GPT-2 was skilled by using a causal language modeling (CLM) goal and is hence highly effective at predicting another
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tensors for more element. This argument can be used only in eager manner, in graph manner the worth while in the
Whether or not to return the hidden states of all levels. See hidden_states below returned tensors for
This second choice is helpful when employing tf.keras.Design.in shape approach which now requires having all
Resulting from concerns about big language styles being used to produce deceptive, biased, or abusive language at scale, we are only releasing a much scaled-down Variation of GPT-2 as well as sampling code. We aren't releasing the dataset, education code, or GPT-2 model weights. Practically a calendar year back we wrote during the OpenAI Constitution: "we assume that safety and security considerations will cut down our standard publishing Later on, though increasing the necessity of sharing protection, plan, and standards study," and we see this recent get the job done as possibly representing the early beginnings of these my response types of concerns, which we anticipate may increase eventually.
A dictionary that maps interest modules to gadgets. Take note which the embedding module and LMHead are constantly
If you select this second solution, there are 3 possibilities You should utilize to gather the many input Tensors in
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techniques the library implements for all its design (like downloading or preserving, resizing the input embeddings,
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