diff --git a/src/text_to_multi_option_benchmark.py b/src/text_to_multi_option_benchmark.py
index 59f1dc4..d80c6f1 100644
--- a/src/text_to_multi_option_benchmark.py
+++ b/src/text_to_multi_option_benchmark.py
@@ -20,7 +20,9 @@
     TextToMultiOptionExtractor,
 )
 
-LABELED_DATA_PATH = join(Path(__file__).parent, "extractors", "text_to_multi_option_extractor", "labeled_data")
+LABELED_DATA_PATH = join(
+    Path(__file__).parent, "trainable_entity_extractor", "extractors", "text_to_multi_option_extractor", "labeled_data"
+)
 
 
 def get_extraction_data(filter_by: list[str] = None):
@@ -38,7 +40,10 @@ def get_extraction_data(filter_by: list[str] = None):
         for i, text_value in enumerate(texts_values):
             values = [Option(id=x, label=x) for x in text_value["values"]]
             language_iso = "es" if "cejil" in task_name else "en"
-            labeled_data = LabeledData(values=values, entity_name=str(i), language_iso=language_iso)
+            labeled_data = LabeledData(
+                values=values, entity_name=str(i), language_iso=language_iso, source_text=text_value["text"]
+            )
+
             extraction_sample = TrainingSample(segment_selector_texts=[text_value["text"]], labeled_data=labeled_data)
             samples.append(extraction_sample)
 
@@ -72,14 +77,15 @@ def get_benchmark():
     for extraction_data in extractions_data:
         start = time()
         extractor = TextToMultiOptionExtractor(extraction_identifier=extraction_data.extraction_identifier)
-        train_set, test_set = ExtractorBase.get_train_test_sets(extraction_data, limit_samples=False)
+        train_set, test_set = ExtractorBase.get_train_test_sets(extraction_data)
         values_list = [x.labeled_data.values for x in test_set.samples]
         truth_one_hot = PdfMultiOptionMethod.one_hot_to_options_list(values_list, extraction_data.options)
         extractor.create_model(train_set)
 
         tags_texts = [x.segment_selector_texts for x in test_set.samples]
         test_data = [
-            PredictionSample(segment_selector_texts=tag_text, entity_name=str(i)) for i, tag_text in enumerate(tags_texts)
+            PredictionSample(segment_selector_texts=tag_text, entity_name=str(i), source_text=" ".join(tag_text))
+            for i, tag_text in enumerate(tags_texts)
         ]
         suggestions = extractor.get_suggestions(test_data)
         values_list = [x.values for x in suggestions]
diff --git a/src/trainable_entity_extractor/extractors/text_to_multi_option_extractor/results/results.txt b/src/trainable_entity_extractor/extractors/text_to_multi_option_extractor/results/results.txt
index fd32974..cab7ab8 100644
--- a/src/trainable_entity_extractor/extractors/text_to_multi_option_extractor/results/results.txt
+++ b/src/trainable_entity_extractor/extractors/text_to_multi_option_extractor/results/results.txt
@@ -11,7 +11,7 @@ Extractor             cejil_countries                                 5.5 94.12%
 Extractor             cyrilla_keywords [TextSetFit]                   4.0 68.29%
 Extractor             cejil_judge                                     0.0 100.0%
 Extractor             action [TextSingleLabelSetFit]                 10.1  88.3%
-Extractor             issues [Setfit]                                13.0  76.7%
+Extractor             issues [Setfit]                                27.7  78.3%
 Extractor             issues [BertBase]                                 -  70.8%
 Extractor             issues [BertLarge]                             98.3  90.2%
 Extractor             themes [Setfit]                                 5.3  66.1%