--- annotations_creators: - derived language: - asm - ben - guj - hin - kan - mal - mar - ory - pan - tam - tel license: cc-by-4.0 multilinguality: translated source_datasets: - Divyanshu/indicxnli task_categories: - text-classification task_ids: - semantic-similarity-classification dataset_info: - config_name: as features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1377288 num_examples: 3340 download_size: 379412 dataset_size: 1377288 - config_name: bn features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1387862 num_examples: 3340 download_size: 373278 dataset_size: 1387862 - config_name: gu features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1323998 num_examples: 3340 download_size: 369826 dataset_size: 1323998 - config_name: hi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1386237 num_examples: 3340 download_size: 372810 dataset_size: 1386237 - config_name: kn features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1517243 num_examples: 3340 download_size: 400043 dataset_size: 1517243 - config_name: ml features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1553600 num_examples: 3340 download_size: 405156 dataset_size: 1553600 - config_name: mr features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1364763 num_examples: 3340 download_size: 371158 dataset_size: 1364763 - config_name: or features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1402477 num_examples: 3340 download_size: 376929 dataset_size: 1402477 - config_name: pa features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1365182 num_examples: 3340 download_size: 371108 dataset_size: 1365182 - config_name: ta features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1654445 num_examples: 3340 download_size: 410640 dataset_size: 1654445 - config_name: te features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 1408656 num_examples: 3340 download_size: 383152 dataset_size: 1408656 configs: - config_name: as data_files: - split: test path: as/test-* - config_name: bn data_files: - split: test path: bn/test-* - config_name: gu data_files: - split: test path: gu/test-* - config_name: hi data_files: - split: test path: hi/test-* - config_name: kn data_files: - split: test path: kn/test-* - config_name: ml data_files: - split: test path: ml/test-* - config_name: mr data_files: - split: test path: mr/test-* - config_name: or data_files: - split: test path: or/test-* - config_name: pa data_files: - split: test path: pa/test-* - config_name: ta data_files: - split: test path: ta/test-* - config_name: te data_files: - split: test path: te/test-* tags: - mteb - text ---

IndicXnliPairClassification

An MTEB dataset
Massive Text Embedding Benchmark
INDICXNLI is similar to existing XNLI dataset in shape/form, but focusses on Indic language family. The train (392,702), validation (2,490), and evaluation sets (5,010) of English XNLI were translated from English into each of the eleven Indic languages. IndicTrans is a large Transformer-based sequence to sequence model. It is trained on Samanantar dataset (Ramesh et al., 2021), which is the largest parallel multi- lingual corpus over eleven Indic languages. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Non-fiction, Fiction, Government, Written | | Reference | https://gem-benchmark.com/data_cards/opusparcus | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_task("IndicXnliPairClassification") 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](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{aggarwal_gupta_kunch_22, author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop}, copyright = {Creative Commons Attribution 4.0 International}, doi = {10.48550/ARXIV.2204.08776}, publisher = {arXiv}, title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages}, url = {https://arxiv.org/abs/2204.08776}, year = {2022}, } @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
Dataset Statistics The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("IndicXnliPairClassification") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*