NGC | Catalog
Welcome Guest
CatalogResourcesBERT+SQuAD TF Finetuning Notebook

BERT+SQuAD TF Finetuning Notebook

For downloads and more information, please view on a desktop device.
Logo for BERT+SQuAD TF Finetuning Notebook

Description

NVIDIA's BERT leverages mixed precision arithmetic and Tensor Cores on A100, V100 and T4 GPUs for faster training while maintaining target accuracy. This notebook demonstrates BERT Question Answering Fine-Tuning with Mixed Precision on SQuaD 2.0 Dataset.

Publisher

NVIDIA

Use Case

Other

Framework

Other

Latest Version

19.10

Modified

May 26, 2021

Compressed Size

696.68 KB

Model overview

BERT, or Bidirectional Encoder Representations from Transformers, is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. NVIDIA's BERT is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and Tensor Cores on A100, V100 and T4 GPUs for faster training times while maintaining target accuracy.

Requirements

This resource contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from those, make sure you have the following components:

  • NVIDIA Docker
  • TensorFlow 20.06-py3+ NGC container
  • GPU with the following architectures:
    • NVIDIA Volta
    • NVIDIA Turing
    • NVIDIA Ampere

In the File Browser section, you can find the jupyter notebook which you can preview and download. You can download the zip file which contains the dockerfile that you will need for setting up the container to run the the notebook in.

In the Setup section, you can find the link to the main repository which contains the dockerfile and the notebooks. This is an alternative to downloading the main repository as a zip file from the "File Browser" section.

In the Quick Start Guide section, you can see how to prepare the dataset, download pretrained NVIDIA BERT models, and perform fine-tuning with mixed precision for the Question Answering task by running the jupyter notebooks inside the container.