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After using DeepQA for a while, I looked into the codebase and it is a bit complicated, because it provides many features.
To understand the training logic, I almost go through all codes, the works are mainly done in chatbot.py. So, I think if we have a plain code for training and provide some other codes for serving is better to understand the workflow. So, I started from DeepQA project and commit to DeepQA2.
The model and textdata are mostly the same, the changes are about -
Simplify the configuration with a config.ini
Enable run captured model with django restapi framework
Pre-process data, train model and serving model are split into three entries.
Well, the magic works have been done in DeepQA Project. Great thanks.
The text was updated successfully, but these errors were encountered:
Glad you succeeded. I had a quick look at the code and it seems you didn't use C++ as you originally planed. Was it because of technical limitation of the C++ API or just because keeping the python code was simpler ?
After using DeepQA for a while, I looked into the codebase and it is a bit complicated, because it provides many features.
To understand the training logic, I almost go through all codes, the works are mainly done in chatbot.py. So, I think if we have a plain code for training and provide some other codes for serving is better to understand the workflow. So, I started from DeepQA project and commit to DeepQA2.
The model and textdata are mostly the same, the changes are about -
Well, the magic works have been done in DeepQA Project. Great thanks.
The text was updated successfully, but these errors were encountered: