P.S.: I think that your use of comes from the fact that other non-generative models like BERT, use similar special tokens (, ) and are specifically designed to receive two concatenated segments as input. Therefore, the maximum token length would apply for the concatenation of text and reference summary. In this series, we use a unique data set of German reviews of physicians written by their patients. įor GPT-2, there is only a single sequence, not 2. To convert a GPT-2 model trained using earlier TensorFlow-based finetuning. Another option is to simply use in the places of your, and. Creative writing using GPT-2 Text Generation Step 1: Determine input prompt and visualize word dependencies Step 2: Use an ML model to generate text based. Not all AI generated text will be good, hence why human curation is currently. This means that if you want to use your special tokens, you would need to add them to the vocabulary and get them trained during fine-tuning. Specifically, the original GPT-2 vocabulary does not have the special tokens you use. You would need, nevertheless, to perform some adaptations. This kind of thing has been done before, for instance, in this NeurIPS 2018 article that uses only a Transformer decoder for machine translation, concatenating source and target sides, like you do: Nevertheless, while it was not meant to work the way you are using it, it is possible that this works. The GPT language model was initially introduced in 2018 in the paper Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, with the goal of developing a system that could learn from previously produced text. Normally, in order to do conditional text generation, people use an encoder-decoder architecture, that is, a full encoder-decoder Transformer instead of GPT-2, which only has the decoder part. Therefore, it is not meant to be used the way you are trying to do it. It then completes whatever it was passed as input. This means that, by default, it receives either no input at all or the initial tokens of a sentence/paragraph. Note that in the original GPT-2 vocabulary there is no token, and neither there is in the Huggingface implementation. txt file which is then used to finetune the GPT-2 model. ![]() After reading answer, I carefully read the original GPT-2 paper and I confirm that the authors do not add special tokens, but simply the text TL DR: (be careful to include the :, which is not present in the referenced answer) after the text to summarize. Since the dataset is massive, we use Ray to override the python GIL and leverage all CPU cores to expedite the process.
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