natural-language-understanding
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Well, Gumbel Distribution is magical. Basically, given a sequence of K logits, i.e., "\log a_1, \log a_2, ..., \log a_K" and K independent gumbel random variables, i.e., "g_1, g_2, ..., g_K". We have
\argmax_i \log a_i + g_i ~ Categorical({a_i / sum(a)})
This gives you a very simple way to sampl
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The architecture
GPT2ForSequenceClassificationwas added in #7501 in PyTorch. It would be great to have it in TensorFlow (cf. issues #7622), but it would also be great to have it for other causal models:OpenAI GPT, CTRL, TransfoXLCurrently working on OpenAI GPT: @fmcurti(done)Below is a list of items to follow to make sure the integration of such an architect