Add dataset card

README.md

@@ -5,6 +5,8 @@ language:
 - eng
 license: cc-by-nc-sa-4.0
 multilinguality: monolingual
+source_datasets:
+- McGill-NLP/mlquestions
 task_categories:
 - text-retrieval
 task_ids:
@@ -94,9 +96,12 @@ MLQuestions is a domain adaptation dataset for the machine learning domainIt con
 
 |               |                                             |
 |---------------|---------------------------------------------|
-| Task category | t2t                              |
+| Task category | Retrieval (text-to-text)                              |
 | Domains       | Encyclopaedic, Academic, Written                               |
-| Reference     | https://github.com/McGill-NLP/MLQuestions |
+| Reference     | [Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing](https://github.com/McGill-NLP/MLQuestions) |
+
+Source datasets:
+- [McGill-NLP/mlquestions](https://huggingface.co/datasets/McGill-NLP/mlquestions)
 
 
 ## How to evaluate on this task
@@ -106,15 +111,13 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["MLQuestions"])
-evaluator = mteb.MTEB(task)
-
+task = mteb.get_task("MLQuestions")
 model = mteb.get_model(YOUR_MODEL)
-evaluator.run(model)
+mteb.evaluate(model, task)
 ```
 
 <!-- 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
 
@@ -123,7 +126,6 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 ```bibtex
 
 @inproceedings{kulshreshtha-etal-2021-back,
-  abstract = {In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.},
   address = {Online and Punta Cana, Dominican Republic},
   author = {Kulshreshtha, Devang  and
 Belfer, Robert  and
@@ -150,7 +152,7 @@ Reddy, Siva},
 }
 
 @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},
@@ -179,60 +181,58 @@ desc_stats = task.metadata.descriptive_stats
     "dev": {
         "num_samples": 12500,
         "number_of_characters": 2915233,
-        "num_documents": 11000,
-        "min_document_length": 3,
-        "average_document_length": 258.8772727272727,
-        "max_document_length": 395,
-        "unique_documents": 11000,
-        "num_queries": 1500,
-        "min_query_length": 14,
-        "average_query_length": 45.05533333333333,
-        "max_query_length": 160,
-        "unique_queries": 1500,
-        "none_queries": 0,
-        "num_relevant_docs": 1500,
-        "min_relevant_docs_per_query": 1,
-        "average_relevant_docs_per_query": 1.0,
-        "max_relevant_docs_per_query": 1,
-        "unique_relevant_docs": 1500,
-        "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
+        "documents_text_statistics": {
+            "total_text_length": 2847650,
+            "min_text_length": 3,
+            "average_text_length": 258.8772727272727,
+            "max_text_length": 395,
+            "unique_texts": 9211
+        },
+        "documents_image_statistics": null,
+        "queries_text_statistics": {
+            "total_text_length": 67583,
+            "min_text_length": 14,
+            "average_text_length": 45.05533333333333,
+            "max_text_length": 160,
+            "unique_texts": 1500
+        },
+        "queries_image_statistics": null,
+        "relevant_docs_statistics": {
+            "num_relevant_docs": 1500,
+            "min_relevant_docs_per_query": 1,
+            "average_relevant_docs_per_query": 1.0,
+            "max_relevant_docs_per_query": 1,
+            "unique_relevant_docs": 1500
+        },
+        "top_ranked_statistics": null
     },
     "test": {
         "num_samples": 12500,
         "number_of_characters": 2916280,
-        "num_documents": 11000,
-        "min_document_length": 3,
-        "average_document_length": 258.8772727272727,
-        "max_document_length": 395,
-        "unique_documents": 11000,
-        "num_queries": 1500,
-        "min_query_length": 12,
-        "average_query_length": 45.75333333333333,
-        "max_query_length": 165,
-        "unique_queries": 1500,
-        "none_queries": 0,
-        "num_relevant_docs": 1500,
-        "min_relevant_docs_per_query": 1,
-        "average_relevant_docs_per_query": 1.0,
-        "max_relevant_docs_per_query": 1,
-        "unique_relevant_docs": 1499,
-        "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
+        "documents_text_statistics": {
+            "total_text_length": 2847650,
+            "min_text_length": 3,
+            "average_text_length": 258.8772727272727,
+            "max_text_length": 395,
+            "unique_texts": 9211
+        },
+        "documents_image_statistics": null,
+        "queries_text_statistics": {
+            "total_text_length": 68630,
+            "min_text_length": 12,
+            "average_text_length": 45.75333333333333,
+            "max_text_length": 165,
+            "unique_texts": 1499
+        },
+        "queries_image_statistics": null,
+        "relevant_docs_statistics": {
+            "num_relevant_docs": 1500,
+            "min_relevant_docs_per_query": 1,
+            "average_relevant_docs_per_query": 1.0,
+            "max_relevant_docs_per_query": 1,
+            "unique_relevant_docs": 1499
+        },
+        "top_ranked_statistics": null
     }
 }
 ```