Duplicate from masakhane/mafand

Co-authored-by: David Adelani <Davlan@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow 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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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+- fr
+- am
+- bm
+- bbj
+- ee
+- fon
+- ha
+- ig
+- lg
+- mos
+- ny
+- pcm
+- rw
+- sn
+- sw
+- tn
+- tw
+- wo
+- xh
+- yo
+- zu
+language_creators:
+- expert-generated
+license:
+- cc-by-nc-4.0
+multilinguality:
+- translation
+- multilingual
+pretty_name: mafand
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- news, mafand, masakhane
+task_categories:
+- translation
+task_ids: []
+---
+
+# Dataset Card for MAFAND
+
+## 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://github.com/masakhane-io/lafand-mt
+- **Repository:** https://github.com/masakhane-io/lafand-mt
+- **Paper:** https://aclanthology.org/2022.naacl-main.223/
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [David Adelani](https://dadelani.github.io/)
+
+### Dataset Summary
+
+MAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages. 
+
+### Supported Tasks and Leaderboards
+
+Machine Translation
+
+### Languages
+
+The languages covered are: 
+- Amharic
+- Bambara
+- Ghomala
+- Ewe
+- Fon
+- Hausa
+- Igbo
+- Kinyarwanda
+- Luganda
+- Luo
+- Mossi
+- Nigerian-Pidgin
+- Chichewa
+- Shona
+- Swahili
+- Setswana
+- Twi
+- Wolof
+- Xhosa
+- Yoruba
+- Zulu
+
+## Dataset Structure
+
+### Data Instances
+```
+>>> from datasets import load_dataset
+>>> data = load_dataset('masakhane/mafand', 'en-yor')
+
+{"translation": {"src": "President Buhari will determine when to lift lockdown – Minister", "tgt": "Ààrẹ Buhari ló lè yóhùn padà lórí ètò kónílégbélé – Mínísítà"}}
+
+
+{"translation": {"en": "President Buhari will determine when to lift lockdown – Minister", "yo": "Ààrẹ Buhari ló lè yóhùn padà lórí ètò kónílégbélé – Mínísítà"}}
+```
+
+### Data Fields
+
+- "translation":  name of the task
+- "src" : source language e.g en
+- "tgt":  target language e.g yo
+
+### Data Splits
+
+Train/dev/test split
+
+language| Train| Dev |Test
+-|-|-|-
+amh |-|899|1037
+bam |3302|1484|1600
+bbj |2232|1133|1430
+ewe |2026|1414|1563
+fon |2637|1227|1579
+hau |5865|1300|1500
+ibo |6998|1500|1500
+kin |-|460|1006
+lug |4075|1500|1500
+luo |4262|1500|1500
+mos |2287|1478|1574
+nya |-|483|1004
+pcm |4790|1484|1574
+sna |-|556|1005
+swa |30782|1791|1835
+tsn |2100|1340|1835
+twi |3337|1284|1500
+wol |3360|1506|1500|
+xho |-|486|1002|
+yor |6644|1544|1558|
+zul |3500|1239|998|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+MAFAND was created from the news domain, translated from English or French to an African language
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+- [Masakhane](https://github.com/masakhane-io/lafand-mt)
+- [Igbo](https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_en_mt)
+- [Swahili](https://opus.nlpl.eu/GlobalVoices.php)
+- [Hausa](https://www.statmt.org/wmt21/translation-task.html)
+- [Yoruba](https://github.com/uds-lsv/menyo-20k_MT)
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+Masakhane members
+
+### 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
+
+[Needs More Information]
+
+### Licensing Information
+
+[CC-BY-4.0-NC](https://creativecommons.org/licenses/by-nc/4.0/)
+
+### Citation Information
+
+```
+@inproceedings{adelani-etal-2022-thousand,
+    title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
+    author = "Adelani, David  and
+      Alabi, Jesujoba  and
+      Fan, Angela  and
+      Kreutzer, Julia  and
+      Shen, Xiaoyu  and
+      Reid, Machel  and
+      Ruiter, Dana  and
+      Klakow, Dietrich  and
+      Nabende, Peter  and
+      Chang, Ernie  and
+      Gwadabe, Tajuddeen  and
+      Sackey, Freshia  and
+      Dossou, Bonaventure F. P.  and
+      Emezue, Chris  and
+      Leong, Colin  and
+      Beukman, Michael  and
+      Muhammad, Shamsuddeen  and
+      Jarso, Guyo  and
+      Yousuf, Oreen  and
+      Niyongabo Rubungo, Andre  and
+      Hacheme, Gilles  and
+      Wairagala, Eric Peter  and
+      Nasir, Muhammad Umair  and
+      Ajibade, Benjamin  and
+      Ajayi, Tunde  and
+      Gitau, Yvonne  and
+      Abbott, Jade  and
+      Ahmed, Mohamed  and
+      Ochieng, Millicent  and
+      Aremu, Anuoluwapo  and
+      Ogayo, Perez  and
+      Mukiibi, Jonathan  and
+      Ouoba Kabore, Fatoumata  and
+      Kalipe, Godson  and
+      Mbaye, Derguene  and
+      Tapo, Allahsera Auguste  and
+      Memdjokam Koagne, Victoire  and
+      Munkoh-Buabeng, Edwin  and
+      Wagner, Valencia  and
+      Abdulmumin, Idris  and
+      Awokoya, Ayodele  and
+      Buzaaba, Happy  and
+      Sibanda, Blessing  and
+      Bukula, Andiswa  and
+      Manthalu, Sam",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.223",
+    doi = "10.18653/v1/2022.naacl-main.223",
+    pages = "3053--3070",
+    abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
+}
+```

mafand.py

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+# 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
+"""MAFAND-MT: Masakhane Anglo and Franco Africa News Dataset for Machine Translation"""
+
+import datasets
+import json
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@inproceedings{adelani-etal-2022-thousand,
+    title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
+    author = "Adelani, David  and
+      Alabi, Jesujoba  and
+      Fan, Angela  and
+      Kreutzer, Julia  and
+      Shen, Xiaoyu  and
+      Reid, Machel  and
+      Ruiter, Dana  and
+      Klakow, Dietrich  and
+      Nabende, Peter  and
+      Chang, Ernie  and
+      Gwadabe, Tajuddeen  and
+      Sackey, Freshia  and
+      Dossou, Bonaventure F. P.  and
+      Emezue, Chris  and
+      Leong, Colin  and
+      Beukman, Michael  and
+      Muhammad, Shamsuddeen  and
+      Jarso, Guyo  and
+      Yousuf, Oreen  and
+      Niyongabo Rubungo, Andre  and
+      Hacheme, Gilles  and
+      Wairagala, Eric Peter  and
+      Nasir, Muhammad Umair  and
+      Ajibade, Benjamin  and
+      Ajayi, Tunde  and
+      Gitau, Yvonne  and
+      Abbott, Jade  and
+      Ahmed, Mohamed  and
+      Ochieng, Millicent  and
+      Aremu, Anuoluwapo  and
+      Ogayo, Perez  and
+      Mukiibi, Jonathan  and
+      Ouoba Kabore, Fatoumata  and
+      Kalipe, Godson  and
+      Mbaye, Derguene  and
+      Tapo, Allahsera Auguste  and
+      Memdjokam Koagne, Victoire  and
+      Munkoh-Buabeng, Edwin  and
+      Wagner, Valencia  and
+      Abdulmumin, Idris  and
+      Awokoya, Ayodele  and
+      Buzaaba, Happy  and
+      Sibanda, Blessing  and
+      Bukula, Andiswa  and
+      Manthalu, Sam",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.223",
+    doi = "10.18653/v1/2022.naacl-main.223",
+    pages = "3053--3070",
+    abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
+}
+"""
+
+_DESCRIPTION = """\
+MAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages. The languages covered are:
+- Amharic
+- Bambara
+- Ghomala
+- Ewe
+- Fon
+- Hausa
+- Igbo
+- Kinyarwanda
+- Luganda
+- Luo
+- Mossi
+- Nigerian-Pidgin
+- Chichewa
+- Shona
+- Swahili
+- Setswana
+- Twi
+- Wolof
+- Xhosa
+- Yoruba
+- Zulu
+
+The train/validation/test sets are available for 16 languages, and validation/test set for amh, kin, nya, sna, and xho
+
+For more details see https://aclanthology.org/2022.naacl-main.223/
+"""
+
+_URL = "https://raw.githubusercontent.com/masakhane-io/lafand-mt/main/data/json_files/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "dev.json"
+_TEST_FILE = "test.json"
+
+
+class MafandConfig(datasets.BuilderConfig):
+    """BuilderConfig for Mafand"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Masakhaner.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MafandConfig, self).__init__(**kwargs)
+
+
+class Mafand(datasets.GeneratorBasedBuilder):
+    """Mafand dataset."""
+    BUILDER_CONFIGS = [
+        MafandConfig(name="en-amh", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Amharic dataset"),
+        MafandConfig(name="en-hau", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Hausa dataset"),
+        MafandConfig(name="en-ibo", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Igbo dataset"),
+        MafandConfig(name="en-kin", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Kinyarwanda dataset"),
+        MafandConfig(name="en-lug", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Luganda dataset"),
+        MafandConfig(name="en-luo", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Luo dataset"),
+        MafandConfig(name="en-nya", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Chichewa dataset"),
+        MafandConfig(name="en-pcm", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Naija dataset"),
+        MafandConfig(name="en-sna", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Shona dataset"),
+        MafandConfig(name="en-swa", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Swahili dataset"),
+        MafandConfig(name="en-tsn", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Setswana dataset"),
+        MafandConfig(name="en-twi", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Twi dataset"),
+        MafandConfig(name="en-xho", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Xhosa dataset"),
+        MafandConfig(name="en-yor", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Yoruba dataset"),
+        MafandConfig(name="en-zul", version=datasets.Version("1.0.0"),
+                     description="Mafand English-Zulu dataset"),
+        MafandConfig(name="fr-bam", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Bambara dataset"),
+        MafandConfig(name="fr-bbj", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Ghomala dataset"),
+        MafandConfig(name="fr-ewe", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Ewe dataset"),
+        MafandConfig(name="fr-fon", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Fon dataset"),
+        MafandConfig(name="fr-mos", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Mossi dataset"),
+        MafandConfig(name="fr-wol", version=datasets.Version("1.0.0"),
+                     description="Mafand French-Wolof dataset"),
+    ]
+
+    def _info(self):
+        source, target = self.config.name.split('-')
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"translation": datasets.features.Translation(languages=(source, target))}),
+            supervised_keys=(source, target),
+            homepage="https://github.com/masakhane-io/lafand-mt",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        source, target = self.config.name.split('-')
+        if target in ['amh', 'kin', 'nya', 'sna', 'xho']:
+            urls_to_download = {
+                "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.VALIDATION,
+                                        gen_kwargs={"filepath": downloaded_files["dev"]}),
+                datasets.SplitGenerator(name=datasets.Split.TEST,
+                                        gen_kwargs={"filepath": downloaded_files["test"]}),
+            ]
+        else:
+            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:
+            idx = 0
+            for line in f:
+                src_tgt = json.loads(line)
+                yield idx, src_tgt
+                idx += 1