README.md

---
annotations_creators:
- expert-annotated
language:
- dan
license: cc-by-sa-3.0
multilinguality: monolingual
task_categories:
- text-classification
task_ids: []
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 806358
    num_examples: 801
  - name: test
    num_bytes: 206987
    num_examples: 201
  download_size: 668118
  dataset_size: 1013345
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Ddisco</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

A Danish Discourse dataset with values for coherence and source (Wikipedia or Reddit)

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2c                              |
| Domains       | Non-fiction, Social, Written                               |
| Reference     | https://aclanthology.org/2022.lrec-1.260/ |


## 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(["Ddisco"])
evaluator = mteb.MTEB(task)

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

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
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

@inproceedings{flansmose-mikkelsen-etal-2022-ddisco,
  abstract = {To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.},
  address = {Marseille, France},
  author = {Flansmose Mikkelsen, Linea  and
Kinch, Oliver  and
Jess Pedersen, Anders  and
Lacroix, Oph{\'e}lie},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  editor = {Calzolari, Nicoletta  and
B{\'e}chet, Fr{\'e}d{\'e}ric  and
Blache, Philippe  and
Choukri, Khalid  and
Cieri, Christopher  and
Declerck, Thierry  and
Goggi, Sara  and
Isahara, Hitoshi  and
Maegaard, Bente  and
Mariani, Joseph  and
Mazo, H{\'e}l{\`e}ne  and
Odijk, Jan  and
Piperidis, Stelios},
  month = jun,
  pages = {2440--2445},
  publisher = {European Language Resources Association},
  title = {{DD}is{C}o: A Discourse Coherence Dataset for {D}anish},
  url = {https://aclanthology.org/2022.lrec-1.260},
  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
<details>
  <summary> Dataset Statistics</summary>

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

```python
import mteb

task = mteb.get_task("Ddisco")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "test": {
        "num_samples": 201,
        "number_of_characters": 200062,
        "number_texts_intersect_with_train": 1,
        "min_text_length": 529,
        "average_text_length": 995.3333333333334,
        "max_text_length": 2050,
        "unique_text": 201,
        "unique_labels": 3,
        "labels": {
            "2": {
                "count": 76
            },
            "3": {
                "count": 115
            },
            "1": {
                "count": 10
            }
        }
    },
    "train": {
        "num_samples": 801,
        "number_of_characters": 779241,
        "number_texts_intersect_with_train": null,
        "min_text_length": 492,
        "average_text_length": 972.8352059925094,
        "max_text_length": 2411,
        "unique_text": 796,
        "unique_labels": 3,
        "labels": {
            "1": {
                "count": 30
            },
            "2": {
                "count": 325
            },
            "3": {
                "count": 446
            }
        }
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*