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Update parquet files
Browse files- README.md +0 -130
- amharic/eval.zip → afaanoromoo/afriberta-corpus-test.parquet +2 -2
- afaanoromoo/{train.zip → afriberta-corpus-train.parquet} +2 -2
- afriberta-corpus.py +0 -113
- afaanoromoo/eval.zip → amharic/afriberta-corpus-test.parquet +2 -2
- amharic/afriberta-corpus-train.parquet +3 -0
- dataset_infos.json +0 -1
- gahuza/{eval.zip → afriberta-corpus-test.parquet} +2 -2
- amharic/train.zip → gahuza/afriberta-corpus-train.parquet +2 -2
- gahuza/train.zip → hausa/afriberta-corpus-test.parquet +2 -2
- hausa/afriberta-corpus-train.parquet +3 -0
- hausa/eval.zip +0 -3
- hausa/train.zip +0 -3
- igbo/afriberta-corpus-test.parquet +3 -0
- igbo/afriberta-corpus-train.parquet +3 -0
- igbo/eval.zip +0 -3
- igbo/train.zip +0 -3
- pidgin/afriberta-corpus-test.parquet +3 -0
- pidgin/afriberta-corpus-train.parquet +3 -0
- pidgin/eval.zip +0 -3
- pidgin/train.zip +0 -3
- somali/afriberta-corpus-test.parquet +3 -0
- somali/afriberta-corpus-train.parquet +3 -0
- somali/eval.zip +0 -3
- somali/train.zip +0 -3
- swahili/afriberta-corpus-test.parquet +3 -0
- swahili/afriberta-corpus-train.parquet +3 -0
- swahili/eval.zip +0 -3
- swahili/train.zip +0 -3
- tigrinya/afriberta-corpus-test.parquet +3 -0
- tigrinya/afriberta-corpus-train.parquet +3 -0
- tigrinya/eval.zip +0 -3
- tigrinya/train.zip +0 -3
- yoruba/afriberta-corpus-test.parquet +3 -0
- yoruba/afriberta-corpus-train.parquet +3 -0
- yoruba/eval.zip +0 -3
- yoruba/train.zip +0 -3
README.md
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---
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language:
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- om
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- am
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- rw
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- rn
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- ha
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- ig
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- pcm
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- so
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- sw
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- ti
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- yo
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- multilingual
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license: apache-2.0
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task_categories:
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- text-generation
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task_ids:
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- language-modeling
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---
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# Dataset Card for AfriBERTa's Corpus
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Loading Dataset](#loading-dataset)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Discussion of Biases](#discussion-of-biases)
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- [Additional Information](#additional-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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### Dataset Summary
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This is the corpus on which AfriBERTa was trained on.
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The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl.
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- **Homepage:** https://github.com/keleog/afriberta
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- **Models:**
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- https://huggingface.co/castorini/afriberta_small
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- https://huggingface.co/castorini/afriberta_base
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- https://huggingface.co/castorini/afriberta_large
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- **Paper:** https://aclanthology.org/2021.mrl-1.11/
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- **Point of Contact:** kelechi.ogueji@uwaterloo.ca
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### Supported Tasks and Leaderboards
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The AfriBERTa corpus was mostly intended to pre-train language models.
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### Languages
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```
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afaanoromoo
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amharic
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gahuza
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hausa
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igbo
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pidgin
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somali
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swahili
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tigrinya
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yoruba
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```
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### Loading Dataset
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An example to load the train split of the Somali corpus:
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```
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dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train")
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```
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An example to load the test split of the Pidgin corpus:
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```
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dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test")
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```
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## Dataset Structure
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### Data Instances
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Each data point is a line of text.
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An example from the `igbo` dataset:
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```
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{"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."}
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```
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### Data Fields
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The data fields are:
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- id: id of the example
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- text: content as a string
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### Data Splits
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Each language has a train and test split, with varying sizes.
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## Considerations for Using the Data
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### Discussion of Biases
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Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to
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be biased towards the news domain.
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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.
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## Additional Information
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### Citation Information
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```
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@inproceedings{ogueji-etal-2021-small,
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title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
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author = "Ogueji, Kelechi and
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Zhu, Yuxin and
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Lin, Jimmy",
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booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
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month = nov,
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year = "2021",
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address = "Punta Cana, Dominican Republic",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.mrl-1.11",
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pages = "116--126",
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}
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```
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### Contributions
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Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset.
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amharic/eval.zip → afaanoromoo/afriberta-corpus-test.parquet
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size 2849238
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afaanoromoo/{train.zip → afriberta-corpus-train.parquet}
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size 38946179
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afriberta-corpus.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the 'License');
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an 'AS IS' BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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import os
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import datasets
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_DESCRIPTION = """\
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Corpus used for training AfriBERTa models
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"""
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_CITATION = """\
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@inproceedings{ogueji-etal-2021-small,
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title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
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author = "Ogueji, Kelechi and
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Zhu, Yuxin and
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Lin, Jimmy",
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booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
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month = nov,
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year = "2021",
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address = "Punta Cana, Dominican Republic",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.mrl-1.11",
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pages = "116--126",
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}
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"""
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_HOMEPAGE_URL = "https://github.com/keleog/afriberta"
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_VERSION = "1.0.0"
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_LANGUAGES = [
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"afaanoromoo",
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"amharic",
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"gahuza",
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"hausa",
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"igbo",
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"pidgin",
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"somali",
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"swahili",
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"tigrinya",
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"yoruba"]
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_DATASET_URLS = {
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language: {
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"train": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/train.zip",
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"test": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/eval.zip",
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} for language in _LANGUAGES
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}
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class AfribertaCorpus(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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version=datasets.Version(_VERSION),
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name=language,
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description=f"AfriBERTa corpus for {language}."
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) for language in _LANGUAGES
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"text": datasets.Value("string"),
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},
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),
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supervised_keys=None,
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homepage=_HOMEPAGE_URL,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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language = self.config.name
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downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[language])
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splits = [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"file_path": os.path.join(downloaded_files["train"], "train.txt"),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"file_path": os.path.join(downloaded_files["test"], "eval.txt"),
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},
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),
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]
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return splits
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def _generate_examples(self, file_path):
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with open(file_path, encoding="utf-8") as f:
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for sentence_counter, line in enumerate(f):
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result = (
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sentence_counter,
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{
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"id": str(sentence_counter),
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"text": line,
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},
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)
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yield result
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afaanoromoo/eval.zip → amharic/afriberta-corpus-test.parquet
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size 7351975
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amharic/afriberta-corpus-train.parquet
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version https://git-lfs.github.com/spec/v1
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dataset_infos.json
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{"afaanoromoo": {"description": "Corpus used for training AfriBERTa models\n", "citation": "@inproceedings{ogueji-etal-2021-small,\n title = \"Small Data? No Problem! 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