# A brief description:
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.
{}
transformers pytorch gpt2 endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us 

# A brief description:
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.
# A brief description:
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.
TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us 
 # A brief description:
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.
passage: TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us 
# A brief description:
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.