Datasets:

mteb
/

IN22ConvBitextMining

annotations_creators:
  - expert-annotated
language:
  - asm
  - ben
  - brx
  - doi
  - eng
  - gom
  - guj
  - hin
  - kan
  - kas
  - mai
  - mal
  - mar
  - mni
  - npi
  - ory
  - pan
  - san
  - sat
  - snd
  - tam
  - tel
  - urd
license: cc-by-4.0
multilinguality: multilingual
source_datasets:
  - mteb/IN22-Conv
task_categories:
  - translation
task_ids: []
dataset_info:
  features:
    - name: asm_Beng
      dtype: string
    - name: ben_Beng
      dtype: string
    - name: brx_Deva
      dtype: string
    - name: doi_Deva
      dtype: string
    - name: eng_Latn
      dtype: string
    - name: gom_Deva
      dtype: string
    - name: guj_Gujr
      dtype: string
    - name: hin_Deva
      dtype: string
    - name: kan_Knda
      dtype: string
    - name: kas_Arab
      dtype: string
    - name: mai_Deva
      dtype: string
    - name: mal_Mlym
      dtype: string
    - name: mar_Deva
      dtype: string
    - name: mni_Mtei
      dtype: string
    - name: npi_Deva
      dtype: string
    - name: ory_Orya
      dtype: string
    - name: pan_Guru
      dtype: string
    - name: san_Deva
      dtype: string
    - name: sat_Olck
      dtype: string
    - name: snd_Deva
      dtype: string
    - name: tam_Taml
      dtype: string
    - name: tel_Telu
      dtype: string
    - name: urd_Arab
      dtype: string
  splits:
    - name: test
      num_bytes: 4869897
      num_examples: 1503
  download_size: 1998395
  dataset_size: 4869897
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
tags:
  - mteb
  - text

IN22ConvBitextMining

An MTEB dataset

Massive Text Embedding Benchmark

IN22-Conv is a n-way parallel conversation domain benchmark dataset for machine translation spanning English and 22 Indic languages.


Task category	t2t
Domains	Social, Spoken, Fiction, Spoken
Reference	https://huggingface.co/datasets/ai4bharat/IN22-Conv

Source datasets:

mteb/IN22-Conv

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("IN22ConvBitextMining")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@article{gala2023indictrans,
  author = {Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
  issn = {2835-8856},
  journal = {Transactions on Machine Learning Research},
  note = {},
  title = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
  url = {https://openreview.net/forum?id=vfT4YuzAYA},
  year = {2023},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("IN22ConvBitextMining")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 760518,
        "number_of_characters": 82637104,
        "unique_pairs": 759283,
        "min_sentence1_length": 3,
        "average_sentence1_length": 54.32948595562498,
        "max_sentence1_length": 239,
        "unique_sentence1": 34430,
        "min_sentence2_length": 3,
        "average_sentence2_length": 54.32948595562498,
        "max_sentence2_length": 239,
        "unique_sentence2": 34430
    }
}

This dataset card was automatically generated using MTEB