Bert

Automatic summarization is the process of shortening a set of data computationally, to create a subset that represents the most important or relevant information within the original content. Extractive summarization can be seen as the task of ranking and scoring sentences in the document based on certain metrics and then Read more

Disfluency detection models now approach high accuracy on English text. However, little exploration has been done in im- proving the size and inference time of the model. At the same time, automatic speech recognition (ASR) models are moving from server-side inference to local, on-device inference. Sup- porting models in the Read more

In this article, we explore one of the most powerful NLP algorithms – BERT. We explain the BERT paper, how it functions and use it with ML.NET. — Lees op rubikscode.net/2021/04/19/machine-learning-with-ml-net-nlp-with-bert/

A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ability to model Read more

BERT is the first deeply bidirectional, unsupervised language representation model, pre-trained using only a plain text corpus. It has been widely used on various natural language processing tasks. Th… — Lees op crossminds.ai/graphlist/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding-60709500c8663c4cfa875fc4/

[1906.04341] What Does BERT Look At? An Analysis of BERT’s Attention — Lees op arxiv.org/abs/1906.04341