) - tagging words in text with their corresponding part of speech.
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+
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**Input** ('*tokens'* column): sequence of tokens
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**Output** ('*pos_tags'* column): sequence of predicted tokens’ classes (35 possible classes, described in detail in the annotation guidelines)
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***example**:*
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[*'Zarejestruj', 'się', 'jako', 'bezrobotny', '.'*] → [*'impt', 'qub', 'conj', 'subst', 'interp'*]
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Measurements:
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## Data splits
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| Subset | Cardinality (sentences) |
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| ----------- | ----------------------: |
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| train | 68528 |
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| val | 8566 |
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| test | 8566 |
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## Class distribution in train
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| Class | Fraction of tokens |
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|:--------|---------------------:|
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+
| subst | 0.27295 |
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| interp | 0.18381 |
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| adj | 0.10607 |
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| prep | 0.09533 |
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| qub | 0.05633 |
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| fin | 0.04895 |
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| praet | 0.04385 |
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| conj | 0.03685 |
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| adv | 0.03498 |
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| inf | 0.01586 |
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| comp | 0.01465 |
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| num | 0.01319 |
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| ppron3 | 0.01090 |
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| ppas | 0.01080 |
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| ger | 0.00967 |
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| brev | 0.00880 |
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| ppron12 | 0.00668 |
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| aglt | 0.00620 |
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| pred | 0.00536 |
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| pact | 0.00452 |
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| bedzie | 0.00232 |
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| pcon | 0.00216 |
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| impt | 0.00201 |
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| siebie | 0.00175 |
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| imps | 0.00172 |
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| interj | 0.00128 |
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| xxx | 0.00067 |
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| winien | 0.00066 |
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| adjp | 0.00066 |
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| adja | 0.00048 |
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| pant | 0.00013 |
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| depr | 0.00010 |
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| burk | 0.00010 |
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| numcol | 0.00010 |
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| adjc | 0.00007 |
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## Citation
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```
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@book{przepiorkowski_narodowy_2012,
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title = {Narodowy korpus języka polskiego},
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isbn = {978-83-01-16700-4},
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language = {pl},
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publisher = {Wydawnictwo Naukowe PWN},
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editor = {Przepiórkowski, Adam and Bańko, Mirosław and Górski, Rafał L. and Lewandowska-Tomaszczyk, Barbara},
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year = {2012}
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}
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```
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## License
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```
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GNU GPL v.3
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```
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## Links
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[HuggingFace](https://huggingface.co/datasets/clarin-pl/nkjp-pos)
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[Source](http://clip.ipipan.waw.pl/NationalCorpusOfPolish)
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[Paper](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf)
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## Examples
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### Loading
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```python
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from pprint import pprint
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from datasets import load_dataset
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dataset = load_dataset("clarin-pl/nkjp-pos")
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pprint(dataset['train'][5000])
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# {'full_pos_tags': ['fin:sg:ter:imperf',
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# 'subst:sg:nom:f',
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# 'adj:sg:nom:f:pos',
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# 'interp'],
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# 'lemmas': ['trwać', 'akcja', 'poszukiwawczy', '.'],
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# 'morph': ['trwać|fin:sg:ter:imperf',
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# 'akcja|subst:sg:nom:f',
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# 'poszukiwawczy|adj:sg:nom:f:pos poszukiwawczy|adj:sg:voc:f:pos',
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# '.|interp'],
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# 'nps': ['', '', 'nps', ''],
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# 'pos_tags': [12, 32, 0, 18],
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# 'tokens': ['Trwa', 'akcja', 'poszukiwawcza', '.']}
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```
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### Evaluation
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```python
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import random
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from pprint import pprint
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from datasets import load_dataset, load_metric
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dataset = load_dataset("clarin-pl/nkjp-pos")
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references = dataset["test"]["pos_tags"]
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# generate random predictions
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predictions = [
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[
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random.randrange(dataset["train"].features["pos_tags"].feature.num_classes)
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for _ in range(len(labels))
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]
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for labels in references
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]
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# transform to original names of labels
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references_named = [
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[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
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for labels in references
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]
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predictions_named = [
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[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
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for labels in predictions
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]
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# transform to BILOU scheme
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references_named = [
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[f"U-{label}" if label != "O" else label for label in labels]
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for labels in references_named
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]
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predictions_named = [
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[f"U-{label}" if label != "O" else label for label in labels]
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for labels in predictions_named
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]
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# utilise seqeval to evaluate
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seqeval = load_metric("seqeval")
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seqeval_score = seqeval.compute(
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predictions=predictions_named,
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references=references_named,
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scheme="BILOU",
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mode="strict",
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)
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pprint(seqeval_score, depth=1)
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# {'adj': {...},
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# 'adja': {...},
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# 'adjc': {...},
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# 'adjp': {...},
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# 'adv': {...},
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# 'aglt': {...},
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# 'bedzie': {...},
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# 'brev': {...},
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# 'burk': {...},
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# 'comp': {...},
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# 'conj': {...},
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# 'depr': {...},
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# 'fin': {...},
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# 'ger': {...},
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# 'imps': {...},
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# 'impt': {...},
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# 'inf': {...},
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# 'interj': {...},
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# 'interp': {...},
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# 'num': {...},
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# 'numcol': {...},
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# 'overall_accuracy': 0.027855061488566583,
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# 'overall_f1': 0.027855061488566583,
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# 'overall_precision': 0.027855061488566583,
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# 'overall_recall': 0.027855061488566583,
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# 'pact': {...},
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# 'pant': {...},
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# 'pcon': {...},
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# 'ppas': {...},
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# 'ppron12': {...},
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# 'ppron3': {...},
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# 'praet': {...},
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# 'pred': {...},
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# 'prep': {...},
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# 'qub': {...},
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# 'siebie': {...},
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# 'subst': {...},
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# 'winien': {...},
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# 'xxx': {...}}
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```
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