diff --git a/wtpsplit/evaluation/intrinsic_list.py b/wtpsplit/evaluation/intrinsic_list.py
index 9620ed4e..ea867b6e 100644
--- a/wtpsplit/evaluation/intrinsic_list.py
+++ b/wtpsplit/evaluation/intrinsic_list.py
@@ -143,6 +143,10 @@ def load_or_compute_logits(args, model, eval_data, valid_data=None, save_str: st
                             for n, p in model.backbone.named_parameters():
                                 if "LayerNorm" in n:
                                     p.data = ln_dict[n].data
+                    if not os.path.exists(os.path.join(args.model_path, "pytorch_model.bin")):
+                        model_path = os.path.join(args.model_path, dataset_name, "en")
+                        print(model_path)
+                        model = PyTorchWrapper(AutoModelForTokenClassification.from_pretrained(model_path).to(args.device))
                 except Exception as e:
                     print(f"Error loading adapter for {dataset_name} in {lang_code}: {e}")
                     continue
@@ -276,7 +280,14 @@ def main(args):
         valid_data = None
 
     print("Loading model...")
-    model = PyTorchWrapper(AutoModelForTokenClassification.from_pretrained(args.model_path).to(args.device))
+    # if model_path does not contain a model, take first subfolder
+    if not os.path.exists(os.path.join(args.model_path, "pytorch_model.bin")):
+        model_path = os.path.join(args.model_path, os.listdir(args.model_path)[0], "en")
+        print("joined")
+        print(model_path)
+    else:
+        model_path = args.model_path
+    model = PyTorchWrapper(AutoModelForTokenClassification.from_pretrained(model_path).to(args.device))
     if args.adapter_path:
         model_type = model.model.config.model_type
         # adapters need xlm-roberta as model type.