SaylorTwift/mteb-bitext-mining-aggregated

MTEB BitextMining Aggregated Dataset (Full)

This dataset aggregates ALL configs from 10 BitextMining datasets in the MTEB (Massive Text Embedding Benchmark) Multilingual v2 benchmark into a single, unified dataset for comprehensive bitext mining evaluation.

Dataset Summary

• Total Examples: 448,229 sentence pairs
• Source Datasets (Configs): 10 MTEB BitextMining tasks
• Total Splits: 332 language pairs/configurations
• Languages: 300+ unique language codes across all datasets
• Task: Bitext Mining (parallel sentence retrieval)
• Format: Standardized schema across all sources

Structure

Each source dataset is a config, and each original config (language pair) within that dataset is a split.

Example Usage

from datasets import load_dataset

# Load specific config (source dataset)
tatoeba = load_dataset("SaylorTwift/mteb-bitext-mining-aggregated", "Tatoeba")
# This gives you 112 splits, one for each language pair

# Access a specific language pair split
french_english = tatoeba['fra-eng']
print(f"French-English pairs: {len(french_english)}")

# Load another config
indic = load_dataset("SaylorTwift/mteb-bitext-mining-aggregated", "IndicGenBenchFloresBitextMining")
# This gives you 58 splits for different Indic language pairs

# Access a split
hindi_english = indic['hin-eng']

Schema

Each example contains:

• sentence1 (string): First sentence of the pair
• sentence2 (string): Second sentence of the pair (translation/parallel text)
• lang (string): Language pair code (e.g., "fra-eng", "de-en")
• source_dataset (string): Original MTEB dataset name
• original_split (string): Original split name (train/validation/test)
• config (string): Original config name

Configs (Source Datasets)

Config	Splits	Examples	Description
Tatoeba	112	88,877	Tatoeba sentence pairs across 112 language pairs
IN22GenBitextMining	128	131,072	Indic language pairs (23 languages, all combinations)
IndicGenBenchFloresBitextMining	58	116,522	Indic languages with English from Flores
NusaTranslationBitextMining	11	50,200	Indonesian regional language pairs
BUCC_v2	4	35,000	BUCC bitext mining (de-en, fr-en, ru-en, zh-en)
DiaBlaBitextMining	2	11,496	English-French dialogue pairs (both directions)
BornholmBitextMining	1	6,785	Danish dialect pairs
NusaXBitextMining	11	5,500	Indonesian languages with English
NollySentiBitextMining	4	1,640	Nigerian languages with English
NorwegianCourtsBitextMining	1	1,137	Norwegian court document pairs

Total: 10 configs, 332 splits, 448,229 examples

Example Splits by Config

Tatoeba (112 language pairs)

sqi-eng, fry-eng, kur-eng, tur-eng, deu-eng, ell-eng, spa-eng, fra-eng, ita-eng, jpn-eng, cmn-eng, kor-eng, ara-eng, rus-eng, por-eng, hin-eng, etc.

IN22GenBitextMining (128 Indic pairs)

asm_Beng-ben_Beng, asm_Beng-eng_Latn, ben_Beng-hin_Deva, guj_Gujr-mar_Deva, etc. (all combinations of 23 Indic languages)

IndicGenBenchFloresBitextMining (58 pairs)

asm-eng, awa-eng, ben-eng, bgc-eng, bho-eng, bod-eng, guj-eng, hin-eng, kan-eng, mal-eng, mar-eng, nep-eng, ory-eng, pan-eng, tam-eng, tel-eng, urd-eng, etc.

BUCC_v2 (4 language pairs)

de-en, fr-en, ru-en, zh-en

NusaTranslationBitextMining (11 Indonesian languages)

ind-abs, ind-bew, ind-bhp, ind-btk, ind-jav, ind-mad, ind-mak, ind-min, ind-mui, ind-rej, ind-sun

NusaXBitextMining (11 pairs)

eng-ace, eng-ban, eng-bbc, eng-bjn, eng-bug, eng-ind, eng-jav, eng-mad, eng-min, eng-nij, eng-sun

Usage Examples

Load all language pairs from a specific source

from datasets import load_dataset

# Load all Tatoeba language pairs
tatoeba = load_dataset("SaylorTwift/mteb-bitext-mining-aggregated", "Tatoeba")

# Iterate through all language pairs
for lang_pair, dataset in tatoeba.items():
    print(f"{lang_pair}: {len(dataset)} pairs")

Load a specific language pair

# Load just German-English from BUCC
bucc = load_dataset("SaylorTwift/mteb-bitext-mining-aggregated", "BUCC_v2")
de_en = bucc['de-en']

for example in de_en:
    print(f"DE: {example['sentence1']}")
    print(f"EN: {example['sentence2']}")
    print()

Filter by language across all datasets

# Load Tatoeba
tatoeba = load_dataset("SaylorTwift/mteb-bitext-mining-aggregated", "Tatoeba")

# Get all examples for a specific language pair
french_english = tatoeba['fra-eng']
print(f"Found {len(french_english)} French-English pairs")

Excluded Datasets

BibleNLPBitextMining (828 configs, 900+ languages) was excluded due to incompatible schema that uses language codes as column names instead of the standard sentence1/sentence2 format.

FloresBitextMining and NTREXBitextMining were excluded in the previous version but may be revisitable with updated processing.

Citation

If you use this dataset, please cite the MTEB benchmark:

@article{muennighoff2022mteb,
  title={MTEB: Massive Text Embedding Benchmark},
  author={Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  journal={arXiv preprint arXiv:2210.07316},
  year={2022}
}

Individual Dataset Citations

Tatoeba

@inproceedings{artetxe2019massively,
  title={Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond},
  author={Artetxe, Mikel and Schwenk, Holger},
  booktitle={Transactions of the Association for Computational Linguistics},
  year={2019}
}

BUCC

@inproceedings{zweigenbaum2017overview,
  title={Overview of the second BUCC shared task: Spotting parallel sentences in comparable corpora},
  author={Zweigenbaum, Pierre and Sharoff, Serge and Rapp, Reinhard},
  booktitle={Proceedings of the 10th workshop on building and using comparable corpora},
  year={2017}
}

Additional citations available in the original MTEB task metadata and individual dataset pages.

Dataset Statistics

Language Coverage

• Total unique language codes: 300+
• Language families: Indo-European, Sino-Tibetan, Afro-Asiatic, Austronesian, Dravidian, and many more
• Coverage: High-resource (English, French, German, Spanish, Chinese, etc.), mid-resource (Hindi, Bengali, Tamil, etc.), and low-resource languages

Split Distribution

• Total splits: 332 (each representing a specific language pair or configuration)
• Examples per split: Ranges from 228 to 8,750, with most splits containing 500-1,000 examples

Data Quality

• All sentence pairs have been validated to contain non-empty sentence1 and sentence2 fields
• Language codes are preserved from original datasets
• Source attribution maintained for every example

License

This aggregated dataset inherits the licenses from its source datasets. Most MTEB datasets are released under permissive licenses (Apache 2.0, MIT, CC-BY, etc.). Please refer to the original dataset pages for specific licensing information.

Acknowledgments

• MTEB Team: For creating and maintaining the benchmark
• Original Dataset Creators: For providing high-quality bitext mining datasets
• Hugging Face: For dataset hosting and infrastructure

Version History

• v2.0 (2026-04-02): Full release

  • 10 source datasets (configs)
  • 332 splits (all language pairs)
  • 448,229 sentence pairs
  • 300+ language codes

• v1.0 (2026-04-02): Initial partial release (deprecated)

  • Only loaded default configs
  • 8 source datasets
  • 139,457 examples

Contact

For questions or issues with this aggregated dataset, please open an issue on the repository or contact the dataset creator.