Datasets:
metadata
annotations_creators:
- derived
language:
- eng
- fra
license: other
multilinguality: multilingual
source_datasets:
- McGill-NLP/statcan-dialogue-dataset-retrieval
task_categories:
- text-retrieval
task_ids:
- conversational
- utterance-retrieval
dataset_info:
- config_name: english-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: dev
num_bytes: 38758739
num_examples: 5907
- name: test
num_bytes: 38758739
num_examples: 5907
download_size: 18253448
dataset_size: 77517478
- config_name: english-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 24362
num_examples: 799
- name: test
num_bytes: 26970
num_examples: 870
download_size: 16359
dataset_size: 51332
- config_name: english-queries
features:
- name: _id
dtype: string
- name: text
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 351289
num_examples: 543
- name: test
num_bytes: 403675
num_examples: 553
download_size: 313644
dataset_size: 754964
- config_name: french-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: dev
num_bytes: 42530544
num_examples: 5907
- name: test
num_bytes: 42530544
num_examples: 5907
download_size: 20041468
dataset_size: 85061088
- config_name: french-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 6089
num_examples: 201
- name: test
num_bytes: 4371
num_examples: 141
download_size: 5938
dataset_size: 10460
- config_name: french-queries
features:
- name: _id
dtype: string
- name: text
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 84889
num_examples: 122
- name: test
num_bytes: 68323
num_examples: 108
download_size: 67281
dataset_size: 153212
configs:
- config_name: english-corpus
data_files:
- split: dev
path: english-corpus/dev-*
- split: test
path: english-corpus/test-*
- config_name: english-qrels
data_files:
- split: dev
path: english-qrels/dev-*
- split: test
path: english-qrels/test-*
- config_name: english-queries
data_files:
- split: dev
path: english-queries/dev-*
- split: test
path: english-queries/test-*
- config_name: french-corpus
data_files:
- split: dev
path: french-corpus/dev-*
- split: test
path: french-corpus/test-*
- config_name: french-qrels
data_files:
- split: dev
path: french-qrels/dev-*
- split: test
path: french-qrels/test-*
- config_name: french-queries
data_files:
- split: dev
path: french-queries/dev-*
- split: test
path: french-queries/test-*
tags:
- mteb
- text
A Dataset for Retrieving Data Tables through Conversations with Genuine Intents, available in English and French.
| Task category | t2t |
| Domains | Government, Web, Written |
| Reference | https://mcgill-nlp.github.io/statcan-dialogue-dataset/ |
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("StatcanDialogueDatasetRetrieval")
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.
@inproceedings{lu-etal-2023-statcan,
address = {Dubrovnik, Croatia},
author = {Lu, Xing Han and
Reddy, Siva and
de Vries, Harm},
booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
month = may,
pages = {2799--2829},
publisher = {Association for Computational Linguistics},
title = {The {S}tat{C}an Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents},
url = {https://arxiv.org/abs/2304.01412},
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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("StatcanDialogueDatasetRetrieval")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB