Update files from the datasets library (from 1.2.0)

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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- id
+licenses:
+- other-nergrit-license
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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:** [PT Gria Inovasi Teknologi](https://grit.id/)
+- **Repository:** [Nergrit Corpus](https://github.com/grit-id/nergrit-corpus)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** [Taufiqur Rohman](mailto:taufiq@grit.id)
+
+### Dataset Summary
+
+Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
+and Sentiment Analysis developed by [PT Gria Inovasi Teknologi (GRIT)](https://grit.id/). 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Indonesian
+
+## Dataset Structure
+
+A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
+```
+{'id': '0',
+ 'tokens': ['Gubernur', 'Bank', 'Indonesia', 'menggelar', 'konferensi', 'pers'],
+ 'ner_tags': [9, 28, 28, 38, 38, 38],
+}
+```
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+- `id`: id of the sample
+- `tokens`: the tokens of the example text
+- `ner_tags`: the NER tags of each token
+
+#### Named Entity Recognition
+The ner_tags correspond to this list:
+```
+"B-CRD", "B-DAT", "B-EVT", "B-FAC", "B-GPE", "B-LAN", "B-LAW", "B-LOC", "B-MON", "B-NOR", 
+"B-ORD", "B-ORG", "B-PER", "B-PRC", "B-PRD", "B-QTY", "B-REG", "B-TIM", "B-WOA",
+"I-CRD", "I-DAT", "I-EVT", "I-FAC", "I-GPE", "I-LAN", "I-LAW", "I-LOC", "I-MON", "I-NOR",
+"I-ORD", "I-ORG", "I-PER", "I-PRC", "I-PRD", "I-QTY", "I-REG", "I-TIM", "I-WOA", "O",
+```
+The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
+non-initial word. The dataset contains 19 following entities
+```
+    'CRD': Cardinal
+    'DAT': Date
+    'EVT': Event
+    'FAC': Facility
+    'GPE': Geopolitical Entity
+    'LAW': Law Entity (such as Undang-Undang)
+    'LOC': Location
+    'MON': Money
+    'NOR': Political Organization
+    'ORD': Ordinal
+    'ORG': Organization
+    'PER': Person
+    'PRC': Percent
+    'PRD': Product
+    'QTY': Quantity
+    'REG': Religion
+    'TIM': Time
+    'WOA': Work of Art
+    'LAN': Language
+```
+#### Sentiment Analysis
+The ner_tags correspond to this list:
+```
+"B-NEG", "B-NET", "B-POS",
+"I-NEG", "I-NET", "I-POS",
+"O",
+```
+
+#### Statement Extraction
+The ner_tags correspond to this list:
+```
+"B-BREL", "B-FREL", "B-STAT", "B-WHO",
+"I-BREL", "I-FREL", "I-STAT", "I-WHO", 
+"O"
+```
+The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
+non-initial word.
+
+### Data Splits
+
+The dataset is splitted in to train, validation and test sets.
+
+## 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?
+The annotators are listed in the
+[Nergrit Corpus repository](https://github.com/grit-id/nergrit-corpus)
+
+### 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
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"ner": {"description": "Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment\nAnalysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as\nfollow:\n    'CRD': Cardinal\n    'DAT': Date\n    'EVT': Event\n    'FAC': Facility\n    'GPE': Geopolitical Entity\n    'LAW': Law Entity (such as Undang-Undang)\n    'LOC': Location\n    'MON': Money\n    'NOR': Political Organization\n    'ORD': Ordinal\n    'ORG': Organization\n    'PER': Person\n    'PRC': Percent\n    'PRD': Product\n    'QTY': Quantity\n    'REG': Religion\n    'TIM': Time\n    'WOA': Work of Art\n    'LAN': Language\n", "citation": "@inproceedings{id_nergrit_corpus,\n  author    = {Gria Inovasi Teknologi},\n  title     = {NERGRIT CORPUS},\n  year      = {2019},\n  url       = {https://github.com/grit-id/nergrit-corpus},\n}\n", "homepage": "https://github.com/grit-id/nergrit-corpus", "license": "", "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": 39, "names": ["B-CRD", "B-DAT", "B-EVT", "B-FAC", "B-GPE", "B-LAN", "B-LAW", "B-LOC", "B-MON", "B-NOR", "B-ORD", "B-ORG", "B-PER", "B-PRC", "B-PRD", "B-QTY", "B-REG", "B-TIM", "B-WOA", "I-CRD", "I-DAT", "I-EVT", "I-FAC", "I-GPE", "I-LAN", "I-LAW", "I-LOC", "I-MON", "I-NOR", "I-ORD", "I-ORG", "I-PER", "I-PRC", "I-PRD", "I-QTY", "I-REG", "I-TIM", "I-WOA", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "id_nergrit_corpus", "config_name": "ner", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5428411, "num_examples": 12532, "dataset_name": "id_nergrit_corpus"}, "test": {"name": "test", "num_bytes": 1135577, "num_examples": 2399, "dataset_name": "id_nergrit_corpus"}, "validation": {"name": "validation", "num_bytes": 1086437, "num_examples": 2521, "dataset_name": "id_nergrit_corpus"}}, "download_checksums": {"https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/nergrit-corpus_20190726_corrected.tgz": {"num_bytes": 14988232, "checksum": "ac53b61612d6d53c8c800a67d70b6b800f662ab7029aa622163834945efa85d6"}}, "download_size": 14988232, "post_processing_size": null, "dataset_size": 7650425, "size_in_bytes": 22638657}, "sentiment": {"description": "Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment\nAnalysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as\nfollow:\n    'CRD': Cardinal\n    'DAT': Date\n    'EVT': Event\n    'FAC': Facility\n    'GPE': Geopolitical Entity\n    'LAW': Law Entity (such as Undang-Undang)\n    'LOC': Location\n    'MON': Money\n    'NOR': Political Organization\n    'ORD': Ordinal\n    'ORG': Organization\n    'PER': Person\n    'PRC': Percent\n    'PRD': Product\n    'QTY': Quantity\n    'REG': Religion\n    'TIM': Time\n    'WOA': Work of Art\n    'LAN': Language\n", "citation": "@inproceedings{id_nergrit_corpus,\n  author    = {Gria Inovasi Teknologi},\n  title     = {NERGRIT CORPUS},\n  year      = {2019},\n  url       = {https://github.com/grit-id/nergrit-corpus},\n}\n", "homepage": "https://github.com/grit-id/nergrit-corpus", "license": "", "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": 7, "names": ["B-NEG", "B-NET", "B-POS", "I-NEG", "I-NET", "I-POS", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "id_nergrit_corpus", "config_name": "sentiment", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3167972, "num_examples": 7485, "dataset_name": "id_nergrit_corpus"}, "test": {"name": "test", "num_bytes": 1097517, "num_examples": 2317, "dataset_name": "id_nergrit_corpus"}, "validation": {"name": "validation", "num_bytes": 337679, "num_examples": 782, "dataset_name": "id_nergrit_corpus"}}, "download_checksums": {"https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/nergrit-corpus_20190726_corrected.tgz": {"num_bytes": 14988232, "checksum": "ac53b61612d6d53c8c800a67d70b6b800f662ab7029aa622163834945efa85d6"}}, "download_size": 14988232, "post_processing_size": null, "dataset_size": 4603168, "size_in_bytes": 19591400}, "statement": {"description": "Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment\nAnalysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as\nfollow:\n    'CRD': Cardinal\n    'DAT': Date\n    'EVT': Event\n    'FAC': Facility\n    'GPE': Geopolitical Entity\n    'LAW': Law Entity (such as Undang-Undang)\n    'LOC': Location\n    'MON': Money\n    'NOR': Political Organization\n    'ORD': Ordinal\n    'ORG': Organization\n    'PER': Person\n    'PRC': Percent\n    'PRD': Product\n    'QTY': Quantity\n    'REG': Religion\n    'TIM': Time\n    'WOA': Work of Art\n    'LAN': Language\n", "citation": "@inproceedings{id_nergrit_corpus,\n  author    = {Gria Inovasi Teknologi},\n  title     = {NERGRIT CORPUS},\n  year      = {2019},\n  url       = {https://github.com/grit-id/nergrit-corpus},\n}\n", "homepage": "https://github.com/grit-id/nergrit-corpus", "license": "", "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": 9, "names": ["B-BREL", "B-FREL", "B-STAT", "B-WHO", "I-BREL", "I-FREL", "I-STAT", "I-WHO", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "id_nergrit_corpus", "config_name": "statement", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1469081, "num_examples": 2405, "dataset_name": "id_nergrit_corpus"}, "test": {"name": "test", "num_bytes": 182553, "num_examples": 335, "dataset_name": "id_nergrit_corpus"}, "validation": {"name": "validation", "num_bytes": 105119, "num_examples": 176, "dataset_name": "id_nergrit_corpus"}}, "download_checksums": 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id_nergrit_corpus.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.
+"""Nergrit Corpus"""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{id_nergrit_corpus,
+  author    = {Gria Inovasi Teknologi},
+  title     = {NERGRIT CORPUS},
+  year      = {2019},
+  url       = {https://github.com/grit-id/nergrit-corpus},
+}
+"""
+
+_DESCRIPTION = """\
+Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment
+Analysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as
+follow:
+    'CRD': Cardinal
+    'DAT': Date
+    'EVT': Event
+    'FAC': Facility
+    'GPE': Geopolitical Entity
+    'LAW': Law Entity (such as Undang-Undang)
+    'LOC': Location
+    'MON': Money
+    'NOR': Political Organization
+    'ORD': Ordinal
+    'ORG': Organization
+    'PER': Person
+    'PRC': Percent
+    'PRD': Product
+    'QTY': Quantity
+    'REG': Religion
+    'TIM': Time
+    'WOA': Work of Art
+    'LAN': Language
+"""
+
+_HOMEPAGE = "https://github.com/grit-id/nergrit-corpus"
+
+_LICENSE = ""
+
+_URLs = [
+    "https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/nergrit-corpus_20190726_corrected.tgz",
+    "https://cloud.uncool.ai/index.php/s/2QEcMrgwkjMAo4o/download",
+]
+
+
+class IdNergritCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for IdNergritCorpus"""
+
+    def __init__(self, label_classes=None, **kwargs):
+        """BuilderConfig for IdNergritCorpus.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(IdNergritCorpusConfig, self).__init__(**kwargs)
+        self.label_classes = label_classes
+
+
+class IdNergritCorpus(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        IdNergritCorpusConfig(
+            name="ner",
+            version=VERSION,
+            description="Named Entity Recognition dataset of Nergrit Corpus",
+            label_classes=[
+                "B-CRD",
+                "B-DAT",
+                "B-EVT",
+                "B-FAC",
+                "B-GPE",
+                "B-LAN",
+                "B-LAW",
+                "B-LOC",
+                "B-MON",
+                "B-NOR",
+                "B-ORD",
+                "B-ORG",
+                "B-PER",
+                "B-PRC",
+                "B-PRD",
+                "B-QTY",
+                "B-REG",
+                "B-TIM",
+                "B-WOA",
+                "I-CRD",
+                "I-DAT",
+                "I-EVT",
+                "I-FAC",
+                "I-GPE",
+                "I-LAN",
+                "I-LAW",
+                "I-LOC",
+                "I-MON",
+                "I-NOR",
+                "I-ORD",
+                "I-ORG",
+                "I-PER",
+                "I-PRC",
+                "I-PRD",
+                "I-QTY",
+                "I-REG",
+                "I-TIM",
+                "I-WOA",
+                "O",
+            ],
+        ),
+        IdNergritCorpusConfig(
+            name="sentiment",
+            version=VERSION,
+            description="Sentiment Analysis dataset of Nergrit Corpus",
+            label_classes=[
+                "B-NEG",
+                "B-NET",
+                "B-POS",
+                "I-NEG",
+                "I-NET",
+                "I-POS",
+                "O",
+            ],
+        ),
+        IdNergritCorpusConfig(
+            name="statement",
+            version=VERSION,
+            description="Statement Extraction dataset of Nergrit Corpus",
+            label_classes=[
+                "B-BREL",
+                "B-FREL",
+                "B-STAT",
+                "B-WHO",
+                "I-BREL",
+                "I-FREL",
+                "I-STAT",
+                "I-WHO",
+                "O",
+            ],
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=self.config.label_classes)),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        my_urls = _URLs[0]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "nergrit-corpus/{}/data/train_corrected.txt".format(self.config.name)
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "nergrit-corpus/{}/data/test_corrected.txt".format(self.config.name)
+                    ),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "nergrit-corpus/{}/data/valid_corrected.txt".format(self.config.name)
+                    ),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        logging.info("⏳ Generating %s examples from = %s", split, filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                splits = line.strip().split()
+                if len(splits) != 2:
+                    if tokens:
+                        assert len(tokens) == len(ner_tags), "word len doesn't match label length"
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }