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Librarian Bot: Add language metadata for dataset

#1
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  1. .gitattributes +0 -1
  2. README.md +3 -32
  3. abk_Cyrl/.gitattributes +3 -1
  4. abk_Cyrl/train.jsonl +3 -0
  5. abk_Cyrl/train.parquet +0 -3
  6. abk_Cyrl/validation.jsonl +3 -0
  7. abk_Cyrl/validation.parquet +0 -3
  8. abt_Latn/.gitattributes +3 -1
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  10. abt_Latn/train.parquet +0 -3
  11. abt_Latn/validation.jsonl +3 -0
  12. abt_Latn/validation.parquet +0 -3
  13. ace_Latn/.gitattributes +3 -1
  14. ace_Latn/train.jsonl +3 -0
  15. ace_Latn/train.parquet +0 -3
  16. ace_Latn/validation.jsonl +3 -0
  17. ace_Latn/validation.parquet +0 -3
  18. acf_Latn/.gitattributes +3 -1
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  20. acf_Latn/train.parquet +0 -3
  21. acf_Latn/validation.jsonl +3 -0
  22. acf_Latn/validation.parquet +0 -3
  23. ach_Latn/.gitattributes +3 -1
  24. ach_Latn/train.jsonl +3 -0
  25. ach_Latn/train.parquet +0 -3
  26. ach_Latn/validation.jsonl +3 -0
  27. ach_Latn/validation.parquet +0 -3
  28. acm_Arab/.gitattributes +3 -1
  29. acm_Arab/train.jsonl +3 -0
  30. acm_Arab/train.parquet +0 -3
  31. acm_Arab/validation.jsonl +3 -0
  32. acm_Arab/validation.parquet +0 -3
  33. acr_Latn/.gitattributes +3 -1
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  35. acr_Latn/train.parquet +0 -3
  36. acr_Latn/validation.jsonl +3 -0
  37. acr_Latn/validation.parquet +0 -3
  38. ada_Latn/.gitattributes +3 -1
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  40. ada_Latn/train.parquet +0 -3
  41. ada_Latn/validation.jsonl +3 -0
  42. ada_Latn/validation.parquet +0 -3
  43. ady_Cyrl/.gitattributes +3 -1
  44. ady_Cyrl/train.jsonl +3 -0
  45. ady_Cyrl/train.parquet +0 -3
  46. ady_Cyrl/validation.jsonl +3 -0
  47. ady_Cyrl/validation.parquet +0 -3
  48. afb_Arab/.gitattributes +3 -1
  49. afb_Arab/train.jsonl +3 -0
  50. afb_Arab/train.parquet +0 -3
.gitattributes CHANGED
@@ -56,4 +56,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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- jpn_Jpan/train.jsonl filter=lfs diff=lfs merge=lfs -text
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
README.md CHANGED
@@ -1,11 +1,7 @@
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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
@@ -16,9 +12,6 @@ This version contains train and validation splits.
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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
@@ -63,31 +56,9 @@ We don't own any part of the data. The original source of each data point is ind
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  We will comply with legitimate requests by removing the affected sources from the corpora.
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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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-
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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},
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- author={Shaoxiong Ji and Zihao Li and Jaakko Paavola and Indraneil Paul and Hengyu Luo and Jörg Tiedemann},
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- year={2025},
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- journal={arXiv preprint 2506.00469},
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- url={https://arxiv.org/abs/2506.00469},
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- }
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- ```
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  ## Acknowledgements
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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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+ language:
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+ - af
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  license: odc-by
 
 
 
 
 
 
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  ---
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  # MaLA Corpus: Massive Language Adaptation Corpus
 
12
 
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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.
14
 
 
 
 
15
  ---
16
 
17
  ## Key Features
 
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  We will comply with legitimate requests by removing the affected sources from the corpora.
57
 
58
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
59
 
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  ## Acknowledgements
61
 
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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.
63
 
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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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