diff --git a/tti_eval/cli/main.py b/tti_eval/cli/main.py
index 5b8862e..93be060 100644
--- a/tti_eval/cli/main.py
+++ b/tti_eval/cli/main.py
@@ -58,7 +58,7 @@ def build_command(
                 embd_defn.save_embeddings(embeddings=embeddings, split=split, overwrite=True)
                 print(f"Embeddings saved successfully to file at `{embd_defn.embedding_path(split)}`")
             except Exception as e:
-                print(f"Failed to build embeddings for this bastard: {embd_defn}")
+                print(f"Failed to build embeddings for {embd_defn} on the specified split {split}")
                 print(e)
                 import traceback
 
diff --git a/tti_eval/dataset/types/encord_ds.py b/tti_eval/dataset/types/encord_ds.py
index bfaec85..4c70b21 100644
--- a/tti_eval/dataset/types/encord_ds.py
+++ b/tti_eval/dataset/types/encord_ds.py
@@ -108,8 +108,8 @@ def _setup(
         ssh_key_path = ssh_key_path or os.getenv("ENCORD_SSH_KEY_PATH")
         if ssh_key_path is None:
             raise ValueError(
-                "The `ssh_key_path` parameter and the `ENCORD_SSH_KEY_PATH` environment variable are both missing."
-                "Please set one of them to proceed"
+                "The `ssh_key_path` parameter and the `ENCORD_SSH_KEY_PATH` environment variable are both missing. "
+                "Please set one of them to proceed."
             )
         client = EncordUserClient.create_with_ssh_private_key(ssh_private_key_path=ssh_key_path)
         self._project = client.get_project(project_hash)
diff --git a/tti_eval/dataset/utils.py b/tti_eval/dataset/utils.py
index 21ae9a0..ee6d736 100644
--- a/tti_eval/dataset/utils.py
+++ b/tti_eval/dataset/utils.py
@@ -95,16 +95,25 @@ def simple_random_split(
     :raises ValueError: If the sum of `train_split` and `validation_split` is greater than 1,
         or if `train_split` or `validation_split` are less than 0.
     """
+    if dataset_size < 3:
+        raise ValueError(f"Expected a dataset with size at least 3, got {dataset_size}")
+
     if train_split < 0 or validation_split < 0:
         raise ValueError(f"Expected positive splits, got ({train_split=}, {validation_split=})")
-    if train_split + validation_split > 1:
+    if train_split + validation_split >= 1:
         raise ValueError(
             f"Expected `train_split` and `validation_split` sum between 0 and 1, got {train_split + validation_split}"
         )
     rng = np.random.default_rng(seed)
     selection = rng.permutation(dataset_size)
-    train_size = int(dataset_size * train_split)
-    validation_size = int(dataset_size * validation_split)
+    train_size = max(1, int(dataset_size * train_split))
+    validation_size = max(1, int(dataset_size * validation_split))
+    # Ensure that the TEST split has at least an element
+    if train_size + validation_size == dataset_size:
+        if train_size > 1:
+            train_size -= 1
+        if validation_size > 1:
+            validation_size -= 1
     return {
         Split.TRAIN: selection[:train_size],
         Split.VALIDATION: selection[train_size : train_size + validation_size],