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The answer is yes but 'lightweight' will require a 'lightweight' model. Your application for 'domain one' is called open domain question answering (ODQA). Here is a demonstration of ODQA using BERT: https://www.pragnakalp.com/demos/BERT-NLP-QnA-Demo/ Your application for 'domain two' is a little different. It is about learning sequences from sequences. ...


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This seems to be inherited from the original Google implementation, which also uses 2 outputs (https://github.com/google-research/bert/blob/master/run_pretraining.py#L293). I can see two possible reasons that the original implementation uses 2 outputs: They are using the cross entropy loss, which typically works with log probabilities. To get probabilities ...


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My answer assumes your fine-tuning architecture simply stacks a single fully-connected layer on top of the BERT [CLS] output, as in Figure 4b of the BERT paper. Generally, when working with mixed data such as continuous and categorical features, the first step is to simply concatenate all the inputs into one long vector. In your case, you would concatenate a ...


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=> I don't have more ideas about BART and T5 Right now. but I had created a chatbot Based on GPT-2 Model-based on Microsoft DialoGPT. Which is fine-tuned on millions of parameters of Reddit. you can fine-tune on your own data using DialoGPT. I had not found a public decoding method. So, We tried to generate diverse responses using simple nucleus sampling at ...


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