Add BartTokenizer and BART Presets #685
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mattdangerw
reviewed
Jan 24, 2023
| Examples: | ||
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| Batched inputs. | ||
| >>> vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "reful": 3, "gent": 4} |
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Can we shrink these vocabs down? They end up distracting from the actual tokenizer, where we want the attention to be. I was playing around with some shorter examples on #653 , e.g. https://github.com/keras-team/keras-nlp/blob/dc62952b023602fde8e5c2373894a449be15265f/keras_nlp/models/roberta/roberta_preprocessor.py#L135-L149
mattdangerw
reviewed
Jan 25, 2023
| def presets(cls): | ||
| return copy.deepcopy(backbone_presets) | ||
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| @classmethod |
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Also, we have no done the base class stuff for tokenizer! Though slightly different as there are many tokenizer types. #673
You can remove all this.
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