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# A brief description
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This model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task learning scheme, further described in the paper "TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents". The model finetunes quickly to the PERSONA-CHAT dataset and 5 epochs of training was sufficient. A batch size of 4 and accumulated gradients over 8 iterations are used, resulting in the effective batch size of 32. In addition, the Adam optimization scheme with a learning rate of 6e-5 is used.
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# A brief description:
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This model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task learning scheme, further described in the paper "TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents". The model finetunes quickly to the PERSONA-CHAT dataset and 5 epochs of training was sufficient. A batch size of 4 and accumulated gradients over 8 iterations are used, resulting in the effective batch size of 32. In addition, the Adam optimization scheme with a learning rate of 6e-5 is used.
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