Add dataset card

README.md

@@ -16,17 +16,26 @@ domains:
 - Written
 ---
 <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
-# AlloprofReranking
+
+<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;">AlloprofReranking</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>
 
 This dataset was provided by AlloProf, an organisation in Quebec, Canada offering resources and a help forum curated by a large number of teachers to students on all subjects taught from in primary and secondary school
 
-> This dataset is included as a task in [`mteb`](https://github.com/embeddings-benchmark/mteb).
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2t                              |
+| Domains       | Web, Academic, Written                               |
+| Reference     | https://huggingface.co/datasets/antoinelb7/alloprof |
 
-- Task category: t2t
-- Domains: ['Web', 'Academic', 'Written']
 
 ## How to evaluate on this task
 
+You can evaluate an embedding model on this dataset using the following code:
+
 ```python
 import mteb
 
@@ -38,23 +47,25 @@ evaluator.run(model)
 ```
 
 <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
-Reference: https://huggingface.co/datasets/antoinelb7/alloprof
+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
+
 @misc{lef23,
-            doi = {10.48550/ARXIV.2302.07738},
-            url = {https://arxiv.org/abs/2302.07738},
-            author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
-            keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
-            title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
-            publisher = {arXiv},
-            year = {2023},
-            copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
-            }
+  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
+  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International},
+  doi = {10.48550/ARXIV.2302.07738},
+  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  publisher = {arXiv},
+  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
+  url = {https://arxiv.org/abs/2302.07738},
+  year = {2023},
+}
+
 
 @article{enevoldsen2025mmtebmassivemultilingualtext,
   title={MMTEB: Massive Multilingual Text Embedding Benchmark},
@@ -78,6 +89,19 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 ```
 
 # 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("AlloprofReranking")
+
+desc_stats = task.metadata.descriptive_stats
+```
+
 ```json
 {
     "test": {
@@ -110,4 +134,9 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
         "max_top_ranked_per_query": 37
     }
 }
-```
+```
+
+</details>
+
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*