Add dataset card

README.md

@@ -1,8 +1,12 @@
 ---
+annotations_creators:
+- derived
 language:
 - eng
 license: cc-by-sa-4.0
 multilinguality: monolingual
+source_datasets:
+- mteb/arguana
 task_categories:
 - text-retrieval
 task_ids: []
@@ -68,13 +72,16 @@ configs:
   <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
 </div>
 
-NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval
+ArguAna: Retrieval of the Best Counterargument without Prior Topic Knowledge
 
 |               |                                             |
 |---------------|---------------------------------------------|
-| Task category | t2t                              |
-| Domains       | Medical, Written                               |
-| Reference     | http://argumentation.bplaced.net/arguana/data |
+| Task category | Retrieval (text-to-text)                              |
+| Domains       | Social, Web, Written                               |
+| Reference     | [ACL](http://argumentation.bplaced.net/arguana/data) |
+
+Source datasets:
+- [mteb/arguana](https://huggingface.co/datasets/mteb/arguana)
 
 
 ## How to evaluate on this task
@@ -84,15 +91,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["ArguAna"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("ArguAna")
+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). 
+To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
 
 ## Citation
 
@@ -100,15 +107,11 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 
 ```bibtex
 
-@inproceedings{boteva2016,
-  author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
-  city = {Padova},
-  country = {Italy},
-  journal = {Proceedings of the 38th European Conference on Information Retrieval},
-  journal-abbrev = {ECIR},
-  title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
-  url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf},
-  year = {2016},
+@inproceedings{wachsmuth2018retrieval,
+  author = {Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno},
+  booktitle = {ACL},
+  title = {Retrieval of the Best Counterargument without Prior Topic Knowledge},
+  year = {2018},
 }
 
 
@@ -123,7 +126,7 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 }
 
 @article{muennighoff2022mteb,
-  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+  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},
@@ -151,32 +154,31 @@ desc_stats = task.metadata.descriptive_stats
 {
     "test": {
         "num_samples": 10080,
-        "number_of_characters": 10613204,
-        "num_documents": 8674,
-        "min_document_length": 3,
-        "average_document_length": 1030.2327645838136,
-        "max_document_length": 6674,
-        "unique_documents": 8674,
-        "num_queries": 1406,
-        "min_query_length": 251,
-        "average_query_length": 1192.7204836415362,
-        "max_query_length": 5500,
-        "unique_queries": 1406,
-        "none_queries": 0,
-        "num_relevant_docs": 1406,
-        "min_relevant_docs_per_query": 1,
-        "average_relevant_docs_per_query": 1.0,
-        "max_relevant_docs_per_query": 1,
-        "unique_relevant_docs": 1406,
-        "num_instructions": null,
-        "min_instruction_length": null,
-        "average_instruction_length": null,
-        "max_instruction_length": null,
-        "unique_instructions": null,
-        "num_top_ranked": null,
-        "min_top_ranked_per_query": null,
-        "average_top_ranked_per_query": null,
-        "max_top_ranked_per_query": null
+        "number_of_characters": 10607229,
+        "documents_text_statistics": {
+            "total_text_length": 8930264,
+            "min_text_length": 2,
+            "average_text_length": 1029.5439243716855,
+            "max_text_length": 6673,
+            "unique_texts": 8626
+        },
+        "documents_image_statistics": null,
+        "queries_text_statistics": {
+            "total_text_length": 1676965,
+            "min_text_length": 251,
+            "average_text_length": 1192.7204836415362,
+            "max_text_length": 5500,
+            "unique_texts": 1298
+        },
+        "queries_image_statistics": null,
+        "relevant_docs_statistics": {
+            "num_relevant_docs": 1406,
+            "min_relevant_docs_per_query": 1,
+            "average_relevant_docs_per_query": 1.0,
+            "max_relevant_docs_per_query": 1,
+            "unique_relevant_docs": 1406
+        },
+        "top_ranked_statistics": null
     }
 }
 ```