Benchmarking Phd Thesis

Benchmarking Phd Thesis-19
All optimizations and code for achieving this performance with BERT are being released as open source in this Tensor RT sample repo.We have optimized the Transformer layer, which is a fundamental building block of the BERT encoder so you can adapt these optimizations to any BERT-based NLP task.

All optimizations and code for achieving this performance with BERT are being released as open source in this Tensor RT sample repo.We have optimized the Transformer layer, which is a fundamental building block of the BERT encoder so you can adapt these optimizations to any BERT-based NLP task.

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BERT is applied to an expanding set of speech and NLP applications beyond conversational AI, all of which can take advantage of these optimizations.

Question Answering(QA) or Reading Comprehension is a very popular way to test the ability of models to understand context.

In our example, BERT provides a high-quality language model that is fine-tuned for question answering, but is suitable for other tasks such as sentence classification and sentiment analysis.

To pre-train BERT, you can either start with the pretrained checkpoints available online (Figure 1 (left)) or pre-train BERT on your own custom corpus (Figure 1 (right)).

To overcome the problem of learning a model for the task from scratch, recent breakthroughs in NLP leverage the vast amounts of unlabeled text and decompose the NLP task into two parts: 1) learning to represent the meaning of words, relationship between them, i.e.

building up a language model using auxiliary tasks and a large corpus of text and 2) specialize the language model to the actual task by augmenting the language model with a relatively small task-specific network that is trained in a supervised manner.

This has posed a challenge for companies to deploy BERT as part of real-time applications until now.

Today, NVIDIA is releasing new Tensor RT optimizations for BERT that allow you to perform inference in 2.2 ms* on T4 GPUs.

In this article, we will demonstrate how to create a simple question answering application using Python, powered by Tensor RT-optimized BERT code that we have released today.

The example provides an API to input passages and questions, and it returns responses generated by the BERT model.

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