Improve dataset card with paper link, task category, and citation

README.md

@@ -1,10 +1,11 @@
 ---
 license: odc-by
 task_categories:
-- text-generation
+  - text-generation
 tags:
-- multilingual
-- low-resource
+  - multilingual
+  - translation
+  - low-resource
 ---
 
 # MaLA Corpus: Massive Language Adaptation Corpus
@@ -13,7 +14,7 @@ This is a cleaned version with some necessary data cleaning.
 
 ## Dataset Summary
 
-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.
+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. This dataset supports the work presented in [Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data](https://arxiv.org/abs/2506.00469).
 
 ---
 
@@ -22,8 +23,6 @@ The **MaLA Corpus** (Massive Language Adaptation) is a comprehensive, multilingu
 - **Language Coverage**: Includes data for **939 languages**, with **546 languages** having over 100,000 tokens.
 - **Pre-processing**: The corpus is cleaned and deduplicated to ensure high-quality training data.
 
-- Project page: https://mala-lm.github.io/emma-500
-- Paper: https://arxiv.org/abs/2409.17892
 
 ---
 
@@ -62,30 +61,22 @@ We will comply with legitimate requests by removing the affected sources from th
 
 ---
 ## Citation
-This dataset is compiled and released in the paper below.
-```
-@article{ji2024emma500enhancingmassivelymultilingual,
-      title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models}, 
-      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},
-      year={2024},
-      journal={arXiv preprint 2409.17892},
-      url={https://arxiv.org/abs/2409.17892}, 
-}
-```
 
-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
 ```
-@article{ji2025emma2,
+@article{ji2025massivelymultilingualadaptation,
       title={Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data}, 
-      author={Shaoxiong Ji and Zihao Li and Jaakko Paavola and Indraneil Paul and Hengyu Luo and Jörg Tiedemann},
+      author={Shaoxiong Ji, Zihao Li, Jaakko Paavola, Indraneil Paul, Hengyu Luo, and Jörg Tiedemann},
       year={2025},
-      journal={arXiv preprint 2506.00469},
-      url={https://arxiv.org/abs/2506.00469},
+      journal={arXiv preprint arXiv:2506.00469},
+      url={https://arxiv.org/abs/2506.00469}, 
 }
 ```
 
+
 ## Acknowledgements
 
 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.
 
-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).
+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).
+
+[Github](https://github.com/mala-lm/emma-500) | [Project Page](https://mala-lm.github.io/emma-500-gen2)

jpn_Japn/.gitattributes

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f1f58fd32af1d9afe3c8d06ebe19deef3656ab96408bbdfb46cf45e8eeb0bc3
+size 38

jpn_Japn/dataset_info.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8b0b6d7aa87413f67c12b57d96638ac68adace2efc93de64a595dab9375e1f3
+size 1349

jpn_Japn/state.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b56148cf1daaafc280303710f72b99c2dd1d19e310d4b62358fc6dcbcba0046a
+size 1014

jpn_Jpan/.gitattributes

@@ -1 +0,0 @@
-* filter=lfs diff=lfs merge=lfs -text

jpn_Jpan/dataset_info.json

@@ -1,73 +0,0 @@
-{
-  "builder_name": "json",
-  "citation": "",
-  "config_name": "default",
-  "dataset_name": "json",
-  "dataset_size": 8566291280,
-  "description": "",
-  "download_checksums": {
-    "/scratch/project_462000506/extract_jsonl/jpn_Japn/jpn_Japn_part_0.jsonl": {
-      "num_bytes": 8793311836,
-      "checksum": null
-    }
-  },
-  "download_size": 8793311836,
-  "features": {
-    "url": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "collection": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "source": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "original_code": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "text": {
-      "dtype": "string",
-      "_type": "Value"
-    }
-  },
-  "homepage": "",
-  "license": "",
-  "size_in_bytes": 17359603116,
-  "splits": {
-    "train": {
-      "name": "train",
-      "num_bytes": 8566291280,
-      "num_examples": 2324748,
-      "shard_lengths": [
-        430959,
-        110881,
-        86844,
-        88699,
-        48981,
-        146685,
-        126148,
-        109301,
-        135385,
-        140761,
-        92436,
-        48445,
-        46205,
-        64016,
-        182297,
-        237700,
-        229005
-      ],
-      "dataset_name": "json"
-    }
-  },
-  "version": {
-    "version_str": "0.0.0",
-    "major": 0,
-    "minor": 0,
-    "patch": 0
-  }
-}

jpn_Jpan/state.json

@@ -1,52 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "data-00000-of-00014.arrow"
-    },
-    {
-      "filename": "data-00001-of-00014.arrow"
-    },
-    {
-      "filename": "data-00002-of-00014.arrow"
-    },
-    {
-      "filename": "data-00003-of-00014.arrow"
-    },
-    {
-      "filename": "data-00004-of-00014.arrow"
-    },
-    {
-      "filename": "data-00005-of-00014.arrow"
-    },
-    {
-      "filename": "data-00006-of-00014.arrow"
-    },
-    {
-      "filename": "data-00007-of-00014.arrow"
-    },
-    {
-      "filename": "data-00008-of-00014.arrow"
-    },
-    {
-      "filename": "data-00009-of-00014.arrow"
-    },
-    {
-      "filename": "data-00010-of-00014.arrow"
-    },
-    {
-      "filename": "data-00011-of-00014.arrow"
-    },
-    {
-      "filename": "data-00012-of-00014.arrow"
-    },
-    {
-      "filename": "data-00013-of-00014.arrow"
-    }
-  ],
-  "_fingerprint": "6d77092ade6a4451",
-  "_format_columns": null,
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_output_all_columns": false,
-  "_split": null
-}