diff --git a/examples/seq2seq/train.py b/examples/seq2seq/train.py
index 55141a43e8..b9dd0316ce 100644
--- a/examples/seq2seq/train.py
+++ b/examples/seq2seq/train.py
@@ -104,7 +104,9 @@ def get_train_state(rng: PRNGKey, ctable: CTable) -> train_state.TrainState:
   return state
 
 
-def cross_entropy_loss(logits: Array, labels: Array, lengths: Array) -> float:
+def cross_entropy_loss(
+    logits: Array, labels: Array, lengths: Array
+) -> jax.Array:
   """Returns cross-entropy loss."""
   xe = jnp.sum(nn.log_softmax(logits) * labels, axis=-1)
   masked_xe = jnp.mean(mask_sequences(xe, lengths))
@@ -113,7 +115,7 @@ def cross_entropy_loss(logits: Array, labels: Array, lengths: Array) -> float:
 
 def compute_metrics(
     logits: Array, labels: Array, eos_id: int
-) -> Dict[str, float]:
+) -> Dict[str, jax.Array]:
   """Computes metrics and returns them."""
   lengths = get_sequence_lengths(labels, eos_id)
   loss = cross_entropy_loss(logits, labels, lengths)
@@ -134,7 +136,7 @@ def compute_metrics(
 @jax.jit
 def train_step(
     state: train_state.TrainState, batch: Array, lstm_rng: PRNGKey, eos_id: int
-) -> Tuple[train_state.TrainState, Dict[str, float]]:
+) -> Tuple[train_state.TrainState, Dict[str, jax.Array]]:
   """Trains one step."""
   labels = batch['answer'][:, 1:]
   lstm_key = jax.random.fold_in(lstm_rng, state.step)