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:
+- extended|other-turkish_ner
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card for turkish_shrinked_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:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** https://www.kaggle.com/behcetsenturk
+
+### Dataset Summary
+
+Shrinked processed version (48 entity type) of the turkish_ner.
+
+Original turkish_ner dataset: 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.
+
+Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Turkish
+
+## Dataset Structure
+
+### Data Instances
+
+[Needs More Information]
+
+### Data Fields
+
+[Needs More Information]
+
+### Data Splits
+
+There's only the training set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+Behcet Senturk
+
+### Licensing Information
+
+Creative Commons Attribution 4.0 International
+
+### Citation Information
+
+[Needs More Information]

dataset_infos.json

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+{"default": {"description": "Shrinked version (48 entity type) of the turkish_ner.\n\nOriginal turkish_ner dataset: 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.\n\nShrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle\n", "citation": "", "homepage": "https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar", "license": "Attribution 4.0 International (CC BY 4.0)", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 97, "names": ["O", "B-academic", "I-academic", "B-academic_person", "I-academic_person", "B-aircraft", "I-aircraft", "B-album_person", "I-album_person", "B-anatomy", "I-anatomy", "B-animal", "I-animal", "B-architect_person", "I-architect_person", "B-capital", "I-capital", "B-chemical", "I-chemical", "B-clothes", "I-clothes", "B-country", "I-country", "B-culture", "I-culture", "B-currency", "I-currency", "B-date", "I-date", "B-food", "I-food", "B-genre", "I-genre", "B-government", "I-government", "B-government_person", "I-government_person", "B-language", "I-language", "B-location", "I-location", "B-material", "I-material", "B-measure", "I-measure", "B-medical", "I-medical", "B-military", "I-military", "B-military_person", "I-military_person", "B-nation", "I-nation", "B-newspaper", "I-newspaper", "B-organization", "I-organization", "B-organization_person", "I-organization_person", "B-person", "I-person", "B-production_art_music", "I-production_art_music", "B-production_art_music_person", "I-production_art_music_person", "B-quantity", "I-quantity", "B-religion", "I-religion", "B-science", "I-science", "B-shape", "I-shape", "B-ship", "I-ship", "B-software", "I-software", "B-space", "I-space", "B-space_person", "I-space_person", "B-sport", "I-sport", "B-sport_name", "I-sport_name", "B-sport_person", "I-sport_person", "B-structure", "I-structure", "B-subject", "I-subject", "B-tech", "I-tech", "B-train", "I-train", "B-vehicle", "I-vehicle"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "turkish_shrinked_ner", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 200728389, "num_examples": 614515, "dataset_name": "turkish_shrinked_ner"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 200728389, "size_in_bytes": 200728389}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66afcc7a8328814a5fe5beb583bba3cc384f0897b7bf9ffa7ff37b927a148859
+size 30664

turkish_shrinked_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.
+""" Shrinked Turkish NER """
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+Shrinked version (48 entity type) of the turkish_ner.
+
+Original turkish_ner dataset: 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.
+
+Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar"
+
+_LICENSE = "Attribution 4.0 International (CC BY 4.0)"
+
+_FILENAME = "train.txt"
+
+
+class TurkishShrinkedNER(datasets.GeneratorBasedBuilder):
+    @property
+    def manual_download_instructions(self):
+        return """\
+    You need to go to https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar,
+    and manually download the turkish_shrinked_ner. Once it is completed,
+    a file named archive.zip will be appeared in your Downloads folder
+    or whichever folder your browser chooses to save files to. You then have
+    to unzip the file and move train.txt under <path/to/folder>.
+    The <path/to/folder> can e.g. be "~/manual_data".
+    turkish_shrinked_ner can then be loaded using the following command `datasets.load_dataset("turkish_shrinked_ner", data_dir="<path/to/folder>")`.
+    """
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-academic",
+                                "I-academic",
+                                "B-academic_person",
+                                "I-academic_person",
+                                "B-aircraft",
+                                "I-aircraft",
+                                "B-album_person",
+                                "I-album_person",
+                                "B-anatomy",
+                                "I-anatomy",
+                                "B-animal",
+                                "I-animal",
+                                "B-architect_person",
+                                "I-architect_person",
+                                "B-capital",
+                                "I-capital",
+                                "B-chemical",
+                                "I-chemical",
+                                "B-clothes",
+                                "I-clothes",
+                                "B-country",
+                                "I-country",
+                                "B-culture",
+                                "I-culture",
+                                "B-currency",
+                                "I-currency",
+                                "B-date",
+                                "I-date",
+                                "B-food",
+                                "I-food",
+                                "B-genre",
+                                "I-genre",
+                                "B-government",
+                                "I-government",
+                                "B-government_person",
+                                "I-government_person",
+                                "B-language",
+                                "I-language",
+                                "B-location",
+                                "I-location",
+                                "B-material",
+                                "I-material",
+                                "B-measure",
+                                "I-measure",
+                                "B-medical",
+                                "I-medical",
+                                "B-military",
+                                "I-military",
+                                "B-military_person",
+                                "I-military_person",
+                                "B-nation",
+                                "I-nation",
+                                "B-newspaper",
+                                "I-newspaper",
+                                "B-organization",
+                                "I-organization",
+                                "B-organization_person",
+                                "I-organization_person",
+                                "B-person",
+                                "I-person",
+                                "B-production_art_music",
+                                "I-production_art_music",
+                                "B-production_art_music_person",
+                                "I-production_art_music_person",
+                                "B-quantity",
+                                "I-quantity",
+                                "B-religion",
+                                "I-religion",
+                                "B-science",
+                                "I-science",
+                                "B-shape",
+                                "I-shape",
+                                "B-ship",
+                                "I-ship",
+                                "B-software",
+                                "I-software",
+                                "B-space",
+                                "I-space",
+                                "B-space_person",
+                                "I-space_person",
+                                "B-sport",
+                                "I-sport",
+                                "B-sport_name",
+                                "I-sport_name",
+                                "B-sport_person",
+                                "I-sport_person",
+                                "B-structure",
+                                "I-structure",
+                                "B-subject",
+                                "I-subject",
+                                "B-tech",
+                                "I-tech",
+                                "B-train",
+                                "I-train",
+                                "B-vehicle",
+                                "I-vehicle",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            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."""
+        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        if not os.path.exists(path_to_manual_file):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('turkish_shrinked_ner', data_dir=...)` that includes file name {}. Manual download instructions: {}".format(
+                    path_to_manual_file,
+                    _FILENAME,
+                    self.manual_download_instructions,
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(path_to_manual_file, "train.txt"),
+                    "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_ = 0
+            tokens = []
+            ner_tags = []
+            for row in f:
+                if row == "":
+                    continue
+                elif row == "\n":
+                    yield id_, {
+                        "id": str(id_),
+                        "tokens": tokens,
+                        "ner_tags": ner_tags,
+                    }
+                    tokens = []
+                    ner_tags = []
+                    id_ += 1
+                else:
+                    token, tag = row.split(" ")
+                    tokens.append(token)
+                    ner_tags.append(tag)
+
+            if len(tokens) > 0:
+                yield id_, {
+                    "id": str(id_),
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                }