use different dicts for v1-v2 emotion9 predictions

interfaces/emotion9.py

@@ -7,7 +7,7 @@ from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
 from huggingface_hub import HfApi
 
-from label_dicts import EMOTION9_LABEL_NAMES
+from label_dicts import EMOTION9_LABEL_NAMES, EMOTION9_V2_LABEL_NAMES
 
 from .utils import is_disk_full, release_model
 
@@ -20,10 +20,15 @@ domains = {
     "parliamentary speech": "parlspeech",
 }
 
+USE_V2 = False
+if language in ["slovak", "polish", "czech", "hungarian"]:
+    EMOTION9_LABEL_NAMES = EMOTION9_V2_LABEL_NAMES
+    USE_V2 = True
+
 def build_huggingface_path(language: str):
     language = language.lower()
     base_model_id = f"poltextlab/xlm-roberta-large-pooled-{language}-emotions9"
-    if language in ["slovak", "polish", "czech", "hungarian"]:
+    if USE_V2:
         return base_model_id + "-v2"
     return base_model_id