keras-nlp
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Modular Natural Language Processing workflows with Keras
An implementation of OpenAI's BytePair encoder in TF compatible graph mode, which would allow for the e2e development of certain pretrained models that use this tokenizer (RoBERTa, GPT etc.). Currently...
**Describe the bug** ByteTokenizer only takes integers for some parameters. Which is fine. But it doesn't fail until runtime - which makes it seem like the input is the issue...
We have numerous checkpoints for text encoders but there's a lot of value in offering ready-to-go fine tuned models as well. Thoughts: * Let's start with fine-tuning on SST using...
In #361 we developed unique string ids and used them for intelligent defaults in `BertClassifier`. We should add defaults to the `BertBase`, `BertLarge`, etc classes as well. * This should...
@mattdangerw, @chenmoneygithub - The original RoBERTa implementation has four different dropout variables: https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/roberta/model.py#L634-L637. Our `RobertaCustom` layer, on the other hand, has only one: https://github.com/keras-team/keras-nlp/blob/master/keras_nlp/models/roberta.py#L86. In order to incorporate all four...
@mattdangerw, @chenmoneygithub - For the `MultiSegmentPacker` layer, we need to make one change. Currently, this is the output of the layer: ``` seq1seq2... ``` But `` is not always used...
Currently the vocabulary is specified directly to BertPreprocessor, for example `'uncased_en'`. However, going forward we want to request the vocabulary associated with a checkpoint rather than knowing what vocab the...