From 9e3c32ba3844d82db43e373706a79d4dbc1f84c8 Mon Sep 17 00:00:00 2001
From: Tetiana Mohorian <tetiana.mohorian@student.tuke.sk>
Date: Mon, 12 May 2025 07:37:02 +0000
Subject: [PATCH] Aktualizovat
 lm-eval-harness/slovak_toxic_classification/__init__.py

---
 .../slovak_toxic_classification/__init__.py   | 145 +++++++++---------
 1 file changed, 72 insertions(+), 73 deletions(-)

diff --git a/lm-eval-harness/slovak_toxic_classification/__init__.py b/lm-eval-harness/slovak_toxic_classification/__init__.py
index 79970b2..50bae80 100644
--- a/lm-eval-harness/slovak_toxic_classification/__init__.py
+++ b/lm-eval-harness/slovak_toxic_classification/__init__.py
@@ -1,73 +1,72 @@
-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]
+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):
+        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]