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:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- ha
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card for Hausa VOA NER Corpus
+
+## 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.aclweb.org/anthology/2020.emnlp-main.204/
+- **Repository:** [Hausa VOA NER](https://github.com/uds-lsv/transfer-distant-transformer-african/tree/master/data/hausa_ner)
+- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
+- **Leaderboard:**
+- **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de)
+
+### Dataset Summary
+The Hausa VOA NER is a named entity recognition (NER) dataset for Hausa language based on the [VOA Hausa news](https://www.voahausa.com/) corpus. 
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The language supported is Hausa.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
+{'id': '0',
+ 'ner_tags': [B-PER, 0, 0, B-LOC, 0],
+ 'tokens': ['Trump', 'ya', 'ce', 'Rasha', 'ma']
+}
+
+### Data Fields
+
+- `id`: id of the sample
+- `tokens`: the tokens of the example text
+- `ner_tags`: the NER tags of each token
+
+The NER tags correspond to this list:
+```
+"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
+```
+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. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
+
+### Data Splits
+
+Training (1,014 sentences), validation (145 sentences) and test split (291 sentences)
+
+## Dataset Creation
+
+### Curation Rationale
+
+The data was created to help introduce resources to new language - Hausa.
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset is based on the news domain and was crawled from [VOA Hausa news](https://www.voahausa.com/).
+
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+The dataset was collected from VOA Hausa news. Most of the texts used in creating the Hausa VOA NER are news stories from Nigeria, Niger Republic, United States, and other parts of the world.
+
+[More Information Needed]
+
+### Annotations
+Named entity recognition annotation
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+The data was annotated by Jesujoba Alabi and David Adelani for the paper: 
+[Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://www.aclweb.org/anthology/2020.emnlp-main.204/).
+
+[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
+
+The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
+
+
+### Licensing Information
+
+The data is under the [Creative Commons Attribution 4.0 ](https://creativecommons.org/licenses/by/4.0/)
+
+### Citation Information
+```
+@inproceedings{hedderich-etal-2020-transfer,
+    title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
+    author = "Hedderich, Michael A.  and
+      Adelani, David  and
+      Zhu, Dawei  and
+      Alabi, Jesujoba  and
+      Markus, Udia  and
+      Klakow, Dietrich",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
+    doi = "10.18653/v1/2020.emnlp-main.204",
+    pages = "2580--2591",
+}
+```

dataset_infos.json

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+{"hausa_voa_ner": {"description": "The Hausa VOA NER dataset is a labeled dataset for named entity recognition in Hausa. The texts were obtained from\nHausa Voice of America News articles https://www.voahausa.com/ . We concentrate on\nfour types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].\n\nThe Hausa VOA NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and\nthere is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second\nis the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase\nof type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words\nhave tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.\n\nFor more details, see https://www.aclweb.org/anthology/2020.emnlp-main.204/\n", "citation": "@inproceedings{hedderich-etal-2020-transfer,\n    title = \"Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages\",\n    author = \"Hedderich, Michael A.  and\n      Adelani, David  and\n      Zhu, Dawei  and\n      Alabi, Jesujoba  and\n      Markus, Udia  and\n      Klakow, Dietrich\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.emnlp-main.204\",\n    doi = \"10.18653/v1/2020.emnlp-main.204\",\n    pages = \"2580--2591\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.emnlp-main.204/", "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": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hausa_voa_ner", "config_name": "hausa_voa_ner", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 483634, "num_examples": 1015, "dataset_name": "hausa_voa_ner"}, "validation": {"name": "validation", "num_bytes": 69673, "num_examples": 146, "dataset_name": "hausa_voa_ner"}, "test": {"name": "test", "num_bytes": 139227, "num_examples": 292, "dataset_name": "hausa_voa_ner"}}, "download_checksums": {"https://github.com/uds-lsv/transfer-distant-transformer-african/raw/master/data/hausa_ner/train_clean.tsv": {"num_bytes": 226686, "checksum": "ab8cf3e36e6ccba84168c8ddfd148b7abf20f97bb150c19cb579cc667de9b20b"}, "https://github.com/uds-lsv/transfer-distant-transformer-african/raw/master/data/hausa_ner/dev.tsv": {"num_bytes": 33139, "checksum": "f1bf48475498ed6c481840697c8a21d62de6e9044636a296bfe56d15bcd2e044"}, "https://github.com/uds-lsv/transfer-distant-transformer-african/raw/master/data/hausa_ner/test.tsv": {"num_bytes": 65137, "checksum": "139656b6a36946cbe3d19a8dd06689c23429c07fe82d59a9de631b8a0e4e69e3"}}, "download_size": 324962, "post_processing_size": null, "dataset_size": 692534, "size_in_bytes": 1017496}}

dummy/hausa_voa_ner/1.0.0/dummy_data.zip

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+oid sha256:7a3784cf65b687612c09b4740af3b42921a85cb5a1d9d1abacdaba2303ac97b9
+size 569

hausa_voa_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.
+
+"""Introduction to the Yoruba GV NER dataset: A Yoruba Global Voices (News) Named Entity Recognition Dataset"""
+
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{hedderich-etal-2020-transfer,
+    title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
+    author = "Hedderich, Michael A.  and
+      Adelani, David  and
+      Zhu, Dawei  and
+      Alabi, Jesujoba  and
+      Markus, Udia  and
+      Klakow, Dietrich",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
+    doi = "10.18653/v1/2020.emnlp-main.204",
+    pages = "2580--2591",
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+The Hausa VOA NER dataset is a labeled dataset for named entity recognition in Hausa. The texts were obtained from
+Hausa Voice of America News articles https://www.voahausa.com/ . We concentrate on
+four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].
+
+The Hausa VOA NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and
+there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second
+is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase
+of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words
+have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.
+
+For more details, see https://www.aclweb.org/anthology/2020.emnlp-main.204/
+"""
+
+_URL = "https://github.com/uds-lsv/transfer-distant-transformer-african/raw/master/data/hausa_ner/"
+_TRAINING_FILE = "train_clean.tsv"
+_DEV_FILE = "dev.tsv"
+_TEST_FILE = "test.tsv"
+
+
+class HausaVoaNerConfig(datasets.BuilderConfig):
+    """BuilderConfig for HausaVoaNer"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for HausaVoaNer.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(HausaVoaNerConfig, self).__init__(**kwargs)
+
+
+class HausaVoaNer(datasets.GeneratorBasedBuilder):
+    """Hausa VOA NER dataset."""
+
+    BUILDER_CONFIGS = [
+        HausaVoaNerConfig(
+            name="hausa_voa_ner", version=datasets.Version("1.0.0"), description="Hausa VOA NER dataset"
+        ),
+    ]
+
+    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-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-DATE",
+                                "I-DATE",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.aclweb.org/anthology/2020.emnlp-main.204/",
+            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):
+        logging.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                line = line.strip()
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # yoruba_gv_ner tokens are tab separated
+                    splits = line.strip().split("\t")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }