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Datasets:
MaLA-LM
/
mala-monolingual-integration

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Size:
1B - 10B
ArXiv:
Tags:
multilingual
low-resource
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
 Community
2

Improve dataset card with task categories, links, and metadata

#2
by nielsr HF Staff - opened
base: refs/heads/main
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.gitattributes CHANGED
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README.md CHANGED
@@ -1,24 +1,22 @@
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  ---
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  license: odc-by
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  task_categories:
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- - text-generation
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- tags:
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- - multilingual
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- - low-resource
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- project_page: https://mala-lm.github.io/emma-500
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  ---
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  # MaLA Corpus: Massive Language Adaptation Corpus
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  This is the noisy version that integrates texts from different sources.
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  ## Dataset Summary
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  The **MaLA Corpus** (Massive Language Adaptation) is a comprehensive, multilingual dataset designed to support the continual pre-training of large language models. It covers **939 languages** and consists of over **74 billion tokens**, making it one of the largest datasets of its kind. With a focus on improving the representation of low-resource languages, the MaLA Corpus is a critical resource for advancing multilingual models, particularly those aimed at serving underrepresented languages.
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- - Project page: https://mala-lm.github.io/emma-500
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- - Paper: https://arxiv.org/abs/2409.17892
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-
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  ---
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  ## Key Features
@@ -63,18 +61,7 @@ We will comply with legitimate requests by removing the affected sources from th
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  ---
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  ## Citation
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- This dataset is compiled and released in the paper below.
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- ```
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- @article{ji2024emma500enhancingmassivelymultilingual,
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- title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models},
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- author={Shaoxiong Ji and Zihao Li and Indraneil Paul and Jaakko Paavola and Peiqin Lin and Pinzhen Chen and Dayyán O'Brien and Hengyu Luo and Hinrich Schütze and Jörg Tiedemann and Barry Haddow},
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- year={2024},
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- journal={arXiv preprint 2409.17892},
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- url={https://arxiv.org/abs/2409.17892},
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- }
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- ```
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- The final version of this dataset 🤗[MaLA-LM/mala-monolingual-split](https://huggingface.co/datasets/MaLA-LM/mala-monolingual-split) is also used for training the models presented in the below paper
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  ```
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  @article{ji2025emma2,
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  title={Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data},
@@ -89,5 +76,4 @@ The final version of this dataset 🤗[MaLA-LM/mala-monolingual-split](https://h
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  We extend our thanks to the language communities and contributors who helped source, clean, and validate the diverse data used in the MaLA Corpus. Their efforts are invaluable in supporting linguistic diversity in AI research.
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- This work is done by researchers at [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) in collaboration with partners from TU Darmstadt, the University of Edinburgh, and LMU Munich. It is funded by [HPLT](https://hplt-project.org) and [UTTER](https://he-utter.eu).
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-
 
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  ---
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  license: odc-by
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  task_categories:
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+ - text-generation
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+ - translation
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+ language:
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+ - multilingual
 
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  ---
9
 
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  # MaLA Corpus: Massive Language Adaptation Corpus
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  This is the noisy version that integrates texts from different sources.
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+ [Project Page](https://mala-lm.github.io/emma-500-gen2)
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+
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  ## Dataset Summary
17
 
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  The **MaLA Corpus** (Massive Language Adaptation) is a comprehensive, multilingual dataset designed to support the continual pre-training of large language models. It covers **939 languages** and consists of over **74 billion tokens**, making it one of the largest datasets of its kind. With a focus on improving the representation of low-resource languages, the MaLA Corpus is a critical resource for advancing multilingual models, particularly those aimed at serving underrepresented languages.
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  ---
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  ## Key Features
 
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  ---
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  ## Citation
 
 
 
 
 
 
 
 
 
 
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  ```
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  @article{ji2025emma2,
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  title={Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data},
 
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  We extend our thanks to the language communities and contributors who helped source, clean, and validate the diverse data used in the MaLA Corpus. Their efforts are invaluable in supporting linguistic diversity in AI research.
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+ This work is done by researchers at [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) in collaboration with partners from TU Darmstadt, the University of Edinburgh, and LMU Munich. It is funded by [HPLT](https://hplt-project.org) and [UTTER](https://he-utter.eu).
 
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