diff --git a/ding/torch_utils/loss/contrastive_loss.py b/ding/torch_utils/loss/contrastive_loss.py
index ed17eebcb1..0871ebdd85 100644
--- a/ding/torch_utils/loss/contrastive_loss.py
+++ b/ding/torch_utils/loss/contrastive_loss.py
@@ -79,7 +79,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor):
         x_n = x.view(-1, self._encode_shape)
         y_n = y.view(-1, self._encode_shape)
 
-        # Use inner product to obtain postive samples.
+        # Use inner product to obtain positive samples.
         # [N, x_heads, encode_dim] * [N, encode_dim, y_heads] -> [N, x_heads, y_heads]
         u_pos = torch.matmul(x, y.permute(0, 2, 1)).unsqueeze(2)
         # Use outer product to obtain all sample permutations.
@@ -92,7 +92,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor):
         u_neg = (n_mask * u_all) - (10. * (1 - n_mask))
         u_neg = u_neg.view(N, N * x_heads, y_heads).unsqueeze(dim=1).expand(-1, x_heads, -1, -1)
 
-        # Concatenate postive and negative samples and apply log softmax.
+        # Concatenate positive and negative samples and apply log softmax.
         pred_lgt = torch.cat([u_pos, u_neg], dim=2)
         pred_log = F.log_softmax(pred_lgt * self._temperature, dim=2)