Upload CoNLL-NERC-es.py

CoNLL-NERC-es.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-2002 Shared Task: Language-Independent Named Entity Recognition"""
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{tjong-kim-sang-2002-introduction,
+    title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.",
+    booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
+    year = "2002",
+    url = "https://www.aclweb.org/anthology/W02-2024",
+}
+"""
+
+_DESCRIPTION = """\
+Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
+
+Example:
+[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
+
+The shared task of CoNLL-2002 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 participants of the shared task will be offered training and test data for at least two languages.
+They will use the data for developing a named-entity recognition system that includes a machine learning component.
+Information sources other than the training data may be used in this shared task.
+We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
+
+The train/validation/test sets are available in Spanish and Dutch.
+
+For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/
+"""
+
+_URL = "https://www.cs.upc.edu/~nlp/tools/nerc/"
+_TRAINING_FILE = "esp.train.gz"
+_DEV_FILE = "esp.testa.gz"
+_TEST_FILE = "esp.testb.gz"
+
+
+class Conll2002Config(datasets.BuilderConfig):
+    """BuilderConfig for Conll2002"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forConll2002.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Conll2002Config, self).__init__(**kwargs)
+
+
+class Conll2002(datasets.GeneratorBasedBuilder):
+    """Conll2002 dataset."""
+
+    BUILDER_CONFIGS = [
+        Conll2002Config(name="es", version=datasets.Version("1.0.0"), description="Conll2002 Spanish 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=[
+                                "AO",
+                                "AQ",
+                                "CC",
+                                "CS",
+                                "DA",
+                                "DE",
+                                "DD",
+                                "DI",
+                                "DN",
+                                "DP",
+                                "DT",
+                                "Faa",
+                                "Fat",
+                                "Fc",
+                                "Fd",
+                                "Fe",
+                                "Fg",
+                                "Fh",
+                                "Fia",
+                                "Fit",
+                                "Fp",
+                                "Fpa",
+                                "Fpt",
+                                "Fs",
+                                "Ft",
+                                "Fx",
+                                "Fz",
+                                "I",
+                                "NC",
+                                "NP",
+                                "P0",
+                                "PD",
+                                "PI",
+                                "PN",
+                                "PP",
+                                "PR",
+                                "PT",
+                                "PX",
+                                "RG",
+                                "RN",
+                                "SP",
+                                "VAI",
+                                "VAM",
+                                "VAN",
+                                "VAP",
+                                "VAS",
+                                "VMG",
+                                "VMI",
+                                "VMM",
+                                "VMN",
+                                "VMP",
+                                "VMS",
+                                "VSG",
+                                "VSI",
+                                "VSM",
+                                "VSN",
+                                "VSP",
+                                "VSS",
+                                "Y",
+                                "Z",
+                            ]
+                        )
+                        if self.config.name == "es"
+                        else datasets.features.ClassLabel(
+                            names=["Adj", "Adv", "Art", "Conj", "Int", "Misc", "N", "Num", "Prep", "Pron", "Punc", "V"]
+                        )
+                    ),
+                    "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/W02-2024/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        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["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="latin-1") as f:
+            guid = 0
+            tokens = []
+            pos_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,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                        ner_tags = []
+                else:
+                    # conll2002 tokens are space separated
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    pos_tags.append(splits[1])
+                    ner_tags.append(splits[2].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "pos_tags": pos_tags,
+                "ner_tags": ner_tags,
+            }