Add dataset card

README.md

@@ -8,7 +8,10 @@ multilinguality: monolingual
 task_categories:
 - text-classification
 task_ids:
-- Sentiment/Hate speech
+- sentiment-analysis
+- sentiment-scoring
+- sentiment-classification
+- hate-speech-detection
 dataset_info:
   features:
   - name: text
@@ -40,7 +43,7 @@ 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;">RuToxicOKMLCUPMultilabelClassification</h1>
+  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">RuToxicOKMLCUPClassification</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>
@@ -61,7 +64,7 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["RuToxicOKMLCUPMultilabelClassification"])
+task = mteb.get_tasks(["RuToxicOKMLCUPClassification"])
 evaluator = mteb.MTEB(task)
 
 model = mteb.get_model(YOUR_MODEL)
@@ -108,7 +111,7 @@ The following code contains the descriptive statistics from the task. These can
 ```python
 import mteb
 
-task = mteb.get_task("RuToxicOKMLCUPMultilabelClassification")
+task = mteb.get_task("RuToxicOKMLCUPClassification")
 
 desc_stats = task.metadata.descriptive_stats
 ```
@@ -124,21 +127,15 @@ desc_stats = task.metadata.descriptive_stats
         "max_text_length": 790,
         "unique_texts": 2000,
         "min_labels_per_text": 1,
-        "average_label_per_text": 1.0885,
-        "max_labels_per_text": 3,
-        "unique_labels": 4,
+        "average_label_per_text": 1.0,
+        "max_labels_per_text": 1,
+        "unique_labels": 2,
         "labels": {
-            "1": {
-                "count": 1000
-            },
             "0": {
-                "count": 810
-            },
-            "3": {
-                "count": 275
+                "count": 1000
             },
-            "2": {
-                "count": 92
+            "1": {
+                "count": 1000
             }
         }
     },
@@ -151,21 +148,15 @@ desc_stats = task.metadata.descriptive_stats
         "max_text_length": 965,
         "unique_texts": 2000,
         "min_labels_per_text": 1,
-        "average_label_per_text": 1.093,
-        "max_labels_per_text": 3,
-        "unique_labels": 4,
+        "average_label_per_text": 1.0,
+        "max_labels_per_text": 1,
+        "unique_labels": 2,
         "labels": {
-            "1": {
-                "count": 1000
-            },
             "0": {
-                "count": 824
-            },
-            "3": {
-                "count": 260
+                "count": 1000
             },
-            "2": {
-                "count": 102
+            "1": {
+                "count": 1000
             }
         }
     }