diff --git a/lm-eval-harness/slovak_toxic_classification/__init__.py b/lm-eval-harness/slovak_toxic_classification/__init__.py
new file mode 100644
index 0000000..79970b2
--- /dev/null
+++ b/lm-eval-harness/slovak_toxic_classification/__init__.py
@@ -0,0 +1,73 @@
+from lm_eval.api.task import Task
+from datasets import load_dataset
+from sklearn.metrics import precision_recall_fscore_support
+
+class SlovakToxicClassification(Task):
+    VERSION = 1
+    DATASET_PATH = "TUKE-KEMT/hate_speech_slovak"
+    DATASET_NAME = None
+
+    def __init__(self):
+        # Загружаем датасет
+        self.dataset = load_dataset(self.DATASET_PATH)
+    
+    def has_training_docs(self):
+        return False
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return True
+
+    def validation_docs(self):
+        return list(self.dataset["test"]) 
+
+    def test_docs(self):
+        return list(self.dataset["test"])
+
+    def doc_to_text(self, doc):
+        return doc["text"]  
+
+    def doc_to_target(self, doc):
+        return int(doc["label"])  
+
+    def construct_requests(self, doc, ctx):
+        
+        return self.loglikelihood(ctx, " 0"), self.loglikelihood(ctx, " 1")
+
+    def process_results(self, doc, results):
+        ll0, ll1 = results
+        pred = int(ll1 > ll0)
+        gold = self.doc_to_target(doc)
+        return {
+            "f1": (gold, pred),
+            "precision": (gold, pred),
+            "recall": (gold, pred),
+        }
+
+    def aggregation(self):
+        return {
+            "f1": self.f1_score,
+            "precision": self.precision_score,
+            "recall": self.recall_score,
+        }
+
+    def higher_is_better(self):
+        return {
+            "f1": True,
+            "precision": True,
+            "recall": True,
+        }
+
+    def f1_score(self, gold_and_pred):
+        golds, preds = zip(*gold_and_pred)
+        return precision_recall_fscore_support(golds, preds, average="binary")[2]
+
+    def precision_score(self, gold_and_pred):
+        golds, preds = zip(*gold_and_pred)
+        return precision_recall_fscore_support(golds, preds, average="binary")[0]
+
+    def recall_score(self, gold_and_pred):
+        golds, preds = zip(*gold_and_pred)
+        return precision_recall_fscore_support(golds, preds, average="binary")[1]