Exploring Google's BERT Architecture: Masked Language Model and Attention Visualizations

Table of Contents

1. Introduction
2. Recap of Part One: Sequence to Sequence and Encoder-Decoder Architectures
3. Recap of Part Two: Attention Basics, Multi-Head Attention, and the BERT Architecture
4. Pre-Training and Fine-Tuning
5. Other Architectures Based on Pre-Training
6. The BERT Model and Token Embedding
7. BERT Base and BERT Large
8. Visualizing Attention Layers in BERT
9. Running BERT: Installation and Usage
10. Conclusion
11. References

Introduction

Welcome to the third and final part of this series on the BERT model. In the first part, we covered sequence to sequence and encoder-decoder architectures, while in the Second part, we discussed attention basics, multi-head attention, and the BERT architecture. In this final part, we will Delve deeper into the BERT model and explore pre-training and fine-tuning, other architectures based on pre-training, the BERT model and token embedding, BERT base and BERT large, visualizing attention layers in BERT, and running BERT.

Recap of Part One: Sequence to Sequence and Encoder-Decoder Architectures

In the first part of this series, we covered sequence to sequence and encoder-decoder architectures. Sequence to sequence models are used for tasks such as machine translation, where the input and output sequences can have different lengths. Encoder-decoder architectures consist of two parts: an encoder that processes the input sequence and a decoder that generates the output sequence. We also discussed the limitations of these architectures, such as the inability to handle long sequences.

Recap of Part Two: Attention Basics, Multi-Head Attention, and the BERT Architecture

In the second part of this series, we discussed attention basics, multi-head attention, and the BERT architecture. Attention mechanisms allow the model to focus on specific parts of the input sequence when generating the output sequence. Multi-head attention is a variant of attention that allows the model to attend to different parts of the input sequence simultaneously. The BERT architecture is a transformer-based model that uses attention mechanisms to achieve state-of-the-art results on various natural language processing tasks.

Pre-Training and Fine-Tuning

Pre-training is a technique where a model is trained on a large corpus of text data before being fine-tuned on a specific task. Google has pre-trained the BERT model and released some vectors for public use. These vectors can be fine-tuned on domain-specific data to achieve better results on that domain. Even if domain-specific data is not available, the results of the pre-trained BERT model are still impressive.

Other Architectures Based on Pre-Training

Other architectures based on pre-training include OpenAI's GPT and ELMo. These architectures also use pre-training to achieve state-of-the-art results on various natural language processing tasks. However, unlike BERT, they do not look in both directions at once.

The BERT Model and Token Embedding

When a word is fed to the BERT model, 15% of the words in each sequence or sentence are replaced with a mass token. The model then tries to predict what was actually there instead of that mask. To do this, it reads the previous Context and whatever is still unknown and tries to guess it. The prediction of the output word requires token embedding, sentence embedding, and transformer positional embedding.

BERT Base and BERT Large

BERT comes in two variants: BERT base and BERT large. BERT base uses 12 Hidden layers and 12 multi-head attention layers, while BERT large uses 24 layers and 16 attention heads. The parameters are calculated based on these numbers.

Visualizing Attention Layers in BERT

There is a code available on the internet that can help You Visualize the attention layers in BERT. While it is not 100% perfect and accurate, it can help you get more familiar with how BERT is doing.

Running BERT: Installation and Usage

To run BERT, you need to install it and download the BERT base encased vector. After that, you can follow the steps provided to train or predict using BERT. The output files include NB prediction, best prediction, and prediction JSON.

Conclusion

In conclusion, the BERT model is a transformer-based model that uses attention mechanisms to achieve state-of-the-art results on various natural language processing tasks. Pre-training and fine-tuning can be used to achieve even better results on domain-specific data. Other architectures based on pre-training, such as GPT and ELMo, also achieve impressive results. BERT base and BERT large are the two variants of the BERT model, and attention layers can be visualized using available code. Running BERT requires installation and downloading of the BERT base encased vector.

References

1. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. arXiv preprint arXiv:1810.04805 (2018).
2. "Attention Is All You Need." Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin. arXiv preprint arXiv:1706.03762 (2017).
3. "BERT Fine-Tuning Tutorial with PyTorch." Chris McCormick and Nick Ryan. https://mccormickml.com/2019/07/22/BERT-fine-tuning/.
4. "BERT Explained: State of the art language model for NLP." Prateek Joshi. https://www.analyticsvidhya.com/blog/2019/09/demystifying-bert-groundbreaking-nlp-framework/.
5. "BERT: Understanding and Fine-Tuning." Chris McCormick. https://mccormickml.com/2019/11/11/BERT-word-embeddings-tutorial/.
6. "BERT: A Pre-trained Transformer for Language Understanding." Jay Alammar. http://jalammar.github.io/illustrated-BERT/.

FAQ

Q: What is pre-training? A: Pre-training is a technique where a model is trained on a large corpus of text data before being fine-tuned on a specific task.

Q: What is BERT? A: BERT is a transformer-based model that uses attention mechanisms to achieve state-of-the-art results on various natural language processing tasks.

Q: What is the difference between BERT base and BERT large? A: BERT base uses 12 hidden layers and 12 multi-head attention layers, while BERT large uses 24 layers and 16 attention heads.

Q: Can BERT be fine-tuned on domain-specific data? A: Yes, BERT can be fine-tuned on domain-specific data to achieve better results on that domain.

Q: What are other architectures based on pre-training? A: Other architectures based on pre-training include OpenAI's GPT and ELMo.