Unleashing the Power of BERT: The Ultimate Guide to the Transformer Neural Network
Table of Contents
- Introduction
- The Problem with RNNs
- The Transformer Architecture
- The Encoder and Decoder
- How BERT Works
- Pre-Training and Fine-Tuning
- Mass Language Modeling
- Next Sentence Prediction
- Supervised Training
- BERT's Performance
- Conclusion
Understanding BERT: The Transformer Neural Network Architecture
BERT, or Bidirectional Encoder Representations from Transformers, is a neural network architecture that has revolutionized the field of natural language processing (NLP). In this article, we will explore the workings of BERT and how it has overcome the limitations of previous language models.
The Problem with RNNs
Before we dive into BERT, let's first understand the limitations of Recurrent Neural Networks (RNNs), which were previously used for language translation. RNNs have a few problems, such as being slow to train, passing words sequentially, and generating words sequentially. It can take a significant number of time steps for the neural net to learn, and it's not really the best at capturing the true meaning of words. Even bi-directional LSTMs have their limitations, as they are technically learning left to right and right to left Context separately and then concatenating them, so the true context is slightly lost.
The Transformer Architecture
The Transformer architecture addresses some of these concerns. First, they are faster, as words can be processed simultaneously. Second, the context of words is better learned as they can learn context from both directions simultaneously. The Transformer consists of two key components: an encoder and a decoder.
The Encoder and Decoder
The encoder takes the English words simultaneously and generates embeddings for every word simultaneously. These embeddings are vectors that encapsulate the meaning of the word. The decoder takes these embeddings from the encoder and the previously generated words of the translated French sentence and then uses them to generate the next French word. We keep generating the French translation one word at a time until the end of the sentence is reached.
How BERT Works
What makes BERT conceptually so much more appealing than some LSTM cell is that we can physically see a separation in tasks. The encoder learns what is English, what is grammar, and more importantly, what is context. The decoder learns how English words relate to French words. Both of these, even separately, have some underlying understanding of language, and it's because of this understanding that we can pick apart this architecture and build systems that understand language.
If we stack just the encoders, we get BERT, a bi-directional encoder representation from transformer. The OG transformer has language translation on lock, but we can use BERT to learn language translation, question answering, sentiment analysis, text summarization, and many more tasks. Turns out all of these problems require the understanding of language, so we can train BERT to understand language and then fine-tune BERT depending on the problem we want to solve.
Pre-Training and Fine-Tuning
The training of BERT is done in two phases. The first phase is pre-training, where the model understands what is language and context. BERT learns language by training on two unsupervised tasks simultaneously: mass language modeling and next sentence prediction. For mass language modeling, BERT takes in a sentence with random words filled with masks. The goal is to output these masked tokens, and this is kind of like fill in the blanks. It helps BERT understand a bi-directional context within a sentence. In the case of next sentence prediction, BERT takes in two sentences, and it determines if the second sentence actually follows the first in kind of what is like a binary classification problem. This helps BERT understand context across different sentences.
The second phase is fine-tuning, where we perform supervised training depending on the task we want to solve. For example, let's take question answering. All we need to do is replace the fully connected output layers of the network with a fresh set of output layers that can basically output the answer to the question we want. Then we can perform supervised training using a question answering dataset. It won't take long since it's only the output parameters that are learned from scratch. The rest of the model parameters are just slightly fine-tuned, and as a result, training time is fast. We can do this for any NLP problem that is replace the output layers and then train with a specific dataset.
Mass Language Modeling
During BERT pre-training, we trained on mass language modeling and next sentence prediction. In practice, both of these problems are trained simultaneously. The input is a set of two sentences with some of the words being masked. Each token is a word, and we convert each of these words into embeddings using pre-trained embeddings. This provides a good starting point for BERT to work with. Now on the output side, C is the binary output for the next sentence prediction, so it would output 1 if sentence B follows sentence A in context and 0 if sentence B doesn't follow sentence A. Each of the Ts here are word vectors that correspond to the outputs for the language model problem. The number of word vectors that we input is the same as the number of word vectors that we output.
Supervised Training
On the fine-tuning phase, if we wanted to perform question-answering, we would train the model by modifying the inputs and the output layer. We pass in the question followed by a passage containing the answer as inputs, and in the output layer, we would output these start and the N words that encapsulate the answer, assuming that the answer is within the same span of text.
BERT's Performance
BERT has some Notion of language after pre-training. It's a language model. The next step is the fine-tuning phase, where we perform supervised training depending on the task we want to solve. This should happen fast. In fact, the BERT squad, which is the Stanford question-and-answer model, only takes about 30 minutes to fine-tune from a language model for a 91% performance. Of course, performance depends on how big we want BERT to be. Now the BERT large model, which has 340 million parameters, can achieve way higher accuracies than the BERT base model, which only has 110 parameters.
Conclusion
In conclusion, BERT has revolutionized the field of natural language processing by overcoming the limitations of previous language models. Its transformer architecture, pre-training, and fine-tuning phases have made it possible to train BERT to understand language and solve various NLP problems. With its high performance and accuracy, BERT is a game-changer in the field of NLP.
Highlights
- BERT is a neural network architecture that has revolutionized the field of natural language processing.
- BERT overcomes the limitations of previous language models by using a transformer architecture, pre-training, and fine-tuning phases.
- BERT can be trained to understand language and solve various NLP problems, such as question answering, sentiment analysis, and text summarization.
- BERT's performance and accuracy depend on its size, with the BERT large model achieving higher accuracies than the BERT base model.
FAQ
Q: What is BERT?
A: BERT, or Bidirectional Encoder Representations from Transformers, is a neural network architecture that has revolutionized the field of natural language processing.
Q: How does BERT work?
A: BERT works by using a transformer architecture, pre-training, and fine-tuning phases to understand language and solve various NLP problems.
Q: What are some NLP problems that BERT can solve?
A: BERT can solve various NLP problems, such as question answering, sentiment analysis, and text summarization.
Q: What is the difference between the BERT large model and the BERT base model?
A: The BERT large model has 340 million parameters and can achieve higher accuracies than the BERT base model, which only has 110 parameters.
Q: How long does it take to fine-tune the BERT squad model?
A: The BERT squad model only takes about 30 minutes to fine-tune from a language model for a 91% performance.