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MTSI-BERT A Session-aware Knowledge-based Conversational
NLP was the presentation of the BERT model (Devlin et al. 2018) that for the first time proposes a bidirectional transformer-based masked language model. The use of pre-trained BERT is today almost a must for a lot of NLP works and has inspired a lot of even more powerful archi-tectures (Yang et al. 2019). These new architectures have
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Jan 20 2020 · The first argument to get_learner uses the ktraintext_classifier function to load the pretrained BERT model with a randomly initialized final Dense layer. The second and third arguments are the training and validation data respectively. The last argument to get_learner is the batch size. We use a small batch size of 6 based on the following recommendations from Google for 12GB GPUs
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Apr 10 2019 · We present simple BERT-based models for relation extraction and semantic role labeling. In recent years state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper extensive experiments on datasets for these two tasks show that without using any external features a simple
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Jun 01 2018 · Ultrasonic stir welding process and apparatus. NASA Technical Reports Server (NTRS) Ding R. Jeffrey (Inventor) . An ultrasonic stir welding device provides a method and apparatus for elevating the temperature of a work piece utilizing at least one ultrasonic heater. Instead of relying on a rotating shoulder to provide heat to a workpiece an ultrasonic heater is utilized to provide
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Jul 20 2020 · Transfer Learning in NLP. Transfer learning is a technique where a deep learning model trained on a large dataset is used to perform similar tasks on another dataset. We call such a deep learning model a pre-trained model. The most renowned examples of pre-trained models are the computer vision deep learning models trained on the ImageNet
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Jun 15 2020 · Transfer learning for question answering. The SQuAD dataset offers 150 000 questions which is not that much in the deep learning world. The idea behind transfer learning is to take a model that was trained on a very large dataset then fine-tune that model using the SQuAD dataset. Overall pre-training and fine-tuning procedures for BERT.
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