This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.
slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet
Natural Language Understanding(NLU) benchmark for closed domain chatbot and task-oriented artificial intelligence secretary system
Intent detection and Slot filling joint learning on ATIS and SNIPS datasets using pre-trained models and built-from-scratch models
Quick and dirty solution to convert CSV training data into Snips JSON format and train an engine
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