![]() ![]() Pre-processing including tokenization and post-processing steps that areĭescribed in the BERT paper and implemented Passage that most likely answers the question. The model takes a passage and a question as input, then returns a segment of the This app uses a compressed version of BERT, MobileBERT, that runs 4x faster andĪnswering Dataset, is a reading comprehension dataset consisting of articlesįrom Wikipedia and a set of question-answer pairs for each article. Representations which obtains state-of-the-art results on a wide array of Representations from Transformers, is a method of pre-training language It was created using a pre-trained BERT model fine-tuned on The model can be used to build a system that can answer users’ questions in If you are using a platform other than Android/iOS, or you are already familiarĬan download our starter question and answer model.įor more information about metadata and associated fields (e.g. Recommend exploring the following example applications that can help you get If you are new to TensorFlow Lite and are working with Android or iOS, we Note: (1) To integrate an existing model, try Use a TensorFlow Lite model to answer questions based on the content of a given ![]()
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