Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- expert-generated
+languages:
+- tr
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+
+# Dataset Card for turkish_ner
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** http://arxiv.org/abs/1702.02363
+- **Repository:** [Needs More Information]
+- **Paper:** http://arxiv.org/abs/1702.02363
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [email protected]
+
+### Dataset Summary
+
+Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Turkish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+There's only the training set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren and Omer Ozan Sonmez
+
+### Licensing Information
+
+Creative Commons Attribution 4.0 International
+
+### Citation Information
+
+@InProceedings@article{DBLP:journals/corr/SahinTYES17,
+  author    = {H. Bahadir Sahin and
+               Caglar Tirkaz and
+               Eray Yildiz and
+               Mustafa Tolga Eren and
+               Omer Ozan Sonmez},
+  title     = {Automatically Annotated Turkish Corpus for Named Entity Recognition
+               and Text Categorization using Large-Scale Gazetteers},
+  journal   = {CoRR},
+  volume    = {abs/1702.02363},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1702.02363},
+  archivePrefix = {arXiv},
+  eprint    = {1702.02363},
+  timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}

dataset_infos.json

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+{"default": {"description": "Turkish Wikipedia Named-Entity Recognition and Text Categorization\n(TWNERTC) dataset is a collection of automatically categorized and annotated\nsentences obtained from Wikipedia. The authors constructed large-scale\ngazetteers by using a graph crawler algorithm to extract\nrelevant entity and domain information\nfrom a semantic knowledge base, Freebase.\nThe constructed gazetteers contains approximately\n300K entities with thousands of fine-grained entity types\nunder 77 different domains.\n", "citation": "@InProceedings@article{DBLP:journals/corr/SahinTYES17,\n  author    = {H. Bahadir Sahin and\n               Caglar Tirkaz and\n               Eray Yildiz and\n               Mustafa Tolga Eren and\n               Omer Ozan Sonmez},\n  title     = {Automatically Annotated Turkish Corpus for Named Entity Recognition\n               and Text Categorization using Large-Scale Gazetteers},\n  journal   = {CoRR},\n  volume    = {abs/1702.02363},\n  year      = {2017},\n  url       = {http://arxiv.org/abs/1702.02363},\n  archivePrefix = {arXiv},\n  eprint    = {1702.02363},\n  timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://data.mendeley.com/datasets/cdcztymf4k/1", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"num_classes": 25, "names": ["architecture", "basketball", "book", "business", "education", "fictional_universe", "film", "food", "geography", "government", "law", "location", "military", "music", "opera", "organization", "people", "religion", "royalty", "soccer", "sports", "theater", "time", "travel", "tv"], "names_file": null, "id": null, "_type": "ClassLabel"}, "ner_tags": {"feature": {"num_classes": 9, "names": ["O", "B-PERSON", "I-PERSON", "B-ORGANIZATION", "I-ORGANIZATION", "B-LOCATION", "I-LOCATION", "B-MISC", "I-MISC"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "turkish_ner", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 177658278, "num_examples": 532629, "dataset_name": "turkish_ner"}}, "download_checksums": {"https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded": {"num_bytes": 204393976, "checksum": "e03e2867a225d63f0139dd4ced028e5da795a8a48e140ad4c17999a8560dbc57"}}, "download_size": 204393976, "post_processing_size": null, "dataset_size": 177658278, "size_in_bytes": 382052254}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ce14f1ea12dda073ba35fe0381e477c03c176f8c4d192d87b72977846b89c9d
+size 5801

turkish_ner.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings@article{DBLP:journals/corr/SahinTYES17,
+  author    = {H. Bahadir Sahin and
+               Caglar Tirkaz and
+               Eray Yildiz and
+               Mustafa Tolga Eren and
+               Omer Ozan Sonmez},
+  title     = {Automatically Annotated Turkish Corpus for Named Entity Recognition
+               and Text Categorization using Large-Scale Gazetteers},
+  journal   = {CoRR},
+  volume    = {abs/1702.02363},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1702.02363},
+  archivePrefix = {arXiv},
+  eprint    = {1702.02363},
+  timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Turkish Wikipedia Named-Entity Recognition and Text Categorization
+(TWNERTC) dataset is a collection of automatically categorized and annotated
+sentences obtained from Wikipedia. The authors constructed large-scale
+gazetteers by using a graph crawler algorithm to extract
+relevant entity and domain information
+from a semantic knowledge base, Freebase.
+The constructed gazetteers contains approximately
+300K entities with thousands of fine-grained entity types
+under 77 different domains.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://data.mendeley.com/datasets/cdcztymf4k/1"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_URL = "https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded"
+
+
+_FILE_NAME_ZIP = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.zip"
+_FILE_NAME = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.DUMP"
+
+
+class TurkishNER(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "domain": datasets.ClassLabel(
+                        names=[
+                            "architecture",
+                            "basketball",
+                            "book",
+                            "business",
+                            "education",
+                            "fictional_universe",
+                            "film",
+                            "food",
+                            "geography",
+                            "government",
+                            "law",
+                            "location",
+                            "military",
+                            "music",
+                            "opera",
+                            "organization",
+                            "people",
+                            "religion",
+                            "royalty",
+                            "soccer",
+                            "sports",
+                            "theater",
+                            "time",
+                            "travel",
+                            "tv",
+                        ]
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PERSON",
+                                "I-PERSON",
+                                "B-ORGANIZATION",
+                                "I-ORGANIZATION",
+                                "B-LOCATION",
+                                "I-LOCATION",
+                                "B-MISC",
+                                "I-MISC",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.extract(os.path.join(dl_manager.download_and_extract(_URL), _FILE_NAME_ZIP))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": (os.path.join(data_dir, _FILE_NAME)),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        logging.info("⏳ Generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+            id_ = -1
+            for line in f:
+                if line == "" or line == "\n":
+                    continue
+                else:
+                    splits = line.split("\t")
+                    id_ += 1
+                    yield id_, {
+                        "id": str(id_),
+                        "domain": splits[0],
+                        "tokens": splits[2].split(" "),
+                        "ner_tags": splits[1].split(" "),
+                    }