Datasets:
metadata
language:
- multilingual
license: apache-2.0
tags:
- bitext-mining
- sentence-embeddings
- mteb
- multilingual
task_categories:
- sentence-similarity
pretty_name: MTEB BitextMining Aggregated Dataset (Full)
size_categories:
- 100K<n<1M
configs:
- config_name: BUCC_v2
data_files:
- split: fr_en
path: BUCC_v2/fr_en-*
- split: ru_en
path: BUCC_v2/ru_en-*
- split: de_en
path: BUCC_v2/de_en-*
- split: zh_en
path: BUCC_v2/zh_en-*
- config_name: BornholmBitextMining
data_files:
- split: default
path: BornholmBitextMining/default-*
- config_name: DiaBlaBitextMining
data_files:
- split: en_fr
path: DiaBlaBitextMining/en_fr-*
- split: fr_en
path: DiaBlaBitextMining/fr_en-*
- config_name: IN22GenBitextMining
data_files:
- split: asm_Beng_ben_Beng
path: IN22GenBitextMining/asm_Beng_ben_Beng-*
- split: asm_Beng_brx_Deva
path: IN22GenBitextMining/asm_Beng_brx_Deva-*
- split: asm_Beng_doi_Deva
path: IN22GenBitextMining/asm_Beng_doi_Deva-*
- split: asm_Beng_eng_Latn
path: IN22GenBitextMining/asm_Beng_eng_Latn-*
- split: asm_Beng_gom_Deva
path: IN22GenBitextMining/asm_Beng_gom_Deva-*
- split: asm_Beng_guj_Gujr
path: IN22GenBitextMining/asm_Beng_guj_Gujr-*
- split: asm_Beng_hin_Deva
path: IN22GenBitextMining/asm_Beng_hin_Deva-*
- split: asm_Beng_kan_Knda
path: IN22GenBitextMining/asm_Beng_kan_Knda-*
- split: asm_Beng_kas_Arab
path: IN22GenBitextMining/asm_Beng_kas_Arab-*
- split: asm_Beng_mai_Deva
path: IN22GenBitextMining/asm_Beng_mai_Deva-*
- split: asm_Beng_mal_Mlym
path: IN22GenBitextMining/asm_Beng_mal_Mlym-*
- split: asm_Beng_mar_Deva
path: IN22GenBitextMining/asm_Beng_mar_Deva-*
- split: asm_Beng_mni_Mtei
path: IN22GenBitextMining/asm_Beng_mni_Mtei-*
- split: asm_Beng_npi_Deva
path: IN22GenBitextMining/asm_Beng_npi_Deva-*
- split: asm_Beng_ory_Orya
path: IN22GenBitextMining/asm_Beng_ory_Orya-*
- split: asm_Beng_pan_Guru
path: IN22GenBitextMining/asm_Beng_pan_Guru-*
- split: asm_Beng_san_Deva
path: IN22GenBitextMining/asm_Beng_san_Deva-*
- split: asm_Beng_sat_Olck
path: IN22GenBitextMining/asm_Beng_sat_Olck-*
- split: asm_Beng_snd_Deva
path: IN22GenBitextMining/asm_Beng_snd_Deva-*
- split: asm_Beng_tam_Taml
path: IN22GenBitextMining/asm_Beng_tam_Taml-*
- split: asm_Beng_tel_Telu
path: IN22GenBitextMining/asm_Beng_tel_Telu-*
- split: asm_Beng_urd_Arab
path: IN22GenBitextMining/asm_Beng_urd_Arab-*
- split: ben_Beng_asm_Beng
path: IN22GenBitextMining/ben_Beng_asm_Beng-*
- split: ben_Beng_brx_Deva
path: IN22GenBitextMining/ben_Beng_brx_Deva-*
- split: ben_Beng_doi_Deva
path: IN22GenBitextMining/ben_Beng_doi_Deva-*
- split: ben_Beng_eng_Latn
path: IN22GenBitextMining/ben_Beng_eng_Latn-*
- split: ben_Beng_gom_Deva
path: IN22GenBitextMining/ben_Beng_gom_Deva-*
- split: ben_Beng_guj_Gujr
path: IN22GenBitextMining/ben_Beng_guj_Gujr-*
- split: ben_Beng_hin_Deva
path: IN22GenBitextMining/ben_Beng_hin_Deva-*
- split: ben_Beng_kan_Knda
path: IN22GenBitextMining/ben_Beng_kan_Knda-*
- split: ben_Beng_kas_Arab
path: IN22GenBitextMining/ben_Beng_kas_Arab-*
- split: ben_Beng_mai_Deva
path: IN22GenBitextMining/ben_Beng_mai_Deva-*
- split: ben_Beng_mal_Mlym
path: IN22GenBitextMining/ben_Beng_mal_Mlym-*
- split: ben_Beng_mar_Deva
path: IN22GenBitextMining/ben_Beng_mar_Deva-*
- split: ben_Beng_mni_Mtei
path: IN22GenBitextMining/ben_Beng_mni_Mtei-*
- split: ben_Beng_npi_Deva
path: IN22GenBitextMining/ben_Beng_npi_Deva-*
- split: ben_Beng_ory_Orya
path: IN22GenBitextMining/ben_Beng_ory_Orya-*
- split: ben_Beng_pan_Guru
path: IN22GenBitextMining/ben_Beng_pan_Guru-*
- split: ben_Beng_san_Deva
path: IN22GenBitextMining/ben_Beng_san_Deva-*
- split: ben_Beng_sat_Olck
path: IN22GenBitextMining/ben_Beng_sat_Olck-*
- split: ben_Beng_snd_Deva
path: IN22GenBitextMining/ben_Beng_snd_Deva-*
- split: ben_Beng_tam_Taml
path: IN22GenBitextMining/ben_Beng_tam_Taml-*
- split: ben_Beng_tel_Telu
path: IN22GenBitextMining/ben_Beng_tel_Telu-*
- split: ben_Beng_urd_Arab
path: IN22GenBitextMining/ben_Beng_urd_Arab-*
- split: brx_Deva_asm_Beng
path: IN22GenBitextMining/brx_Deva_asm_Beng-*
- split: brx_Deva_ben_Beng
path: IN22GenBitextMining/brx_Deva_ben_Beng-*
- split: brx_Deva_doi_Deva
path: IN22GenBitextMining/brx_Deva_doi_Deva-*
- split: brx_Deva_eng_Latn
path: IN22GenBitextMining/brx_Deva_eng_Latn-*
- split: brx_Deva_gom_Deva
path: IN22GenBitextMining/brx_Deva_gom_Deva-*
- split: brx_Deva_guj_Gujr
path: IN22GenBitextMining/brx_Deva_guj_Gujr-*
- split: brx_Deva_hin_Deva
path: IN22GenBitextMining/brx_Deva_hin_Deva-*
- split: brx_Deva_kan_Knda
path: IN22GenBitextMining/brx_Deva_kan_Knda-*
- split: brx_Deva_kas_Arab
path: IN22GenBitextMining/brx_Deva_kas_Arab-*
- split: brx_Deva_mai_Deva
path: IN22GenBitextMining/brx_Deva_mai_Deva-*
- split: brx_Deva_mal_Mlym
path: IN22GenBitextMining/brx_Deva_mal_Mlym-*
- split: brx_Deva_mar_Deva
path: IN22GenBitextMining/brx_Deva_mar_Deva-*
- split: brx_Deva_mni_Mtei
path: IN22GenBitextMining/brx_Deva_mni_Mtei-*
- split: brx_Deva_npi_Deva
path: IN22GenBitextMining/brx_Deva_npi_Deva-*
- split: brx_Deva_ory_Orya
path: IN22GenBitextMining/brx_Deva_ory_Orya-*
- split: brx_Deva_pan_Guru
path: IN22GenBitextMining/brx_Deva_pan_Guru-*
- split: brx_Deva_san_Deva
path: IN22GenBitextMining/brx_Deva_san_Deva-*
- split: brx_Deva_sat_Olck
path: IN22GenBitextMining/brx_Deva_sat_Olck-*
- split: brx_Deva_snd_Deva
path: IN22GenBitextMining/brx_Deva_snd_Deva-*
- split: brx_Deva_tam_Taml
path: IN22GenBitextMining/brx_Deva_tam_Taml-*
- split: brx_Deva_tel_Telu
path: IN22GenBitextMining/brx_Deva_tel_Telu-*
- split: brx_Deva_urd_Arab
path: IN22GenBitextMining/brx_Deva_urd_Arab-*
- split: doi_Deva_asm_Beng
path: IN22GenBitextMining/doi_Deva_asm_Beng-*
- split: doi_Deva_ben_Beng
path: IN22GenBitextMining/doi_Deva_ben_Beng-*
- split: doi_Deva_brx_Deva
path: IN22GenBitextMining/doi_Deva_brx_Deva-*
- split: doi_Deva_eng_Latn
path: IN22GenBitextMining/doi_Deva_eng_Latn-*
- split: doi_Deva_gom_Deva
path: IN22GenBitextMining/doi_Deva_gom_Deva-*
- split: doi_Deva_guj_Gujr
path: IN22GenBitextMining/doi_Deva_guj_Gujr-*
- split: doi_Deva_hin_Deva
path: IN22GenBitextMining/doi_Deva_hin_Deva-*
- split: doi_Deva_kan_Knda
path: IN22GenBitextMining/doi_Deva_kan_Knda-*
- split: doi_Deva_kas_Arab
path: IN22GenBitextMining/doi_Deva_kas_Arab-*
- split: doi_Deva_mai_Deva
path: IN22GenBitextMining/doi_Deva_mai_Deva-*
- split: doi_Deva_mal_Mlym
path: IN22GenBitextMining/doi_Deva_mal_Mlym-*
- split: doi_Deva_mar_Deva
path: IN22GenBitextMining/doi_Deva_mar_Deva-*
- split: doi_Deva_mni_Mtei
path: IN22GenBitextMining/doi_Deva_mni_Mtei-*
- split: doi_Deva_npi_Deva
path: IN22GenBitextMining/doi_Deva_npi_Deva-*
- split: doi_Deva_ory_Orya
path: IN22GenBitextMining/doi_Deva_ory_Orya-*
- split: doi_Deva_pan_Guru
path: IN22GenBitextMining/doi_Deva_pan_Guru-*
- split: doi_Deva_san_Deva
path: IN22GenBitextMining/doi_Deva_san_Deva-*
- split: doi_Deva_sat_Olck
path: IN22GenBitextMining/doi_Deva_sat_Olck-*
- split: doi_Deva_snd_Deva
path: IN22GenBitextMining/doi_Deva_snd_Deva-*
- split: doi_Deva_tam_Taml
path: IN22GenBitextMining/doi_Deva_tam_Taml-*
- split: doi_Deva_tel_Telu
path: IN22GenBitextMining/doi_Deva_tel_Telu-*
- split: doi_Deva_urd_Arab
path: IN22GenBitextMining/doi_Deva_urd_Arab-*
- split: eng_Latn_asm_Beng
path: IN22GenBitextMining/eng_Latn_asm_Beng-*
- split: eng_Latn_ben_Beng
path: IN22GenBitextMining/eng_Latn_ben_Beng-*
- split: eng_Latn_brx_Deva
path: IN22GenBitextMining/eng_Latn_brx_Deva-*
- split: eng_Latn_doi_Deva
path: IN22GenBitextMining/eng_Latn_doi_Deva-*
- split: eng_Latn_gom_Deva
path: IN22GenBitextMining/eng_Latn_gom_Deva-*
- split: eng_Latn_guj_Gujr
path: IN22GenBitextMining/eng_Latn_guj_Gujr-*
- split: eng_Latn_hin_Deva
path: IN22GenBitextMining/eng_Latn_hin_Deva-*
- split: eng_Latn_kan_Knda
path: IN22GenBitextMining/eng_Latn_kan_Knda-*
- split: eng_Latn_kas_Arab
path: IN22GenBitextMining/eng_Latn_kas_Arab-*
- split: eng_Latn_mai_Deva
path: IN22GenBitextMining/eng_Latn_mai_Deva-*
- split: eng_Latn_mal_Mlym
path: IN22GenBitextMining/eng_Latn_mal_Mlym-*
- split: eng_Latn_mar_Deva
path: IN22GenBitextMining/eng_Latn_mar_Deva-*
- split: eng_Latn_mni_Mtei
path: IN22GenBitextMining/eng_Latn_mni_Mtei-*
- split: eng_Latn_npi_Deva
path: IN22GenBitextMining/eng_Latn_npi_Deva-*
- split: eng_Latn_ory_Orya
path: IN22GenBitextMining/eng_Latn_ory_Orya-*
- split: eng_Latn_pan_Guru
path: IN22GenBitextMining/eng_Latn_pan_Guru-*
- split: eng_Latn_san_Deva
path: IN22GenBitextMining/eng_Latn_san_Deva-*
- split: eng_Latn_sat_Olck
path: IN22GenBitextMining/eng_Latn_sat_Olck-*
- split: eng_Latn_snd_Deva
path: IN22GenBitextMining/eng_Latn_snd_Deva-*
- split: eng_Latn_tam_Taml
path: IN22GenBitextMining/eng_Latn_tam_Taml-*
- split: eng_Latn_tel_Telu
path: IN22GenBitextMining/eng_Latn_tel_Telu-*
- split: eng_Latn_urd_Arab
path: IN22GenBitextMining/eng_Latn_urd_Arab-*
- split: gom_Deva_asm_Beng
path: IN22GenBitextMining/gom_Deva_asm_Beng-*
- split: gom_Deva_ben_Beng
path: IN22GenBitextMining/gom_Deva_ben_Beng-*
- split: gom_Deva_brx_Deva
path: IN22GenBitextMining/gom_Deva_brx_Deva-*
- split: gom_Deva_doi_Deva
path: IN22GenBitextMining/gom_Deva_doi_Deva-*
- split: gom_Deva_eng_Latn
path: IN22GenBitextMining/gom_Deva_eng_Latn-*
- split: gom_Deva_guj_Gujr
path: IN22GenBitextMining/gom_Deva_guj_Gujr-*
- split: gom_Deva_hin_Deva
path: IN22GenBitextMining/gom_Deva_hin_Deva-*
- split: gom_Deva_kan_Knda
path: IN22GenBitextMining/gom_Deva_kan_Knda-*
- split: gom_Deva_kas_Arab
path: IN22GenBitextMining/gom_Deva_kas_Arab-*
- split: gom_Deva_mai_Deva
path: IN22GenBitextMining/gom_Deva_mai_Deva-*
- split: gom_Deva_mal_Mlym
path: IN22GenBitextMining/gom_Deva_mal_Mlym-*
- split: gom_Deva_mar_Deva
path: IN22GenBitextMining/gom_Deva_mar_Deva-*
- split: gom_Deva_mni_Mtei
path: IN22GenBitextMining/gom_Deva_mni_Mtei-*
- split: gom_Deva_npi_Deva
path: IN22GenBitextMining/gom_Deva_npi_Deva-*
- split: gom_Deva_ory_Orya
path: IN22GenBitextMining/gom_Deva_ory_Orya-*
- split: gom_Deva_pan_Guru
path: IN22GenBitextMining/gom_Deva_pan_Guru-*
- split: gom_Deva_san_Deva
path: IN22GenBitextMining/gom_Deva_san_Deva-*
- split: gom_Deva_sat_Olck
path: IN22GenBitextMining/gom_Deva_sat_Olck-*
- config_name: IndicGenBenchFloresBitextMining
data_files:
- split: asm_eng
path: IndicGenBenchFloresBitextMining/asm_eng-*
- split: awa_eng
path: IndicGenBenchFloresBitextMining/awa_eng-*
- split: ben_eng
path: IndicGenBenchFloresBitextMining/ben_eng-*
- split: bgc_eng
path: IndicGenBenchFloresBitextMining/bgc_eng-*
- split: bho_eng
path: IndicGenBenchFloresBitextMining/bho_eng-*
- split: bod_eng
path: IndicGenBenchFloresBitextMining/bod_eng-*
- split: boy_eng
path: IndicGenBenchFloresBitextMining/boy_eng-*
- split: eng_asm
path: IndicGenBenchFloresBitextMining/eng_asm-*
- split: eng_awa
path: IndicGenBenchFloresBitextMining/eng_awa-*
- split: eng_ben
path: IndicGenBenchFloresBitextMining/eng_ben-*
- split: eng_bgc
path: IndicGenBenchFloresBitextMining/eng_bgc-*
- split: eng_bho
path: IndicGenBenchFloresBitextMining/eng_bho-*
- split: eng_bod
path: IndicGenBenchFloresBitextMining/eng_bod-*
- split: eng_boy
path: IndicGenBenchFloresBitextMining/eng_boy-*
- split: eng_gbm
path: IndicGenBenchFloresBitextMining/eng_gbm-*
- split: eng_gom
path: IndicGenBenchFloresBitextMining/eng_gom-*
- split: eng_guj
path: IndicGenBenchFloresBitextMining/eng_guj-*
- split: eng_hin
path: IndicGenBenchFloresBitextMining/eng_hin-*
- split: eng_hne
path: IndicGenBenchFloresBitextMining/eng_hne-*
- split: eng_kan
path: IndicGenBenchFloresBitextMining/eng_kan-*
- split: eng_mai
path: IndicGenBenchFloresBitextMining/eng_mai-*
- split: eng_mal
path: IndicGenBenchFloresBitextMining/eng_mal-*
- split: eng_mar
path: IndicGenBenchFloresBitextMining/eng_mar-*
- split: eng_mni
path: IndicGenBenchFloresBitextMining/eng_mni-*
- split: eng_mup
path: IndicGenBenchFloresBitextMining/eng_mup-*
- split: eng_mwr
path: IndicGenBenchFloresBitextMining/eng_mwr-*
- split: eng_nep
path: IndicGenBenchFloresBitextMining/eng_nep-*
- split: eng_ory
path: IndicGenBenchFloresBitextMining/eng_ory-*
- split: eng_pan
path: IndicGenBenchFloresBitextMining/eng_pan-*
- split: eng_pus
path: IndicGenBenchFloresBitextMining/eng_pus-*
- split: eng_raj
path: IndicGenBenchFloresBitextMining/eng_raj-*
- split: eng_san
path: IndicGenBenchFloresBitextMining/eng_san-*
- split: eng_sat
path: IndicGenBenchFloresBitextMining/eng_sat-*
- split: eng_tam
path: IndicGenBenchFloresBitextMining/eng_tam-*
- split: eng_tel
path: IndicGenBenchFloresBitextMining/eng_tel-*
- split: eng_urd
path: IndicGenBenchFloresBitextMining/eng_urd-*
- split: gbm_eng
path: IndicGenBenchFloresBitextMining/gbm_eng-*
- split: gom_eng
path: IndicGenBenchFloresBitextMining/gom_eng-*
- split: guj_eng
path: IndicGenBenchFloresBitextMining/guj_eng-*
- split: hin_eng
path: IndicGenBenchFloresBitextMining/hin_eng-*
- split: hne_eng
path: IndicGenBenchFloresBitextMining/hne_eng-*
- split: kan_eng
path: IndicGenBenchFloresBitextMining/kan_eng-*
- split: mai_eng
path: IndicGenBenchFloresBitextMining/mai_eng-*
- split: mal_eng
path: IndicGenBenchFloresBitextMining/mal_eng-*
- split: mar_eng
path: IndicGenBenchFloresBitextMining/mar_eng-*
- split: mni_eng
path: IndicGenBenchFloresBitextMining/mni_eng-*
- split: mup_eng
path: IndicGenBenchFloresBitextMining/mup_eng-*
- split: mwr_eng
path: IndicGenBenchFloresBitextMining/mwr_eng-*
- split: nep_eng
path: IndicGenBenchFloresBitextMining/nep_eng-*
- split: ory_eng
path: IndicGenBenchFloresBitextMining/ory_eng-*
- split: pan_eng
path: IndicGenBenchFloresBitextMining/pan_eng-*
- split: pus_eng
path: IndicGenBenchFloresBitextMining/pus_eng-*
- split: raj_eng
path: IndicGenBenchFloresBitextMining/raj_eng-*
- split: san_eng
path: IndicGenBenchFloresBitextMining/san_eng-*
- split: sat_eng
path: IndicGenBenchFloresBitextMining/sat_eng-*
- split: tam_eng
path: IndicGenBenchFloresBitextMining/tam_eng-*
- split: tel_eng
path: IndicGenBenchFloresBitextMining/tel_eng-*
- split: urd_eng
path: IndicGenBenchFloresBitextMining/urd_eng-*
- config_name: NollySentiBitextMining
data_files:
- split: en_ha
path: NollySentiBitextMining/en_ha-*
- split: en_ig
path: NollySentiBitextMining/en_ig-*
- split: en_pcm
path: NollySentiBitextMining/en_pcm-*
- split: en_yo
path: NollySentiBitextMining/en_yo-*
- config_name: NorwegianCourtsBitextMining
data_files:
- split: default
path: NorwegianCourtsBitextMining/default-*
- config_name: NusaTranslationBitextMining
data_files:
- split: ind_abs
path: NusaTranslationBitextMining/ind_abs-*
- split: ind_bew
path: NusaTranslationBitextMining/ind_bew-*
- split: ind_bhp
path: NusaTranslationBitextMining/ind_bhp-*
- split: ind_btk
path: NusaTranslationBitextMining/ind_btk-*
- split: ind_jav
path: NusaTranslationBitextMining/ind_jav-*
- split: ind_mad
path: NusaTranslationBitextMining/ind_mad-*
- split: ind_mak
path: NusaTranslationBitextMining/ind_mak-*
- split: ind_min
path: NusaTranslationBitextMining/ind_min-*
- split: ind_mui
path: NusaTranslationBitextMining/ind_mui-*
- split: ind_rej
path: NusaTranslationBitextMining/ind_rej-*
- split: ind_sun
path: NusaTranslationBitextMining/ind_sun-*
- config_name: NusaXBitextMining
data_files:
- split: eng_ace
path: NusaXBitextMining/eng_ace-*
- split: eng_ban
path: NusaXBitextMining/eng_ban-*
- split: eng_bbc
path: NusaXBitextMining/eng_bbc-*
- split: eng_bjn
path: NusaXBitextMining/eng_bjn-*
- split: eng_bug
path: NusaXBitextMining/eng_bug-*
- split: eng_ind
path: NusaXBitextMining/eng_ind-*
- split: eng_jav
path: NusaXBitextMining/eng_jav-*
- split: eng_mad
path: NusaXBitextMining/eng_mad-*
- split: eng_min
path: NusaXBitextMining/eng_min-*
- split: eng_nij
path: NusaXBitextMining/eng_nij-*
- split: eng_sun
path: NusaXBitextMining/eng_sun-*
- config_name: Tatoeba
data_files:
- split: sqi_eng
path: Tatoeba/sqi_eng-*
- split: fry_eng
path: Tatoeba/fry_eng-*
- split: kur_eng
path: Tatoeba/kur_eng-*
- split: tur_eng
path: Tatoeba/tur_eng-*
- split: deu_eng
path: Tatoeba/deu_eng-*
- split: nld_eng
path: Tatoeba/nld_eng-*
- split: ron_eng
path: Tatoeba/ron_eng-*
- split: ang_eng
path: Tatoeba/ang_eng-*
- split: ido_eng
path: Tatoeba/ido_eng-*
- split: jav_eng
path: Tatoeba/jav_eng-*
- split: isl_eng
path: Tatoeba/isl_eng-*
- split: slv_eng
path: Tatoeba/slv_eng-*
- split: cym_eng
path: Tatoeba/cym_eng-*
- split: kaz_eng
path: Tatoeba/kaz_eng-*
- split: est_eng
path: Tatoeba/est_eng-*
- split: heb_eng
path: Tatoeba/heb_eng-*
- split: gla_eng
path: Tatoeba/gla_eng-*
- split: mar_eng
path: Tatoeba/mar_eng-*
- split: lat_eng
path: Tatoeba/lat_eng-*
- split: bel_eng
path: Tatoeba/bel_eng-*
- split: pms_eng
path: Tatoeba/pms_eng-*
- split: gle_eng
path: Tatoeba/gle_eng-*
- split: pes_eng
path: Tatoeba/pes_eng-*
- split: nob_eng
path: Tatoeba/nob_eng-*
- split: bul_eng
path: Tatoeba/bul_eng-*
- split: cbk_eng
path: Tatoeba/cbk_eng-*
- split: hun_eng
path: Tatoeba/hun_eng-*
- split: uig_eng
path: Tatoeba/uig_eng-*
- split: rus_eng
path: Tatoeba/rus_eng-*
- split: spa_eng
path: Tatoeba/spa_eng-*
- split: hye_eng
path: Tatoeba/hye_eng-*
- split: tel_eng
path: Tatoeba/tel_eng-*
- split: afr_eng
path: Tatoeba/afr_eng-*
- split: mon_eng
path: Tatoeba/mon_eng-*
- split: arz_eng
path: Tatoeba/arz_eng-*
- split: hrv_eng
path: Tatoeba/hrv_eng-*
- split: nov_eng
path: Tatoeba/nov_eng-*
- split: gsw_eng
path: Tatoeba/gsw_eng-*
- split: nds_eng
path: Tatoeba/nds_eng-*
- split: ukr_eng
path: Tatoeba/ukr_eng-*
- split: uzb_eng
path: Tatoeba/uzb_eng-*
- split: lit_eng
path: Tatoeba/lit_eng-*
- split: ina_eng
path: Tatoeba/ina_eng-*
- split: lfn_eng
path: Tatoeba/lfn_eng-*
- split: zsm_eng
path: Tatoeba/zsm_eng-*
- split: ita_eng
path: Tatoeba/ita_eng-*
- split: cmn_eng
path: Tatoeba/cmn_eng-*
- split: lvs_eng
path: Tatoeba/lvs_eng-*
- split: glg_eng
path: Tatoeba/glg_eng-*
- split: ceb_eng
path: Tatoeba/ceb_eng-*
- split: bre_eng
path: Tatoeba/bre_eng-*
- split: ben_eng
path: Tatoeba/ben_eng-*
- split: swg_eng
path: Tatoeba/swg_eng-*
- split: arq_eng
path: Tatoeba/arq_eng-*
- split: kab_eng
path: Tatoeba/kab_eng-*
- split: fra_eng
path: Tatoeba/fra_eng-*
- split: por_eng
path: Tatoeba/por_eng-*
- split: tat_eng
path: Tatoeba/tat_eng-*
- split: oci_eng
path: Tatoeba/oci_eng-*
- split: pol_eng
path: Tatoeba/pol_eng-*
- split: war_eng
path: Tatoeba/war_eng-*
- split: aze_eng
path: Tatoeba/aze_eng-*
- split: vie_eng
path: Tatoeba/vie_eng-*
- split: nno_eng
path: Tatoeba/nno_eng-*
- split: cha_eng
path: Tatoeba/cha_eng-*
- split: mhr_eng
path: Tatoeba/mhr_eng-*
- split: dan_eng
path: Tatoeba/dan_eng-*
- split: ell_eng
path: Tatoeba/ell_eng-*
- split: amh_eng
path: Tatoeba/amh_eng-*
- split: pam_eng
path: Tatoeba/pam_eng-*
- split: hsb_eng
path: Tatoeba/hsb_eng-*
- split: srp_eng
path: Tatoeba/srp_eng-*
- split: epo_eng
path: Tatoeba/epo_eng-*
- split: kzj_eng
path: Tatoeba/kzj_eng-*
- split: awa_eng
path: Tatoeba/awa_eng-*
- split: fao_eng
path: Tatoeba/fao_eng-*
- split: mal_eng
path: Tatoeba/mal_eng-*
- split: ile_eng
path: Tatoeba/ile_eng-*
- split: bos_eng
path: Tatoeba/bos_eng-*
- split: cor_eng
path: Tatoeba/cor_eng-*
- split: cat_eng
path: Tatoeba/cat_eng-*
- split: eus_eng
path: Tatoeba/eus_eng-*
- split: yue_eng
path: Tatoeba/yue_eng-*
- split: swe_eng
path: Tatoeba/swe_eng-*
- split: dtp_eng
path: Tatoeba/dtp_eng-*
- split: kat_eng
path: Tatoeba/kat_eng-*
- split: jpn_eng
path: Tatoeba/jpn_eng-*
- split: csb_eng
path: Tatoeba/csb_eng-*
- split: xho_eng
path: Tatoeba/xho_eng-*
- split: orv_eng
path: Tatoeba/orv_eng-*
- split: ind_eng
path: Tatoeba/ind_eng-*
- split: tuk_eng
path: Tatoeba/tuk_eng-*
- split: max_eng
path: Tatoeba/max_eng-*
- split: swh_eng
path: Tatoeba/swh_eng-*
- split: hin_eng
path: Tatoeba/hin_eng-*
- split: dsb_eng
path: Tatoeba/dsb_eng-*
- split: ber_eng
path: Tatoeba/ber_eng-*
- split: tam_eng
path: Tatoeba/tam_eng-*
- split: slk_eng
path: Tatoeba/slk_eng-*
- split: tgl_eng
path: Tatoeba/tgl_eng-*
- split: ast_eng
path: Tatoeba/ast_eng-*
- split: mkd_eng
path: Tatoeba/mkd_eng-*
- split: khm_eng
path: Tatoeba/khm_eng-*
- split: ces_eng
path: Tatoeba/ces_eng-*
- split: tzl_eng
path: Tatoeba/tzl_eng-*
- split: urd_eng
path: Tatoeba/urd_eng-*
- split: ara_eng
path: Tatoeba/ara_eng-*
- split: kor_eng
path: Tatoeba/kor_eng-*
- split: yid_eng
path: Tatoeba/yid_eng-*
- split: fin_eng
path: Tatoeba/fin_eng-*
- split: tha_eng
path: Tatoeba/tha_eng-*
- split: wuu_eng
path: Tatoeba/wuu_eng-*
- config_name: default
data_files:
- split: train
path: data/train-*
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features:
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- name: sentence2
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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 pairsentence2(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 nameoriginal_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
sentence1andsentence2fields - 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.