setup

README.md

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----
-license: other
----
+# Mini conll dataset see https://huggingface.co/datasets/conll2003 for the original

conll2003_mini.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
+
+
+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 = """\
+
+!! forked version producing at most 10 items per split !!
+
+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 = "https://data.deepai.org/conll2003.zip"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "valid.txt"
+_TEST_FILE = "test.txt"
+
+_MAX_ITEMS = 10
+
+
+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):
+        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=[
+                                '"',
+                                "''",
+                                "#",
+                                "$",
+                                "(",
+                                ")",
+                                ",",
+                                ".",
+                                ":",
+                                "``",
+                                "CC",
+                                "CD",
+                                "DT",
+                                "EX",
+                                "FW",
+                                "IN",
+                                "JJ",
+                                "JJR",
+                                "JJS",
+                                "LS",
+                                "MD",
+                                "NN",
+                                "NNP",
+                                "NNPS",
+                                "NNS",
+                                "NN|SYM",
+                                "PDT",
+                                "POS",
+                                "PRP",
+                                "PRP$",
+                                "RB",
+                                "RBR",
+                                "RBS",
+                                "RP",
+                                "SYM",
+                                "TO",
+                                "UH",
+                                "VB",
+                                "VBD",
+                                "VBG",
+                                "VBN",
+                                "VBP",
+                                "VBZ",
+                                "WDT",
+                                "WP",
+                                "WP$",
+                                "WRB",
+                            ]
+                        )
+                    ),
+                    "chunk_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-ADJP",
+                                "I-ADJP",
+                                "B-ADVP",
+                                "I-ADVP",
+                                "B-CONJP",
+                                "I-CONJP",
+                                "B-INTJ",
+                                "I-INTJ",
+                                "B-LST",
+                                "I-LST",
+                                "B-NP",
+                                "I-NP",
+                                "B-PP",
+                                "I-PP",
+                                "B-PRT",
+                                "I-PRT",
+                                "B-SBAR",
+                                "I-SBAR",
+                                "B-UCP",
+                                "I-UCP",
+                                "B-VP",
+                                "I-VP",
+                            ]
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-MISC",
+                                "I-MISC",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            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":
+
+                    # produce at most _MAX_ITEMS
+                    if guid >= _MAX_ITEMS:
+                        return
+
+                    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,
+                }