Add dataset card

README.md

@@ -5,6 +5,8 @@ language:
 - kor
 license: cc-by-sa-4.0
 multilinguality: monolingual
+source_datasets:
+- on-and-on/clustering_klue_mrc_ynat_title
 task_categories:
 - text-classification
 task_ids: []
@@ -45,6 +47,9 @@ this dataset is a processed and redistributed version of the KLUE-Ynat & KLUE-MR
 | Domains       | News, Written                               |
 | Reference     | https://huggingface.co/datasets/on-and-on/clustering_klue_mrc_ynat_title |
 
+Source datasets:
+- [on-and-on/clustering_klue_mrc_ynat_title](https://huggingface.co/datasets/on-and-on/clustering_klue_mrc_ynat_title)
+
 
 ## How to evaluate on this task
 
@@ -53,15 +58,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["KlueYnatMrcCategoryClustering"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("KlueYnatMrcCategoryClustering")
+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
 
@@ -90,7 +95,7 @@ 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},
@@ -117,31 +122,36 @@ desc_stats = task.metadata.descriptive_stats
 ```json
 {
     "test": {
-        "num_samples": 1,
-        "number_of_characters": 904,
-        "min_text_length": 904,
-        "average_text_length": 904.0,
-        "max_text_length": 904,
-        "unique_texts": 904,
-        "min_labels_per_text": 99,
-        "average_labels_per_text": 904.0,
-        "max_labels_per_text": 240,
-        "unique_labels": 5,
-        "labels": {
-            "3": {
-                "count": 173
-            },
-            "2": {
-                "count": 164
-            },
-            "1": {
-                "count": 99
-            },
-            "0": {
-                "count": 240
-            },
-            "5": {
-                "count": 228
+        "num_samples": 904,
+        "text_statistics": {
+            "total_text_length": 30703,
+            "min_text_length": 21,
+            "average_text_length": 33.96349557522124,
+            "max_text_length": 89,
+            "unique_texts": 904
+        },
+        "image_statistics": null,
+        "label_statistics": {
+            "min_labels_per_text": 1,
+            "average_label_per_text": 1.0,
+            "max_labels_per_text": 1,
+            "unique_labels": 5,
+            "labels": {
+                "3": {
+                    "count": 173
+                },
+                "2": {
+                    "count": 164
+                },
+                "1": {
+                    "count": 99
+                },
+                "0": {
+                    "count": 240
+                },
+                "5": {
+                    "count": 228
+                }
             }
         }
     }