Update README.md (#1)

- Update README.md (69f5bd7c60aa50331c903af9b7dcf026a1c72135)

README.md

@@ -17,6 +17,7 @@ source_datasets:
 tags:
 - National Corpus of Polish
 - Narodowy Korpus Języka Polskiego
+- Universal Dependencies
 task_categories:
 - token-classification
 task_ids:
@@ -166,6 +167,69 @@ and then we repeat the above-mentioned procedure per category. This provides us
 
 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.
 
+### Supported versions
+
+This dataset is available for two tagsets and in 3 formats.
+
+Tagsets:
+- UD
+- NKJP
+
+File formats:
+- conllu
+- conll
+- conll with SpaceAfter token
+
+All the available combinations can be found below:
+
+- fair_by_name + nkjp tagset + conllu format
+
+```
+load_dataset("nlprepl", name="by_name-nkjp-conllu")
+```
+
+- fair_by_name + nkjp tagset + conll format
+
+```
+load_dataset("nlprepl", name="by_name-nkjp-conll")
+```
+
+- fair_by_name + nkjp tagset + conll-SpaceAfter format
+
+```
+load_dataset("nlprepl", name="by_name-nkjp-conll_space_after")
+```
+
+- fair_by_name + UD tagset + conllu format
+
+```
+load_dataset("nlprepl", name="by_name-nkjp-conllu")
+```
+
+- fair_by_type + nkjp tagset + conllu format
+
+```
+load_dataset("nlprepl", name="by_type-nkjp-conllu")
+```
+
+- fair_by_type + nkjp tagset + conll format
+
+```
+load_dataset("nlprepl", name="by_type-nkjp-conll")
+```
+
+- fair_by_type + nkjp tagset + conll-SpaceAfter format
+
+```
+load_dataset("nlprepl", name="by_type-nkjp-conll_space_after")
+```
+
+- fair_by_type + UD tagset + conllu format
+
+```
+load_dataset("nlprepl", name="by_type-nkjp-conllu")
+```
+
 ### Languages
 
 Polish (monolingual)
@@ -174,29 +238,51 @@ Polish (monolingual)
 
 ### Data Instances
 
+
+        "sent_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "lemmas": datasets.Sequence(datasets.Value("string")),
+                    "upos": datasets.Sequence(datasets.Value("string")),
+                    "xpos": datasets.Sequence(datasets.Value("string")),
+                    "feats": datasets.Sequence(datasets.Value("string")),
+                    "head": datasets.Sequence(datasets.Value("string")),
+                    "deprel": datasets.Sequence(datasets.Value("string")),
+                    "deps": datasets.Sequence(datasets.Value("string")),
+                    "misc"
 ```
-{'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']}
+{
+ 'sent_id': '3',
+ 'text': 'I zawrócił na rzekę.',
+ 'orig_file_sentence': '030-2-000000002#2-3',
+ 'id': ['1', '2', '3', '4', '5']
+ 'tokens': ['I', 'zawrócił', 'na', 'rzekę', '.'],
+ 'lemmas': ['i', 'zawrócić', 'na', 'rzeka', '.'],
+ 'upos': ['conj', 'praet', 'prep', 'subst', 'interp'],
+ 'xpos': ['con', 'praet:sg:m1:perf', 'prep:acc', 'subst:sg:acc:f', 'interp'],
+ 'feats': ['', 'sg|m1|perf', 'acc', 'sg|acc|f', ''],
+ 'head': ['0', '1', '2', '3', '1'],
+ 'deprel': ['root', 'conjunct', 'adjunct', 'comp', 'punct'],
+ 'deps': [''', '', '', '', ''],
+ 'misc': ['', '', '', '', '']
+}
 ```
 
 ### 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.)
+- `sent_id`, `text`, `orig_file_sentence` (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.)
+- `id` (sequence of strings): ids of the appropriate tokens.
 - `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).
+- `upos` (sequence of strings): universal part-of-speech tags corresponding to the tokens
+- `xpos` (sequence of labels): Optional language-specific (or treebank-specific) part-of-speech / morphological tag; underscore if not available.
+- `feats` (sequence of labels): List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.
+- `head` (sequence of labels): Head of the current word, which is either a value of ID or zero (0).
+- `deprel` (sequence of labels): Universal dependency relation to the HEAD of the token.
+- `deps` (sequence of labels): Enhanced dependency graph in the form of a list of head-deprel pairs.
+- `misc` (sequence of labels): Any other annotation (most commonly contains SpaceAfter tag).
+
 
 ### Data Splits