diff --git a/data/collate/data_collate_factory.py b/data/collate/data_collate_factory.py
index 999a1a78786041461866834a284250df48a69ec1..20507fcaac1164060a74aeb28409fd7ab124b451 100644
--- a/data/collate/data_collate_factory.py
+++ b/data/collate/data_collate_factory.py
@@ -17,7 +17,7 @@ def create_data_collate(architecture, embeddings, resolution):
     """
     if architecture == "autoencoder":
         return AutoencoderCollate(embeddings)
-    if architecture in ("encoder_only", 'pytorch', 'fast', 'fast-recurrent', 'sliding-window'):
+    if architecture in ("encoder_only", 'pytorch', 'fast', 'fast-recurrent', 'fast_recurrent', 'sliding_window'):
         return EncoderOnlyCollate()
     if architecture == "encoder_decoder":
         return EncoderDecoderCollate(embeddings)
diff --git a/modules/architecture/architecture_factory.py b/modules/architecture/architecture_factory.py
index ed041d9a4b63c279130e64b7774f5dc2273096de..4f2b0a2f2c53745eb60f64676c5a777f8b148d87 100644
--- a/modules/architecture/architecture_factory.py
+++ b/modules/architecture/architecture_factory.py
@@ -62,13 +62,13 @@ def create_architecture(
         return Transformer(**kwargs, num_decoders=len(token_embedding) - 1)
     elif architecture == "pytorch":
         return PytorchTransformer(**kwargs)
-    elif architecture == "sliding-window":
+    elif architecture == "sliding_window":
         return SlidingWindowTransformer(**kwargs)
     elif architecture == "fast":
         # include `pytorch-fast-transformers` as an optional module
         from .fast_transformer import FastTransformer
         return FastTransformer(**kwargs)
-    elif architecture == "fast-recurrent":
+    elif architecture in ("fast-recurrent", "fast_recurrent"):
         # include `pytorch-fast-transformers` as an optional module
         from .fast_recurrent_transformer import FastRecurrentTransformer
         return FastRecurrentTransformer(**kwargs)
diff --git a/sample/layer_sampler/recurrent_sampler.py b/sample/layer_sampler/recurrent_sampler.py
index 0d2e1be09986b17ee3ebe4e5537b9aa8cd9b00bd..901ca742b7fbccdb43fd24ca4e78545db50d2d8d 100644
--- a/sample/layer_sampler/recurrent_sampler.py
+++ b/sample/layer_sampler/recurrent_sampler.py
@@ -13,7 +13,7 @@ from ..token_generator.recurrent import create_recurrent_token_generator
 
 class RecurrentSampler:
     def __init__(self, model, head, spatial_dim, max_resolution, position_encoding, device, **_):
-        """ Provides a basic implementation of the sampler for the 'fast-recurrent-transformer' architecture.
+        """ Provides a basic implementation of the sampler for the 'fast_recurrent-transformer' architecture.
 
         Args:
             model: Model which is used for sampling.
diff --git a/sample/layer_sampler/sampler_factory.py b/sample/layer_sampler/sampler_factory.py
index 6a383de75065db0392179f7fb30fa0d36722534c..0d37d9fbc8cb0993352b90eeea7a4459933ffaf7 100644
--- a/sample/layer_sampler/sampler_factory.py
+++ b/sample/layer_sampler/sampler_factory.py
@@ -37,9 +37,9 @@ def create_sampler(
 
     if architecture == "autoencoder":
         return AutoencoderSampler(**kwargs)
-    elif architecture in ("encoder_only", "fast", "pytorch", "sliding-window"):
+    elif architecture in ("encoder_only", "fast", "pytorch", "sliding_window"):
         return EncoderOnlySampler(**kwargs)
-    elif architecture in ("fast-recurrent"):
+    elif architecture in ("fast-recurrent", "fast_recurrent"):
         return RecurrentSampler(**kwargs)
     elif architecture == "encoder_decoder":
         return EncoderDecoderSampler(**kwargs)
diff --git a/sample/shape_sampler.py b/sample/shape_sampler.py
index 37c311f711e24e92a09dee8591c66f7a4fcfd487..4ca127e1a97af0a53949f55d39a403f8e3f69c35 100644
--- a/sample/shape_sampler.py
+++ b/sample/shape_sampler.py
@@ -20,7 +20,7 @@ class ShapeSampler:
         pl_module = ShapeTransformer.load_from_checkpoint(checkpoint_path)
         if fast_recurrent is True and pl_module.hparams['architecture'] == 'fast':
             print("Reload model as a recurrent implementation for a major improvement of inference time.")
-            pl_module = ShapeTransformer.load_from_checkpoint(checkpoint_path, architecture='fast-recurrent')
+            pl_module = ShapeTransformer.load_from_checkpoint(checkpoint_path, architecture='fast_recurrent')
         pl_module.freeze()
 
         # extract hyperparameters from the model