added the dataset script and text files

.gitattributes

@@ -14,3 +14,6 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+train.txt filter=lfs diff=lfs merge=lfs -text
+validation.txt filter=lfs diff=lfs merge=lfs -text
+test.txt filter=lfs diff=lfs merge=lfs -text

conll2012.py

@@ -0,0 +1,239 @@
+# 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
+# CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{pradhan2012conll,
+  title={CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes},
+  author={Pradhan, Sameer and Moschitti, Alessandro and Xue, Nianwen and Uryupina, Olga and Zhang, Yuchen},
+  booktitle={Joint Conference on EMNLP and CoNLL-Shared Task},
+  pages={1--40},
+  year={2012}
+}
+"""
+
+_DESCRIPTION = """\
+The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and Arabic, using the final version, v5.0,
+of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes
+corpus, resources in this sub-field of language processing were limited to noun phrase coreference, often on a restricted
+set of entities, such as the ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not
+restricted to noun phrases or to a specified set of entity types, and covers multiple languages. OntoNotes also provides
+additional layers of integrated annotation, capturing additional shallow semantic structure. This paper describes the
+OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the
+format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating
+systems. The task of coreference has had a complex evaluation history. Potentially many evaluation conditions, have, in the past,
+made it difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set
+and standard evaluation parameters, all based on a resource that provides multiple integrated annotation layers (syntactic
+parses, semantic roles, word senses, named entities and coreference) and in multiple languages could support joint modeling
+and help ground and energize ongoing research in the task of entity and event coreference.
+For more details see https://aclanthology.org/W12-4501.pdf
+"""
+
+
+class Conll2012Config(datasets.BuilderConfig):
+    """BuilderConfig for Conll2012"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Conll2012.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Conll2012Config, self).__init__(**kwargs)
+
+
+class Conll2012(datasets.GeneratorBasedBuilder):
+    """Conll2012 dataset."""
+
+    BUILDER_CONFIGS = [
+        Conll2012Config(name="conll2012", version=datasets.Version("1.0.0"), description="Conll2012 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=[
+                                '$',
+                                "''",
+                                '*',
+                                ',',
+                                '-LRB-',
+                                '-RRB-',
+                                '.',
+                                ':',
+                                'ADD',
+                                'AFX',
+                                'CC',
+                                'CD',
+                                'DT',
+                                'EX',
+                                'FW',
+                                'HYPH',
+                                'IN',
+                                'JJ',
+                                'JJR',
+                                'JJS',
+                                'LS',
+                                'MD',
+                                'NFP',
+                                'NN',
+                                'NNP',
+                                'NNPS',
+                                'NNS',
+                                'PDT',
+                                'POS',
+                                'PRP',
+                                'PRP$',
+                                'RB',
+                                'RBR',
+                                'RBS',
+                                'RP',
+                                'SYM',
+                                'TO',
+                                'UH',
+                                'VB',
+                                'VBD',
+                                'VBG',
+                                'VBN',
+                                'VBP',
+                                'VBZ',
+                                'VERB',
+                                'WDT',
+                                'WP',
+                                'WP$',
+                                'WRB',
+                                'XX',
+                                '``'
+                            ]
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                'O',
+                                'B-CARDINAL',
+                                'B-DATE',
+                                'B-EVENT',
+                                'B-FAC',
+                                'B-GPE',
+                                'B-LANGUAGE',
+                                'B-LAW',
+                                'B-LOC',
+                                'B-MONEY',
+                                'B-NORP',
+                                'B-ORDINAL',
+                                'B-ORG',
+                                'B-PERCENT',
+                                'B-PERSON',
+                                'B-PRODUCT',
+                                'B-QUANTITY',
+                                'B-TIME',
+                                'B-WORK_OF_ART',
+                                'I-CARDINAL',
+                                'I-DATE',
+                                'I-EVENT',
+                                'I-FAC',
+                                'I-GPE',
+                                'I-LANGUAGE',
+                                'I-LAW',
+                                'I-LOC',
+                                'I-MONEY',
+                                'I-NORP',
+                                'I-ORDINAL',
+                                'I-ORG',
+                                'I-PERCENT',
+                                'I-PERSON',
+                                'I-PRODUCT',
+                                'I-QUANTITY',
+                                'I-TIME',
+                                'I-WORK_OF_ART'
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://catalog.ldc.upenn.edu/LDC2013T19",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            # "train": f"{_URL}{_TRAINING_FILE}",
+            # "validation": f"{_URL}{_DEV_FILE}",
+            # "test": f"{_URL}{_TEST_FILE}",
+            'train': 'train.txt',
+            'validation': 'validation.txt',
+            'test': 'test.txt',
+        }
+        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"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+
+        guid = 0
+        tokens = []
+        pos_tags = []
+        ner_tags = []
+
+        for line in lines:
+            if line.startswith("-DOCSTART-") or line.strip() == "" 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:
+                # conll2012 tokens are tab- separated
+                splits = line.split('\t')
+                tokens.append(splits[0])
+                pos_tags.append(splits[1])
+                ner_tags.append(splits[3].rstrip())
+        # last example
+        yield guid, {
+            "id": str(guid),
+            "tokens": tokens,
+            "pos_tags": pos_tags,
+            "ner_tags": ner_tags,
+        }

test.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ca6d86e6a641c0bb3c56638ce88abdcc56a243a3e678205d7fcd978108f1e9b
+size 3821129

train.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b05b94a76652c7d53979b6f854631afa8a8f0a50793f408c198af37d9323c19
+size 36758959

validation.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c78bc284b229a2d6d95ccb9190015aacfb75d8be4cdc773de24ad63007878e0
+size 5107622