Datasets:

clarin-pl
/

nkjp-pos

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- other
	language:
	- pl
	license:
	- gpl-3.0
	multilinguality:
	- monolingual
	size_categories:
	- unknown
	source_datasets:
	- original
	task_categories:
	- other
	task_ids:
	- part-of-speech
	pretty_name: nkjp-pos
	tags:
	- structure-prediction
	---

	# nkjp-pos

	## Description

	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.

	## Tasks (input, output and metrics)

	Part-of-speech tagging (POS tagging) - tagging words in text with their corresponding part of speech.

	Input ('tokens' column): sequence of tokens

	Output ('pos_tags' column): sequence of predicted tokens’ classes (35 possible classes, described in detail in the annotation guidelines)

	Measurements: F1-score (seqeval)

	Example:

	Input: `['Zarejestruj', 'się', 'jako', 'bezrobotny', '.']`

	Input (translated by DeepL): `Register as unemployed.`

	Output: `['impt', 'qub', 'conj', 'subst', 'interp']`

	## Data splits

	\| Subset \| Cardinality (sentences) \|
	\| ----------- \| ----------------------: \|
	\| train \| 78219 \|
	\| dev \| 0 \|
	\| test \| 7444 \|


	## Class distribution


	\| Class \| train \| dev \| test \|
	\|:--------\|--------:\|------:\|--------:\|
	\| subst \| 0.27345 \| - \| 0.27656 \|
	\| interp \| 0.18101 \| - \| 0.17944 \|
	\| adj \| 0.10611 \| - \| 0.10919 \|
	\| prep \| 0.09567 \| - \| 0.09547 \|
	\| qub \| 0.05670 \| - \| 0.05491 \|
	\| fin \| 0.04939 \| - \| 0.04648 \|
	\| praet \| 0.04409 \| - \| 0.04348 \|
	\| conj \| 0.03711 \| - \| 0.03724 \|
	\| adv \| 0.03512 \| - \| 0.03333 \|
	\| inf \| 0.01591 \| - \| 0.01547 \|
	\| comp \| 0.01476 \| - \| 0.01439 \|
	\| num \| 0.01322 \| - \| 0.01436 \|
	\| ppron3 \| 0.01111 \| - \| 0.01018 \|
	\| ppas \| 0.01086 \| - \| 0.01085 \|
	\| ger \| 0.00961 \| - \| 0.01050 \|
	\| brev \| 0.00856 \| - \| 0.01181 \|
	\| ppron12 \| 0.00670 \| - \| 0.00665 \|
	\| aglt \| 0.00629 \| - \| 0.00602 \|
	\| pred \| 0.00539 \| - \| 0.00540 \|
	\| pact \| 0.00454 \| - \| 0.00452 \|
	\| bedzie \| 0.00229 \| - \| 0.00243 \|
	\| pcon \| 0.00218 \| - \| 0.00189 \|
	\| impt \| 0.00203 \| - \| 0.00226 \|
	\| siebie \| 0.00177 \| - \| 0.00158 \|
	\| imps \| 0.00174 \| - \| 0.00177 \|
	\| interj \| 0.00131 \| - \| 0.00102 \|
	\| xxx \| 0.00070 \| - \| 0.00048 \|
	\| adjp \| 0.00069 \| - \| 0.00065 \|
	\| winien \| 0.00068 \| - \| 0.00057 \|
	\| adja \| 0.00048 \| - \| 0.00058 \|
	\| pant \| 0.00012 \| - \| 0.00018 \|
	\| burk \| 0.00011 \| - \| 0.00006 \|
	\| numcol \| 0.00011 \| - \| 0.00013 \|
	\| depr \| 0.00010 \| - \| 0.00004 \|
	\| adjc \| 0.00007 \| - \| 0.00008 \|


	## Citation

	```
	@book{przepiorkowski_narodowy_2012,
	title = {Narodowy korpus języka polskiego},
	isbn = {978-83-01-16700-4},
	language = {pl},
	publisher = {Wydawnictwo Naukowe PWN},
	editor = {Przepiórkowski, Adam and Bańko, Mirosław and Górski, Rafał L. and Lewandowska-Tomaszczyk, Barbara},
	year = {2012}
	}
	```

	## License

	```
	GNU GPL v.3
	```

	## Links

	[HuggingFace](https://huggingface.co/datasets/clarin-pl/nkjp-pos)

	[Source](http://clip.ipipan.waw.pl/NationalCorpusOfPolish)

	[Paper](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf)

	## Examples

	### Loading

	```python
	from pprint import pprint

	from datasets import load_dataset

	dataset = load_dataset("clarin-pl/nkjp-pos")
	pprint(dataset['train'][5000])

	# {'id': '130-2-900005_morph_49.49-s',
	# 'pos_tags': [16, 4, 3, 30, 12, 18, 3, 16, 14, 6, 14, 26, 1, 30, 12],
	# 'tokens': ['Najwyraźniej',
	# 'źle',
	# 'ocenił',
	# 'odległość',
	# ',',
	# 'bo',
	# 'zderzył',
	# 'się',
	# 'z',
	# 'jadącą',
	# 'z',
	# 'naprzeciwka',
	# 'ciężarową',
	# 'scanią',
	# '.']}
	```

	### Evaluation

	```python
	import random
	from pprint import pprint

	from datasets import load_dataset, load_metric

	dataset = load_dataset("clarin-pl/nkjp-pos")
	references = dataset["test"]["pos_tags"]

	# generate random predictions
	predictions = [
	[
	random.randrange(dataset["train"].features["pos_tags"].feature.num_classes)
	for _ in range(len(labels))
	]
	for labels in references
	]

	# transform to original names of labels
	references_named = [
	[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
	for labels in references
	]
	predictions_named = [
	[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
	for labels in predictions
	]

	# transform to BILOU scheme
	references_named = [
	[f"U-{label}" if label != "O" else label for label in labels]
	for labels in references_named
	]
	predictions_named = [
	[f"U-{label}" if label != "O" else label for label in labels]
	for labels in predictions_named
	]

	# utilise seqeval to evaluate
	seqeval = load_metric("seqeval")
	seqeval_score = seqeval.compute(
	predictions=predictions_named,
	references=references_named,
	scheme="BILOU",
	mode="strict",
	)

	pprint(seqeval_score, depth=1)

	# {'adj': {...},
	# 'adja': {...},
	# 'adjc': {...},
	# 'adjp': {...},
	# 'adv': {...},
	# 'aglt': {...},
	# 'bedzie': {...},
	# 'brev': {...},
	# 'burk': {...},
	# 'comp': {...},
	# 'conj': {...},
	# 'depr': {...},
	# 'fin': {...},
	# 'ger': {...},
	# 'imps': {...},
	# 'impt': {...},
	# 'inf': {...},
	# 'interj': {...},
	# 'interp': {...},
	# 'num': {...},
	# 'numcol': {...},
	# 'overall_accuracy': 0.027855061488566583,
	# 'overall_f1': 0.027855061488566583,
	# 'overall_precision': 0.027855061488566583,
	# 'overall_recall': 0.027855061488566583,
	# 'pact': {...},
	# 'pant': {...},
	# 'pcon': {...},
	# 'ppas': {...},
	# 'ppron12': {...},
	# 'ppron3': {...},
	# 'praet': {...},
	# 'pred': {...},
	# 'prep': {...},
	# 'qub': {...},
	# 'siebie': {...},
	# 'subst': {...},
	# 'winien': {...},
	# 'xxx': {...}}
	```