Create conll2003.py

conll2003.py

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+# coding=utf-8
+# Copyright 2020 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
+"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
+
+import os
+
+import datasets
+from datasets import load_dataset
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
+    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.  and
+      De Meulder, Fien",
+    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
+    year = "2003",
+    url = "https://www.aclweb.org/anthology/W03-0419",
+    pages = "142--147",
+}
+"""
+
+_DESCRIPTION = """\
+The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
+four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
+not belong to the previous three groups.
+
+The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
+a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
+a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
+and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
+if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
+B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
+tagging scheme, whereas the original dataset uses IOB1.
+
+For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
+"""
+
+_URL = "http://github.com/lunesco/conll2003/raw/e3006b5e2c5c68c1c7ccc0f2583c7cab33a31c15/conll2003.zip"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "valid.txt"
+_TEST_FILE = "test.txt"
+
+
+class Conll2003Config(datasets.BuilderConfig):
+    """BuilderConfig for Conll2003"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forConll2003.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Conll2003Config, self).__init__(**kwargs)
+
+
+class Conll2003(datasets.GeneratorBasedBuilder):
+    """Conll2003 dataset."""
+
+    BUILDER_CONFIGS = [
+        Conll2003Config(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
+    ]
+
+    def _info(self):  # 49, 23, 42 dlugosc (vs 47, 23, 9)
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=['VAFIN', 'PPOSAT', 'NN', 'APPR', 'ADV', 'VVINF', '$.', 'NE',
+       'CARD', 'TRUNC', 'XY', 'ADJA', 'ART', 'VVFIN', 'PPER', 'APPRART',
+       '$[', 'VVPP', 'KON', '$,', 'PTKVZ', 'ADJD', 'PIAT', 'PRELS',
+       'PTKNEG', 'VAINF', 'VMFIN', 'PTKZU', 'PROAV', 'PIDAT', 'PDS',
+       'PWAV', 'PWS', 'KOUS', 'PIS', 'PRF', 'FM', 'ITJ', 'PTKANT', 'PDAT',
+       'VVIZU', 'PWAT', 'APZR', 'KOKOM', 'VVIMP', 'PTKA', 'KOUI', 'APPO',
+       'VAPP']
+                        )
+                    ),
+                    "chunk_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=['I-VA', 'I-PP', 'I-NN', 'I-AP', 'I-AD', 'I-VV', 'I-$.', 'I-NE',
+       'I-CA', 'I-TR', 'I-XY', 'I-AR', 'I-$[', 'I-KO', 'I-$,',
+       'I-PT', 'I-PI', 'I-PR', 'I-VM', 'I-PD', 'I-PW', 'I-FM', 'I-IT']
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=['O', 'B-organization-company', 'B-location-route',
+       'B-trigger', 'B-location-stop', 'B-date', 'B-location-city',
+       'B-event-cause', 'I-event-cause', 'B-time', 'I-time', 'B-number',
+       'B-organization', 'I-organization', 'B-location-street',
+       'I-trigger', 'B-location', 'I-location', 'I-location-city',
+       'I-organization-company', 'B-duration', 'I-duration',
+       'I-location-street', 'I-location-stop', 'I-location-route',
+       'B-person', 'I-date', 'B-set', 'B-money', 'I-person', 'I-money',
+       'B-distance', 'I-distance', 'I-number', 'B-disaster-type',
+       'B-org-position', 'I-org-position', 'I-set', 'B-percent',
+       'I-percent', 'I-disaster-type']
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.aclweb.org/anthology/W03-0419/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        data_files = {
+            "train": os.path.join(downloaded_file, _TRAINING_FILE),
+            "dev": os.path.join(downloaded_file, _DEV_FILE),
+            "test": os.path.join(downloaded_file, _TEST_FILE),
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            pos_tags = []
+            chunk_tags = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "pos_tags": pos_tags,
+                            "chunk_tags": chunk_tags,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                        chunk_tags = []
+                        ner_tags = []
+                else:
+                    # conll2003 tokens are space separated
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    pos_tags.append(splits[1])
+                    chunk_tags.append(splits[2])
+                    ner_tags.append(splits[3].rstrip())
+            # last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    "pos_tags": pos_tags,
+                    "chunk_tags": chunk_tags,
+                    "ner_tags": ner_tags,
+                }
+
+
+if __name__ == '__main__':
+    datasets = load_dataset('conll2003')