Add dataset card

README.md

@@ -5,6 +5,8 @@ language:
 - eng
 license: mit
 multilinguality: monolingual
+source_datasets:
+- jhu-clsp/core17-instructions-mteb
 task_categories:
 - text-ranking
 task_ids: []
@@ -116,6 +118,9 @@ Measuring retrieval instruction following ability on Core17 narratives for the F
 | Domains       | News, Written                               |
 | Reference     | https://arxiv.org/abs/2403.15246 |
 
+Source datasets:
+- [jhu-clsp/core17-instructions-mteb](https://huggingface.co/datasets/jhu-clsp/core17-instructions-mteb)
+
 
 ## How to evaluate on this task
 
@@ -124,15 +129,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["Core17InstructionRetrieval"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("Core17InstructionRetrieval")
+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
 
@@ -161,7 +166,7 @@ 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},
@@ -189,32 +194,36 @@ desc_stats = task.metadata.descriptive_stats
 {
     "test": {
         "num_samples": 19939,
-        "number_of_characters": 44459412,
-        "num_documents": 19899,
-        "min_document_length": 8,
-        "average_document_length": 2234.0329664807277,
-        "max_document_length": 2960,
-        "unique_documents": 19899,
-        "num_queries": 40,
-        "min_query_length": 55,
-        "average_query_length": 109.75,
-        "max_query_length": 278,
-        "unique_queries": 40,
-        "none_queries": 0,
-        "num_relevant_docs": 9480,
-        "min_relevant_docs_per_query": 135,
-        "average_relevant_docs_per_query": 43.6,
-        "max_relevant_docs_per_query": 379,
-        "unique_relevant_docs": 4739,
-        "num_instructions": 40,
-        "min_instruction_length": 102,
-        "average_instruction_length": 13015,
-        "max_instruction_length": 837,
-        "unique_instructions": 40,
-        "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": 44471883,
+        "documents_text_statistics": {
+            "total_text_length": 44454438,
+            "min_text_length": 7,
+            "average_text_length": 2234.003618272275,
+            "max_text_length": 2960,
+            "unique_texts": 19143
+        },
+        "documents_image_statistics": null,
+        "queries_text_statistics": {
+            "total_text_length": 17445,
+            "min_text_length": 198,
+            "average_text_length": 436.125,
+            "max_text_length": 1000,
+            "unique_texts": 40
+        },
+        "queries_image_statistics": null,
+        "relevant_docs_statistics": {
+            "num_relevant_docs": 1744,
+            "min_relevant_docs_per_query": 135,
+            "average_relevant_docs_per_query": 43.6,
+            "max_relevant_docs_per_query": 379,
+            "unique_relevant_docs": 4739
+        },
+        "top_ranked_statistics": {
+            "num_top_ranked": 40000,
+            "min_top_ranked_per_query": 1000,
+            "average_top_ranked_per_query": 1000.0,
+            "max_top_ranked_per_query": 1000
+        }
     }
 }
 ```