multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks. We support various data formats for majority of NLU tasks and multiple transformer-based encoders (eg. BERT, Distil-BERT, ALBERT, RoBERTa, XLNET etc.)
For complete documentation for this library, please refer to documentation
What is multi_task_NLP about?
Any conversational AI system involves building multiple components to perform various tasks and a pipeline to stitch all components together. Provided the recent effectiveness of transformer-based models in NLP, it’s very common to build a transformer-based model to solve your use case. But having multiple such models running together for a conversational AI system can lead to expensive resource consumption, increased latencies for predictions and make the system difficult to manage. This poses a real challenge for anyone who wants to build a conversational AI system in a simplistic way.
multi_task_NLP gives you the capability to define multiple tasks together and train a single model which simultaneously learns on all defined tasks. This means one can perform multiple tasks with latency and resource consumption equivalent to a single task.