--- a
+++ b/docproduct/loss.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+
+def qa_pair_loss(y_true, y_pred):
+    y_true = tf.eye(tf.shape(y_pred)[0])*2-1
+    q_embedding, a_embedding = tf.unstack(y_pred, axis=1)
+    q_embedding = q_embedding / \
+        tf.norm(q_embedding, axis=-1, keepdims=True)
+    a_embedding = a_embedding / \
+        tf.norm(a_embedding, axis=-1, keepdims=True)
+    similarity_matrix = tf.matmul(
+        q_embedding, a_embedding, transpose_b=True)
+    return tf.reduce_mean(tf.norm(y_true - similarity_matrix, axis=-1))
+
+
+def qa_pair_cross_entropy_loss(y_true, y_pred):
+    y_true = tf.eye(tf.shape(y_pred)[0])
+    q_embedding, a_embedding = tf.unstack(y_pred, axis=1)
+    similarity_matrix = tf.matmul(
+        a=q_embedding, b=a_embedding, transpose_b=True)
+    similarity_matrix_softmaxed = tf.nn.softmax(similarity_matrix)
+    return tf.keras.losses.categorical_crossentropy(y_true, similarity_matrix_softmaxed, from_logits=False)