README.md

---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: NLPre-PL_dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- National Corpus of Polish
- Narodowy Korpus Języka Polskiego
task_categories:
- token-classification
task_ids:
- part-of-speech
- lemmatization
- parsing
dataset_info:
- config_name: nlprepl_by_name
  features:
  - name: idx
    dtype: string
  - name: text
    dtype: string
  - name: tokens
    sequence: string
  - name: lemmas
    sequence: string
  - name: upos
    sequence:
      class_label:
        names:
          '0': NOUN
          '1': PUNCT
          '2': ADP
          '3': NUM
          '4': SYM
          '5': SCONJ
          '6': ADJ
          '7': PART
          '8': DET
          '9': CCONJ
          '10': PROPN
          '11': PRON
          '12': X
          '13': _
          '14': ADV
          '15': INTJ
          '16': VERB
          '17': AUX
  - name: xpos
    sequence: string
  - name: feats
    sequence: string
  - name: head
    sequence: string
  - name: deprel
    sequence: string
  - name: deps
    sequence: string
  - name: misc
    sequence: string
  splits:
  - name: train
    num_bytes: 0
    num_examples: 69360
  - name: dev
    num_bytes: 0
    num_examples: 7669
  - name: test
    num_bytes: 0
    num_examples: 8633
  download_size: 3088237
  dataset_size: 5120697
- config_name: nlprepl_by_type
  features:
  - name: idx
    dtype: string
  - name: text
    dtype: string
  - name: tokens
    sequence: string
  - name: lemmas
    sequence: string
  - name: upos
    sequence:
      class_label:
        names:
          '0': NOUN
          '1': PUNCT
          '2': ADP
          '3': NUM
          '4': SYM
          '5': SCONJ
          '6': ADJ
          '7': PART
          '8': DET
          '9': CCONJ
          '10': PROPN
          '11': PRON
          '12': X
          '13': _
          '14': ADV
          '15': INTJ
          '16': VERB
          '17': AUX
  - name: xpos
    sequence: string
  - name: feats
    sequence: string
  - name: head
    sequence: string
  - name: deprel
    sequence: string
  - name: deps
    sequence: string
  - name: misc
    sequence: string
  splits:
  - name: train
    num_bytes: 0
    num_examples: 68943
  - name: dev
    num_bytes: 0
    num_examples: 7755
  - name: test
    num_bytes: 0
    num_examples: 8964
  download_size: 3088237
  dataset_size: 5120697
---
# Dataset Card for NLPre-PL – fairly divided version of NKJP1M

### Dataset Summary

This is the official NLPre-PL dataset - a uniformly paragraph-level divided version of NKJP1M corpus – the 1-million token balanced subcorpus of the National Corpus of Polish (Narodowy Korpus Języka Polskiego)

The NLPre dataset aims at fairly dividing the paragraphs length-wise and topic-wise into train, development, and test sets. Thus, we ensure a similar number of segments
distribution per paragraph and avoid the situation when paragraphs with a small (or large) number of segments are available only e.g. during test time.

We treat paragraphs as indivisible units (to ensure there is no data leakage between different dataset types). The paragraphs inherit the corresponding document's ID and type (a book, an article, etc.).

We provide two variations of the dataset, based on the fair division of paragraphs:
- fair by document's ID
- fair by document's type

### Creation of the dataset

We investigate the distribution over the number of segments in each paragraph. Being Gaussian-like, we divide the paragraphs into 10 buckets of roughly similar size and then sample from them with respective ratios of 0.8 : 0.1 : 0.1 
(corresponding to training, development, and testing subsets). 
This data selection technique assures a similar distribution of segment numbers per paragraph in our three subsets. We call it **fair_by_name** (shortly: **by_name**) 
since it is divided equitably regarding the unique IDs of the documents.

For creating our second split, we also consider the type of document a paragraph belongs to. We first group paragraphs into categories equal to the document types,
and then we repeat the above-mentioned procedure per category. This provides us with a second split: **fair_by_type** (shortly: **by_type**).

### Supported Tasks and Leaderboards

This resource can be mainly used for training the morphosyntactic analyzer models for Polish. It support such tasks as: lemmatization, part-of-speech recognition, dependency parsing.

### Languages

Polish (monolingual)

## Dataset Structure

### Data Instances

```
{'nkjp_text': 'NKJP_1M_1102000002',
 'nkjp_par': 'morph_1-p',
 'nkjp_sent': 'morph_1.18-s',
 'tokens': ['-', 'Nie', 'mam', 'pieniędzy', ',', 'da', 'mi', 'pani', 'wywiad', '?'],
 'lemmas': ['-', 'nie', 'mieć', 'pieniądz', ',', 'dać', 'ja', 'pani', 'wywiad', '?'],
 'cposes': [8, 11, 10, 9, 8, 10, 9, 9, 9, 8],
 'poses': [19, 25, 12, 35, 19, 12, 28, 35, 35, 19],
 'tags': [266, 464, 213, 923, 266, 218, 692, 988, 961, 266],
 'nps': [False, False, False, False, True, False, False, False, False, True],
 'nkjp_ids': ['morph_1.9-seg', 'morph_1.10-seg', 'morph_1.11-seg', 'morph_1.12-seg', 'morph_1.13-seg', 'morph_1.14-seg', 'morph_1.15-seg', 'morph_1.16-seg', 'morph_1.17-seg', 'morph_1.18-seg']}
```

### Data Fields

- `nkjp_text`, `nkjp_par`, `nkjp_sent` (strings): XML identifiers of the present text (document), paragraph and sentence in NKJP. (These allow to map the data point back to the source corpus and to identify paragraphs/samples.)
- `tokens` (sequence of strings): tokens of the text defined as in NKJP.
- `lemmas` (sequence of strings): lemmas corresponding to the tokens.
- `tags` (sequence of labels): morpho-syntactic tags according to Morfeusz2 tagset (1019 distinct tags).
- `poses` (sequence of labels): flexemic class (detailed part of speech, 40 classes) – the first element of the corresponding tag.
- `cposes` (sequence of labels): coarse part of speech (13 classes): all verbal and deverbal flexemic classes get mapped to a `V`, nominal – `N`, adjectival – `A`, “strange” (abbreviations, alien elements, symbols, emojis…)  – `X`, rest as in `poses`.
- `nps` (sequence of booleans): `True` means that the corresponding token is not preceded by a space in the source text.
- `nkjp_ids` (sequence of strings): XML identifiers of particular tokens in NKJP (probably an overkill).

### Data Splits

#### Fair_by_name

|           | Train  | Validation | Test   |
| -----     | ------ | -----      | ----   |
| sentences | 69360  | 7669       |  8633  |
| tokens    | 984077 | 109900     | 121907 |

#### Fair_by_type

|           | Train  | Validation | Test   |
| -----     | ------ | -----      | ----   |
| sentences | 68943  | 7755       |  8964  |
| tokens    | 978371 | 112454     | 125059 |


## Licensing Information

![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png) This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).


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### Contributions

Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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