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README.md

@@ -1,130 +0,0 @@
----
-language: 
-- om
-- am
-- rw
-- rn
-- ha
-- ig
-- pcm
-- so
-- sw
-- ti
-- yo
-- multilingual
-    
-license: apache-2.0
-
-task_categories:
-- text-generation
-task_ids:
-- language-modeling
----
-
-# Dataset Card for AfriBERTa's Corpus
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-  - [Loading Dataset](#loading-dataset)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Discussion of Biases](#discussion-of-biases)
-- [Additional Information](#additional-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-### Dataset Summary
-This is the corpus on which AfriBERTa was trained on.
-The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl. 
-
-- **Homepage:** https://github.com/keleog/afriberta
-- **Models:** 
-    - https://huggingface.co/castorini/afriberta_small
-    - https://huggingface.co/castorini/afriberta_base
-    - https://huggingface.co/castorini/afriberta_large
-- **Paper:** https://aclanthology.org/2021.mrl-1.11/
-- **Point of Contact:** kelechi.ogueji@uwaterloo.ca
-
-
-### Supported Tasks and Leaderboards
-The AfriBERTa corpus was mostly intended to pre-train language models.
-
-### Languages
-```
-afaanoromoo
-amharic
-gahuza
-hausa
-igbo
-pidgin
-somali
-swahili
-tigrinya
-yoruba
-```
-### Loading Dataset
-An example to load the train split of the Somali corpus:
-```
-dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train")
-```
-
-An example to load the test split of the Pidgin corpus:
-```
-dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test")
-```
-
-## Dataset Structure
-
-### Data Instances
-Each data point is a line of text. 
-An example from the `igbo` dataset:
-```
-{"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."}
-```
-
-### Data Fields
-
-The data fields are:
-
-- id: id of the example
-- text: content as a string
-
-### Data Splits
-Each language has a train and test split, with varying sizes. 
-
-## Considerations for Using the Data
-
-### Discussion of Biases
-Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to 
-be biased towards the news domain.
-
-Also, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.
-
-## Additional Information
-### Citation Information
-```
-@inproceedings{ogueji-etal-2021-small,
-    title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
-    author = "Ogueji, Kelechi  and
-      Zhu, Yuxin  and
-      Lin, Jimmy",
-    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
-    month = nov,
-    year = "2021",
-    address = "Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.mrl-1.11",
-    pages = "116--126",
-}
-```
-
-### Contributions
-Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset.

amharic/eval.zip → afaanoromoo/afriberta-corpus-test.parquet

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afaanoromoo/{train.zip → afriberta-corpus-train.parquet}

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afriberta-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.
-
-# Lint as: python3
-
-import os
-import datasets
-
-_DESCRIPTION = """\
-Corpus used for training AfriBERTa models
-"""
-_CITATION = """\
-@inproceedings{ogueji-etal-2021-small,
-    title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
-    author = "Ogueji, Kelechi  and
-      Zhu, Yuxin  and
-      Lin, Jimmy",
-    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
-    month = nov,
-    year = "2021",
-    address = "Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.mrl-1.11",
-    pages = "116--126",
-}
-"""
-_HOMEPAGE_URL = "https://github.com/keleog/afriberta"
-_VERSION = "1.0.0"
-_LANGUAGES = [
-    "afaanoromoo", 
-    "amharic", 
-    "gahuza", 
-    "hausa", 
-    "igbo", 
-    "pidgin", 
-    "somali", 
-    "swahili", 
-    "tigrinya", 
-    "yoruba"]
-
-_DATASET_URLS = {
-    language: {
-        "train": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/train.zip",
-        "test": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/eval.zip",
-    } for language in _LANGUAGES
-}
-
-class AfribertaCorpus(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            version=datasets.Version(_VERSION),
-            name=language,
-            description=f"AfriBERTa corpus for {language}."
-        ) for language in _LANGUAGES
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                },
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE_URL,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        language = self.config.name
-        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[language])
-
-        splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file_path": os.path.join(downloaded_files["train"], "train.txt"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file_path": os.path.join(downloaded_files["test"], "eval.txt"),
-                },
-            ),
-        ]
-        return splits
-
-    def _generate_examples(self, file_path):
-        with open(file_path, encoding="utf-8") as f:
-            for sentence_counter, line in enumerate(f):
-                result = (
-                    sentence_counter,
-                    {
-                        "id": str(sentence_counter),
-                        "text": line,
-                    },
-                )
-                yield result

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