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Fix for sequence regression fit() in TF (#19316)

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Matt 2022-10-04 14:48:27 +01:00 committed by GitHub
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src/transformers/modeling_tf_utils.py
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@ -274,6 +274,9 @@ class TFSequenceClassificationLoss:
def hf_compute_loss(self, labels, logits): def hf_compute_loss(self, labels, logits):
if logits.shape.rank == 1 or logits.shape[1] == 1: if logits.shape.rank == 1 or logits.shape[1] == 1:
loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)
if labels.shape.rank == 1:
# MeanSquaredError returns a scalar loss if the labels are 1D, so avoid that
labels = tf.expand_dims(labels, axis=-1)
else: else:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE from_logits=True, reduction=tf.keras.losses.Reduction.NONE