diff --git a/netam/common.py b/netam/common.py
index 93bae1b5..c50200a3 100644
--- a/netam/common.py
+++ b/netam/common.py
@@ -387,18 +387,18 @@ def encode_sequences(sequences, encoder):
 
 # from https://docs.python.org/3.11/library/itertools.html#itertools-recipes
 # avoiding walrus:
-def batched(iterable, n):
-    "Batch data into lists of length n. The last batch may be shorter."
+def chunked(iterable, n):
+    "Chunk data into lists of length n. The last chunk may be shorter."
     it = iter(iterable)
     while True:
-        batch = list(islice(it, n))
-        if not batch:
+        chunk = list(islice(it, n))
+        if not chunk:
             return
-        yield batch
+        yield chunk
 
 
-def batch_method(default_batch_size=2048, progress_bar_name=None):
-    """Decorator to batch the input to a method.
+def chunk_method(default_chunk_size=2048, progress_bar_name=None):
+    """Decorator to chunk the input to a method.
 
     Expects that all positional arguments are iterables of the same length,
     and that outputs are tuples of tensors whose first dimension
@@ -406,30 +406,30 @@ def batch_method(default_batch_size=2048, progress_bar_name=None):
 
     If method returns just one item, it must not be a tuple.
 
-    Batching is done along the first dimension of all inputs.
+    Chunking is done along the first dimension of all inputs.
 
     Args:
-        default_batch_size: The default batch size. The decorated method can
-            also automatically accept a `default_batch_size` keyword argument.
+        default_chunk_size: The default chunk size. The decorated method can
+            also automatically accept a `default_chunk_size` keyword argument.
         progress_bar_name: The name of the progress bar. If None, no progress bar is shown.
     """
 
     def decorator(method):
         @wraps(method)
         def wrapper(self, *args, **kwargs):
-            if "batch_size" in kwargs:
-                batch_size = kwargs.pop("batch_size")
+            if "chunk_size" in kwargs:
+                chunk_size = kwargs.pop("chunk_size")
             else:
-                batch_size = default_batch_size
+                chunk_size = default_chunk_size
             results = []
             if progress_bar_name is None:
                 progargs = {"disable": True}
             else:
                 progargs = {"desc": progress_bar_name}
             bar = tqdm(total=len(args[0]), delay=2.0, **progargs)
-            for batched_args in zip(*(batched(arg, batch_size) for arg in args)):
-                bar.update(len(batched_args[0]))
-                results.append(method(self, *batched_args, **kwargs))
+            for chunked_args in zip(*(chunked(arg, chunk_size) for arg in args)):
+                bar.update(len(chunked_args[0]))
+                results.append(method(self, *chunked_args, **kwargs))
             if isinstance(results[0], tuple):
                 return tuple(torch.cat(tensors) for tensors in zip(*results))
             else:
diff --git a/netam/models.py b/netam/models.py
index 08360200..93293b8e 100644
--- a/netam/models.py
+++ b/netam/models.py
@@ -17,7 +17,7 @@
     generate_kmers,
     aa_mask_tensor_of,
     encode_sequences,
-    batch_method,
+    chunk_method,
 )
 
 warnings.filterwarnings(
@@ -65,8 +65,8 @@ def unfreeze(self):
         for param in self.parameters():
             param.requires_grad = True
 
-    @batch_method(progress_bar_name="Evaluating model")
-    def evaluate_sequences(self, sequences, encoder=None, batch_size=2048):
+    @chunk_method(progress_bar_name="Evaluating model")
+    def evaluate_sequences(self, sequences, encoder=None, chunk_size=2048):
         if encoder is None:
             raise ValueError("An encoder must be provided.")
         encoded_parents, masks, wt_base_modifiers = encode_sequences(sequences, encoder)