Add loader

nkjp-pos.py

@@ -0,0 +1,164 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""NKJP-POS tagging dataset."""
+
+import csv
+from typing import List, Tuple, Dict, Generator
+
+import datasets
+
+_DESCRIPTION = """NKJP-POS tagging dataset."""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/train.tsv",
+    "validation": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/valid.tsv",
+    "test": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/test.tsv",
+}
+
+_HOMEPAGE = "http://clip.ipipan.waw.pl/NationalCorpusOfPolish"
+
+_POS_TAGS = [
+    'adj',
+    'adja',
+    'adjc',
+    'adjp',
+    'adv',
+    'aglt',
+    'bedzie',
+    'brev',
+    'burk',
+    'comp',
+    'conj',
+    'depr',
+    'fin',
+    'ger',
+    'imps',
+    'impt',
+    'inf',
+    'interj',
+    'interp',
+    'num',
+    'numcol',
+    'pact',
+    'pant',
+    'pcon',
+    'ppas',
+    'ppron12',
+    'ppron3',
+    'praet',
+    'pred',
+    'prep',
+    'qub',
+    'siebie',
+    'subst',
+    'winien',
+    'xxx'
+]
+
+
+class NKJPPOS(datasets.GeneratorBasedBuilder):
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "morph": datasets.Sequence(datasets.Value("string")),
+                    "lemmas": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(datasets.features.ClassLabel(
+                        names=_POS_TAGS,
+                        num_classes=len(_POS_TAGS)
+                    )),
+                    "full_pos_tags": datasets.Sequence(
+                        datasets.Value("string")),
+                    "nps": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            homepage=_HOMEPAGE,
+        )
+
+    def _split_generators(
+            self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"]},
+            ),
+        ]
+
+    @staticmethod
+    def _parse_tag(
+            tag: str
+    ) -> Tuple[str, str]:
+        full_tag = tag
+        pos_tag = tag.split(':')[0]
+
+        return pos_tag, full_tag
+
+    def _generate_examples(
+            self, filepath: str
+    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
+        with open(filepath, 'r') as f:
+            reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
+
+            tokens = []
+            morph = []
+            tags = []
+            full_tags = []
+            lemma = []
+            nps = []
+            gid = 0
+
+            for line in reader:
+                if not line:
+                    yield gid, {
+                        'tokens': tokens,
+                        'morph': morph,
+                        'pos_tags': tags,
+                        'full_pos_tags': full_tags,
+                        'lemmas': lemma,
+                        'nps': nps
+                    }
+                    gid += 1
+                    tokens = []
+                    morph = []
+                    tags = []
+                    full_tags = []
+                    lemma = []
+                    nps = []
+
+                else:
+                    tokens.append(line[0])
+                    morph.append(line[1])
+                    lemma.append(line[3])
+                    nps.append(line[4])
+                    tag, full_tag = self._parse_tag(line[2])
+
+                    tags.append(tag)
+                    full_tags.append(full_tag)