--- annotations_creators: - expert-annotated language: - asm - ben - eng - guj - hin - kan - mal - mar - ory - pan - tam - tel - urd license: cc0-1.0 multilinguality: multilingual task_categories: - sentence-similarity task_ids: - semantic-similarity-scoring dataset_info: - config_name: en-as features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 60945 num_examples: 256 download_size: 35376 dataset_size: 60945 - config_name: en-bn features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 67460 num_examples: 256 download_size: 38088 dataset_size: 67460 - config_name: en-gu features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 64786 num_examples: 256 download_size: 37140 dataset_size: 64786 - config_name: en-hi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 92497 num_examples: 256 download_size: 51498 dataset_size: 92497 - config_name: en-kn features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 77385 num_examples: 256 download_size: 42987 dataset_size: 77385 - config_name: en-ml features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 79979 num_examples: 256 download_size: 44196 dataset_size: 79979 - config_name: en-mr features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 75928 num_examples: 256 download_size: 43383 dataset_size: 75928 - config_name: en-or features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 57794 num_examples: 256 download_size: 32315 dataset_size: 57794 - config_name: en-pa features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 75532 num_examples: 256 download_size: 43175 dataset_size: 75532 - config_name: en-ta features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 87284 num_examples: 256 download_size: 43472 dataset_size: 87284 - config_name: en-te features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 79011 num_examples: 256 download_size: 43790 dataset_size: 79011 - config_name: en-ur features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 72395 num_examples: 256 download_size: 46115 dataset_size: 72395 configs: - config_name: en-as data_files: - split: test path: en-as/test-* - config_name: en-bn data_files: - split: test path: en-bn/test-* - config_name: en-gu data_files: - split: test path: en-gu/test-* - config_name: en-hi data_files: - split: test path: en-hi/test-* - config_name: en-kn data_files: - split: test path: en-kn/test-* - config_name: en-ml data_files: - split: test path: en-ml/test-* - config_name: en-mr data_files: - split: test path: en-mr/test-* - config_name: en-or data_files: - split: test path: en-or/test-* - config_name: en-pa data_files: - split: test path: en-pa/test-* - config_name: en-ta data_files: - split: test path: en-ta/test-* - config_name: en-te data_files: - split: test path: en-te/test-* - config_name: en-ur data_files: - split: test path: en-ur/test-* tags: - mteb - text ---

IndicCrosslingualSTS

An MTEB dataset
Massive Text Embedding Benchmark
This is a Semantic Textual Similarity testset between English and 12 high-resource Indic languages. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | News, Non-fiction, Web, Spoken, Government, Written, Spoken | | Reference | https://huggingface.co/datasets/jaygala24/indic_sts | ## 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_tasks(["IndicCrosslingualSTS"]) 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 repitory](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 @article{10.1162/tacl_a_00452, author = {Ramesh, Gowtham and Doddapaneni, Sumanth and Bheemaraj, Aravinth and Jobanputra, Mayank and AK, Raghavan and Sharma, Ajitesh and Sahoo, Sujit and Diddee, Harshita and J, Mahalakshmi and Kakwani, Divyanshu and Kumar, Navneet and Pradeep, Aswin and Nagaraj, Srihari and Deepak, Kumar and Raghavan, Vivek and Kunchukuttan, Anoop and Kumar, Pratyush and Khapra, Mitesh Shantadevi}, doi = {10.1162/tacl_a_00452}, eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\\_a\\_00452/1987010/tacl\\_a\\_00452.pdf}, issn = {2307-387X}, journal = {Transactions of the Association for Computational Linguistics}, month = {02}, pages = {145-162}, title = {{Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages}}, url = {https://doi.org/10.1162/tacl\\_a\\_00452}, volume = {10}, 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, Lo{\"\i}c 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("IndicCrosslingualSTS") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 3072, "number_of_characters": 468907, "unique_pairs": 3072, "min_sentence1_length": 2, "average_sentence1_len": 74.6455078125, "max_sentence1_length": 1042, "unique_sentence1": 3059, "min_sentence2_length": 5, "average_sentence2_len": 77.99348958333333, "max_sentence2_length": 958, "unique_sentence2": 3071, "min_score": 0.0, "avg_score": 3.816057942708332, "max_score": 5.0 } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*