Add dataset card

README.md

@@ -1,92 +1,151 @@
 ---
-dataset_info:
-- config_name: corpus
-  features:
-  - name: _id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: title
-    dtype: string
-  splits:
-  - name: validation
-    num_bytes: 14082989
-    num_examples: 118681
-  - name: test
-    num_bytes: 13338796
-    num_examples: 118600
-  download_size: 13524910
-  dataset_size: 27421785
-- config_name: default
-  features:
-  - name: query-id
-    dtype: string
-  - name: corpus-id
-    dtype: string
-  - name: score
-    dtype: int64
-  splits:
-  - name: validation
-    num_bytes: 8379567
-    num_examples: 118681
-  - name: test
-    num_bytes: 6950563
-    num_examples: 118600
-  download_size: 1632642
-  dataset_size: 15330130
-- config_name: queries
-  features:
-  - name: _id
-    dtype: string
-  - name: text
-    dtype: string
-  splits:
-  - name: validation
-    num_bytes: 384286
-    num_examples: 3981
-  - name: test
-    num_bytes: 364609
-    num_examples: 3978
-  download_size: 437805
-  dataset_size: 748895
-- config_name: top_ranked
-  features:
-  - name: query-id
-    dtype: string
-  - name: corpus-ids
-    sequence: string
-  splits:
-  - name: validation
-    num_bytes: 4725322
-    num_examples: 3981
-  - name: test
-    num_bytes: 3986587
-    num_examples: 3978
-  download_size: 1589577
-  dataset_size: 8711909
-configs:
-- config_name: corpus
-  data_files:
-  - split: validation
-    path: corpus/validation-*
-  - split: test
-    path: corpus/test-*
-- config_name: default
-  data_files:
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-- config_name: queries
-  data_files:
-  - split: validation
-    path: queries/validation-*
-  - split: test
-    path: queries/test-*
-- config_name: top_ranked
-  data_files:
-  - split: validation
-    path: top_ranked/validation-*
-  - split: test
-    path: top_ranked/test-*
+language:
+- eng
+license: cc-by-4.0
+multilinguality: monolingual
+source_datasets:
+- mteb/scidocs
+task_categories:
+- text-ranking
+task_ids:
+- Scientific Reranking
+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;">SciDocsRR</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>
+
+Ranking of related scientific papers based on their title.
+
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2t                              |
+| Domains       | Academic, Non-fiction, Written                               |
+| Reference     | https://allenai.org/data/scidocs |
+
+
+## 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(["SciDocsRR"])
+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{cohan-etal-2020-specter,
+    title = "{SPECTER}: Document-level Representation Learning using Citation-informed Transformers",
+    author = "Cohan, Arman  and
+      Feldman, Sergey  and
+      Beltagy, Iz  and
+      Downey, Doug  and
+      Weld, Daniel",
+    editor = "Jurafsky, Dan  and
+      Chai, Joyce  and
+      Schluter, Natalie  and
+      Tetreault, Joel",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.acl-main.207",
+    doi = "10.18653/v1/2020.acl-main.207",
+    pages = "2270--2282",
+    abstract = "Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.",
+}
+
+
+@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("SciDocsRR")
+
+desc_stats = task.metadata.descriptive_stats
+```
+
+```json
+{
+    "test": {
+        "num_samples": 122578,
+        "number_of_characters": 8761260,
+        "num_documents": 118600,
+        "min_document_length": 8,
+        "average_document_length": 71.52865935919056,
+        "max_document_length": 300,
+        "unique_documents": 118600,
+        "num_queries": 3978,
+        "min_query_length": 13,
+        "average_query_length": 69.87456008044244,
+        "max_query_length": 300,
+        "unique_queries": 3978,
+        "none_queries": 0,
+        "num_relevant_docs": 118600,
+        "min_relevant_docs_per_query": 26,
+        "average_relevant_docs_per_query": 4.92684766214178,
+        "max_relevant_docs_per_query": 60,
+        "unique_relevant_docs": 118600,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": 3978,
+        "min_top_ranked_per_query": 26,
+        "average_top_ranked_per_query": 29.813976872800403,
+        "max_top_ranked_per_query": 60
+    }
+}
+```
+
+</details>
+
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*