[Python] why explain logit as 'unscaled log probabililty' in sotfmax_cross_entropy_with_logits?

In the tensorflow documentation (softmax_cross_entropy_with_logits), they said "logits : unscaled log probablilty". What is 'log probability'? First, I understand that 'logits' is an 'output before normalization' or a 'score for class'.

logits = tf.matmul(X,W) + b
hypothesis = tf.nn.softmax(logits)


If I got [1.5, 2.4, 0,7] by tf.matmul(X,W) + b, then [1.5, 2.4, 0,7] is logits(score) and this was unscaled. I can understand it up to this stage. But, I can't understand why [1.5, 2.4, 0.7] is 'log probability'.

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