Add dataset card

README.md

@@ -40,7 +40,7 @@ tags:
 <!-- 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;">RuToxicOKMLCUPClassification</h1>
+  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">RuToxicOKMLCUPMultilabelClassification</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>
@@ -50,7 +50,7 @@ On the Odnoklassniki social network, users post a huge number of comments of var
 |               |                                             |
 |---------------|---------------------------------------------|
 | Task category | t2t                              |
-| Domains       |                                |
+| Domains       | None                               |
 | Reference     | https://cups.online/ru/contests/okmlcup2020 |
 
 
@@ -61,7 +61,7 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["RuToxicOKMLCUPClassification"])
+task = mteb.get_tasks(["RuToxicOKMLCUPMultilabelClassification"])
 evaluator = mteb.MTEB(task)
 
 model = mteb.get_model(YOUR_MODEL)
@@ -108,7 +108,7 @@ The following code contains the descriptive statistics from the task. These can
 ```python
 import mteb
 
-task = mteb.get_task("RuToxicOKMLCUPClassification")
+task = mteb.get_task("RuToxicOKMLCUPMultilabelClassification")
 
 desc_stats = task.metadata.descriptive_stats
 ```
@@ -124,15 +124,21 @@ desc_stats = task.metadata.descriptive_stats
         "max_text_length": 790,
         "unique_texts": 2000,
         "min_labels_per_text": 1,
-        "average_label_per_text": 1.0,
-        "max_labels_per_text": 1,
-        "unique_labels": 2,
+        "average_label_per_text": 1.0885,
+        "max_labels_per_text": 3,
+        "unique_labels": 4,
         "labels": {
-            "0": {
-                "count": 1000
-            },
             "1": {
                 "count": 1000
+            },
+            "0": {
+                "count": 810
+            },
+            "3": {
+                "count": 275
+            },
+            "2": {
+                "count": 92
             }
         }
     },
@@ -145,15 +151,21 @@ desc_stats = task.metadata.descriptive_stats
         "max_text_length": 965,
         "unique_texts": 2000,
         "min_labels_per_text": 1,
-        "average_label_per_text": 1.0,
-        "max_labels_per_text": 1,
-        "unique_labels": 2,
+        "average_label_per_text": 1.093,
+        "max_labels_per_text": 3,
+        "unique_labels": 4,
         "labels": {
-            "0": {
-                "count": 1000
-            },
             "1": {
                 "count": 1000
+            },
+            "0": {
+                "count": 824
+            },
+            "3": {
+                "count": 260
+            },
+            "2": {
+                "count": 102
             }
         }
     }