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README.md

@@ -1,3 +1,263 @@
 ---
-license: mit
+annotations_creators:
+- expert-generated
+language:
+- bm
+- bbj
+- ee
+- fon
+- ha
+- ig
+- rw
+- lg
+- luo
+- mos
+- ny
+- pcm
+- sn
+- sw
+- tn
+- tw
+- wo
+- xh
+- yo
+- zu
+language_creators:
+- expert-generated
+license:
+- afl-3.0
+multilinguality:
+- multilingual
+pretty_name: masakhapos
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- pos
+- masakhapos
+- masakhane
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+
 ---
+
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [homepage](https://github.com/masakhane-io/masakhane-pos/)
+- **Repository:** [github](https://github.com/masakhane-io/masakhane-pos/)
+- **Paper:** [paper](https://aclanthology.org/2023.acl-long.609/)
+- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or [email protected]
+
+### Dataset Summary
+
+MasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are: 
+
+
+The train/validation/test sets are available for all the 20 languages.
+
+For more details see https://aclanthology.org/2023.acl-long.609/
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+- `Part-of-speech`: The performance in this task is measured with [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) (higher is better). 
+### Languages
+
+There are 20 languages available :
+- Bambara (bam)
+- Ghomala (bbj)
+- Ewe (ewe)
+- Fon (fon)
+- Hausa (hau)
+- Igbo (ibo)
+- Kinyarwanda (kin)
+- Luganda (lug)
+- Dholuo (luo) 
+- Mossi (mos)
+- Chichewa (nya)
+- Nigerian Pidgin
+- chShona (sna)
+- Kiswahili (swą)
+- Setswana (tsn)
+- Twi (twi)
+- Wolof (wol)
+- isiXhosa (xho)
+- Yorùbá (yor)
+- isiZulu (zul)
+
+## Dataset Structure
+
+### Data Instances
+
+The examples look like this for Yorùbá:
+
+```
+from datasets import load_dataset
+data = load_dataset('masakhane/masakhapos', 'yor') 
+
+# Please, specify the language code
+
+# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
+{'id': '0',
+ 'ner_tags':  [0, 10, 10, 16, 0, 14, 0, 16, 0],
+ 'tokens': ['Ọ̀gbẹ́ni', 'Nuhu', 'Adam', 'kúrò', 'nípò', 'bí', 'ẹní', 'yọ', 'jìgá']
+}
+```
+
+### Data Fields
+
+- `id`: id of the sample
+- `tokens`: the tokens of the example text
+- `upos`: the POS tags of each token
+
+The POS tags correspond to this list:
+```
+"NOUN", "PUNCT", "ADP", "NUM", "SYM", "SCONJ", "ADJ", "PART", "DET", "CCONJ", "PROPN", "PRON", "X", "ADV", "INTJ", "VERB", "AUX",```
+              
+The definition  of the tags can be found on [UD website](https://universaldependencies.org/u/pos/)
+
+### Data Splits
+
+For all languages, there are three splits.
+
+The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
+
+The splits have the following sizes :
+
+| Language        | train | validation | test  |
+|-----------------|------:|-----------:|------:|
+| Bambara         |  775  |        154 |  619  |
+| Ghomala         |  750  |        149 |  599  |
+| Ewe             |  728  |        145 |  582  |
+| Fon             |  810  |        161 |  646  |
+| Hausa           |  753  |        150 |  601  |
+| Igbo            |  803  |        160 |  642  |
+| Kinyarwanda     |  757  |        151 |  604  |
+| Luganda         |  733  |        146 |  586  |
+| Luo             |  758  |        151 |  606  |
+| Mossi           |  757  |        151 |  604  |
+| Chichewa        |  728  |        145 |  582  |
+| Nigerian-Pidgin |  752  |        150 |  600  |
+| chiShona        |  747  |        149 |  596  |
+| Kiswahili       |  693  |        138 |  553  |
+| Setswana        |  754  |        150 |  602  |
+| Akan/Twi        |  785  |        157 |  628  |
+| Wolof           |  782  |        156 |  625  |
+| isiXhosa        |  752  |        150 |  601  |
+| Yoruba          |  893  |        178 |  713  |
+| isiZulu         |  753  |        150 |  601  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.
+
+[More Information Needed]
+
+### Source Data
+
+The source of the data is from the news domain, details can be found here https://aclanthology.org/2023.acl-long.609/
+#### Initial Data Collection and Normalization
+
+The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
+
+#### Who are the source language producers?
+
+The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
+
+### Annotations
+
+#### Annotation process
+
+Details can be found here https://aclanthology.org/2023.acl-long.609/
+
+#### Who are the annotators?
+
+Annotators were recruited from [Masakhane](https://www.masakhane.io/)
+
+### Personal and Sensitive Information
+
+The data is sourced from newspaper source and only contains mentions of public figures or individuals
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+[More Information Needed]
+
+
+### Discussion of Biases
+[More Information Needed]
+
+
+### Other Known Limitations
+
+Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
+
+## Additional Information
+
+### Dataset Curators
+
+
+### Licensing Information
+
+The licensing status of the data is CC 4.0 Non-Commercial
+
+### Citation Information
+
+Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
+```
+@inproceedings{dione-etal-2023-masakhapos,
+    title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
+    author = "Dione, Cheikh M. Bamba  and Adelani, David Ifeoluwa  and Nabende, Peter  and Alabi, Jesujoba  and Sindane, Thapelo  and Buzaaba, Happy  and Muhammad, Shamsuddeen Hassan  and Emezue, Chris Chinenye  and Ogayo, Perez  and Aremu, Anuoluwapo  and Gitau, Catherine  and Mbaye, Derguene  and Mukiibi, Jonathan  and Sibanda, Blessing  and Dossou, Bonaventure F. P.  and Bukula, Andiswa  and Mabuya, Rooweither  and Tapo, Allahsera Auguste  and Munkoh-Buabeng, Edwin  and Memdjokam Koagne, Victoire  and Ouoba Kabore, Fatoumata  and Taylor, Amelia  and Kalipe, Godson  and Macucwa, Tebogo  and Marivate, Vukosi  and Gwadabe, Tajuddeen  and Elvis, Mboning Tchiaze  and Onyenwe, Ikechukwu  and Atindogbe, Gratien  and Adelani, Tolulope  and Akinade, Idris  and Samuel, Olanrewaju  and Nahimana, Marien  and Musabeyezu, Th{\'e}og{\`e}ne  and Niyomutabazi, Emile  and Chimhenga, Ester  and Gotosa, Kudzai  and Mizha, Patrick  and Agbolo, Apelete  and Traore, Seydou  and Uchechukwu, Chinedu  and Yusuf, Aliyu  and Abdullahi, Muhammad  and Klakow, Dietrich",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.609",
+    doi = "10.18653/v1/2023.acl-long.609",
+    pages = "10883--10900",
+    abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.",
+}
+```
+
+### Contributions
+
+Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.

masakhapos.py

@@ -0,0 +1,203 @@
+# 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
+"""MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages"""
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{dione-etal-2023-masakhapos,
+    title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
+    author = "Dione, Cheikh M. Bamba  and Adelani, David Ifeoluwa  and Nabende, Peter  and Alabi, Jesujoba  and Sindane, Thapelo  and Buzaaba, Happy  and Muhammad, Shamsuddeen Hassan  and Emezue, Chris Chinenye  and Ogayo, Perez  and Aremu, Anuoluwapo  and Gitau, Catherine  and Mbaye, Derguene  and Mukiibi, Jonathan  and Sibanda, Blessing  and Dossou, Bonaventure F. P.  and Bukula, Andiswa  and Mabuya, Rooweither  and Tapo, Allahsera Auguste  and Munkoh-Buabeng, Edwin  and Memdjokam Koagne, Victoire  and Ouoba Kabore, Fatoumata  and Taylor, Amelia  and Kalipe, Godson  and Macucwa, Tebogo  and Marivate, Vukosi  and Gwadabe, Tajuddeen  and Elvis, Mboning Tchiaze  and Onyenwe, Ikechukwu  and Atindogbe, Gratien  and Adelani, Tolulope  and Akinade, Idris  and Samuel, Olanrewaju  and Nahimana, Marien  and Musabeyezu, Th{\'e}og{\`e}ne  and Niyomutabazi, Emile  and Chimhenga, Ester  and Gotosa, Kudzai  and Mizha, Patrick  and Agbolo, Apelete  and Traore, Seydou  and Uchechukwu, Chinedu  and Yusuf, Aliyu  and Abdullahi, Muhammad  and Klakow, Dietrich",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.609",
+    doi = "10.18653/v1/2023.acl-long.609",
+    pages = "10883--10900",
+    abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.",
+}
+"""
+
+_DESCRIPTION = """\
+MasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are: 
+- Bambara (bam)
+- Ghomala (bbj)
+- Ewe (ewe)
+- Fon (fon)
+- Hausa (hau)
+- Igbo (ibo)
+- Kinyarwanda (kin)
+- Luganda (lug)
+- Dholuo (luo) 
+- Mossi (mos)
+- Chichewa (nya)
+- Nigerian Pidgin
+- chShona (sna)
+- Kiswahili (swą)
+- Setswana (tsn)
+- Twi (twi)
+- Wolof (wol)
+- isiXhosa (xho)
+- Yorùbá (yor)
+- isiZulu (zul)
+
+The train/validation/test sets are available for all the ten languages.
+
+For more details see https://aclanthology.org/2023.acl-long.609/
+"""
+_URL = "https://github.com/masakhane-io/masakhane-pos/raw/main/data/"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "dev.txt"
+_TEST_FILE = "test.txt"
+
+
+class MasakhaposConfig(datasets.BuilderConfig):
+    """BuilderConfig for MasakhaposConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for MasakhaposConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MasakhaposConfig, self).__init__(**kwargs)
+
+
+class Masakhapos(datasets.GeneratorBasedBuilder):
+    """Masakhapos dataset."""
+
+    BUILDER_CONFIGS = [
+        MasakhaposConfig(name="bam", version=datasets.Version("1.0.0"), description="Masakhapos Bambara dataset"),
+        MasakhaposConfig(name="bbj", version=datasets.Version("1.0.0"), description="Masakhapos Ghomala dataset"),
+        MasakhaposConfig(name="ewe", version=datasets.Version("1.0.0"), description="Masakhapos Ewe dataset"),
+        MasakhaposConfig(name="fon", version=datasets.Version("1.0.0"), description="Masakhapos Fon dataset"),
+        MasakhaposConfig(name="hau", version=datasets.Version("1.0.0"), description="Masakhapos Hausa dataset"),
+        MasakhaposConfig(name="ibo", version=datasets.Version("1.0.0"), description="Masakhapos Igbo dataset"),
+        MasakhaposConfig(name="kin", version=datasets.Version("1.0.0"), description="Masakhapos Kinyarwanda dataset"),
+        MasakhaposConfig(name="lug", version=datasets.Version("1.0.0"), description="Masakhapos Luganda dataset"),
+        MasakhaposConfig(name="luo", version=datasets.Version("1.0.0"), description="Masakhapos Luo dataset"),
+        MasakhaposConfig(name="mos", version=datasets.Version("1.0.0"), description="Masakhapos Mossi dataset"),
+        MasakhaposConfig(name="nya", version=datasets.Version("1.0.0"), description="Masakhapos Chichewa` dataset"),
+        MasakhaposConfig(
+            name="pcm", version=datasets.Version("1.0.0"), description="Masakhapos Nigerian-Pidgin dataset"
+        ),
+        MasakhaposConfig(name="sna", version=datasets.Version("1.0.0"), description="Masakhapos Shona dataset"),
+        MasakhaposConfig(name="swa", version=datasets.Version("1.0.0"), description="Masakhapos Swahili dataset"),
+        MasakhaposConfig(name="tsn", version=datasets.Version("1.0.0"), description="Masakhapos Setswana dataset"),
+        MasakhaposConfig(name="twi", version=datasets.Version("1.0.0"), description="Masakhapos Twi dataset"),
+        MasakhaposConfig(name="wol", version=datasets.Version("1.0.0"), description="Masakhapos Wolof dataset"),
+        MasakhaposConfig(name="xho", version=datasets.Version("1.0.0"), description="Masakhapos Xhosa dataset"),
+        MasakhaposConfig(name="yor", version=datasets.Version("1.0.0"), description="Masakhapos Yoruba dataset"),
+        MasakhaposConfig(name="zul", version=datasets.Version("1.0.0"), description="Masakhapos Zulu dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "upos": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "NOUN",
+                                "PUNCT",
+                                "ADP",
+                                "NUM",
+                                "SYM",
+                                "SCONJ",
+                                "ADJ",
+                                "PART",
+                                "DET",
+                                "CCONJ",
+                                "PROPN",
+                                "PRON",
+                                "X",
+                                "_",
+                                "ADV",
+                                "INTJ",
+                                "VERB",
+                                "AUX",
+                            ]
+
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://aclanthology.org/2023.acl-long.609/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{self.config.name}/{_TRAINING_FILE}",
+            "dev": f"{_URL}{self.config.name}/{_DEV_FILE}",
+            "test": f"{_URL}{self.config.name}/{_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):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            pos_tags = []
+            for line in f:
+                if line.startswith('-DOCSTART-'):
+                    continue
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "upos": pos_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                else:
+                    # Masakhapos tokens are space separated
+                    splits = line.strip().split()
+                    tokens.append(splits[0])
+                    pos_tag = splits[-1]
+                    pos_tags.append(pos_tag)
+            # last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    "upos": pos_tags,
+                }
+