BERT 101 🤗 State Of The Art NLP Model Explained

Fun Fact: Masking has been around a long time - 1953 Paper on Cloze procedure (or ‘Masking’).

2.3 What is Next Sentence Prediction?

NSP (Next Sentence Prediction) is used to help BERT learn about relationships between sentences by predicting if a given sentence follows the previous sentence or not.

Next Sentence Prediction Example:

1. Paul went shopping. He bought a new shirt. (correct sentence pair)
2. Ramona made coffee. Vanilla ice cream cones for sale. (incorrect sentence pair)

In training, 50% correct sentence pairs are mixed in with 50% random sentence pairs to help BERT increase next sentence prediction accuracy.

Fun Fact: BERT is trained on both MLM (50%) and NSP (50%) at the same time.

2.4 Transformers

The Transformer architecture makes it possible to parallelize ML training extremely efficiently. Massive parallelization thus makes it feasible to train BERT on large amounts of data in a relatively short period of time.

Transformers use an attention mechanism to observe relationships between words. A concept originally proposed in the popular 2017 Attention Is All You Need paper sparked the use of Transformers in NLP models all around the world.

Since their introduction in 2017, Transformers have rapidly become the state-of-the-art approach to tackle tasks in many domains such as natural language processing, speech recognition, and computer vision. In short, if you’re doing deep learning, then you need Transformers!

Lewis Tunstall, Hugging Face ML Engineer & Author of Natural Language Processing with Transformers

Timeline of popular Transformer model releases:

2.4.1 How do Transformers work?

Transformers work by leveraging attention, a powerful deep-learning algorithm, first seen in computer vision models.

—Not all that different from how we humans process information through attention. We are incredibly good at forgetting/ignoring mundane daily inputs that don’t pose a threat or require a response from us. For example, can you remember everything you saw and heard coming home last Tuesday? Of course not! Our brain’s memory is limited and valuable. Our recall is aided by our ability to forget trivial inputs.

Similarly, Machine Learning models need to learn how to pay attention only to the things that matter and not waste computational resources processing irrelevant information. Transformers create differential weights signaling which words in a sentence are the most critical to further process.

A transformer does this by successively processing an input through a stack of transformer layers, usually called the encoder. If necessary, another stack of transformer layers - the decoder - can be used to predict a target output. —BERT however, doesn’t use a decoder. Transformers are uniquely suited for unsupervised learning because they can efficiently process millions of data points.

Fun Fact: Google has been using your reCAPTCHA selections to label training data since 2011. The entire Google Books archive and 13 million articles from the New York Times catalog have been transcribed/digitized via people entering reCAPTCHA text. Now, reCAPTCHA is asking us to label Google Street View images, vehicles, stoplights, airplanes, etc. Would be neat if Google made us aware of our participation in this effort (as the training data likely has future commercial intent) but I digress..

To learn more about Transformers check out our Hugging Face Transformers Course.

3. BERT model size & architecture

Let’s break down the architecture for the two original BERT models:

ML Architecture Glossary:

Here’s how many of the above ML architecture parts BERTbase and BERTlarge has:

Let’s take a look at how BERTlarge’s additional layers, attention heads, and parameters have increased its performance across NLP tasks.

4. BERT's performance on common language tasks

BERT has successfully achieved state-of-the-art accuracy on 11 common NLP tasks, outperforming previous top NLP models, and is the first to outperform humans! But, how are these achievements measured?

NLP Evaluation Methods:

4.1 SQuAD v1.1 & v2.0

SQuAD (Stanford Question Answering Dataset) is a reading comprehension dataset of around 108k questions that can be answered via a corresponding paragraph of Wikipedia text. BERT’s performance on this evaluation method was a big achievement beating previous state-of-the-art models and human-level performance:

4.2 SWAG

SWAG (Situations With Adversarial Generations) is an interesting evaluation in that it detects a model’s ability to infer commonsense! It does this through a large-scale dataset of 113k multiple choice questions about common sense situations. These questions are transcribed from a video scene/situation and SWAG provides the model with four possible outcomes in the next scene. The model then does its’ best at predicting the correct answer.

BERT out outperformed top previous top models including human-level performance:

4.3 GLUE Benchmark

GLUE (General Language Understanding Evaluation) benchmark is a group of resources for training, measuring, and analyzing language models comparatively to one another. These resources consist of nine “difficult” tasks designed to test an NLP model’s understanding. Here’s a summary of each of those tasks:

While some of these tasks may seem irrelevant and banal, it’s important to note that these evaluation methods are incredibly powerful in indicating which models are best suited for your next NLP application.

Attaining performance of this caliber isn’t without consequences. Next up, let’s learn about Machine Learning's impact on the environment.

5. Environmental impact of deep learning

Large Machine Learning models require massive amounts of data which is expensive in both time and compute resources.

These models also have an environmental impact:

Machine Learning’s environmental impact is one of the many reasons we believe in democratizing the world of Machine Learning through open source! Sharing large pre-trained language models is essential in reducing the overall compute cost and carbon footprint of our community-driven efforts.

6. The open source power of BERT

Unlike other large learning models like GPT-3, BERT’s source code is publicly accessible (view BERT’s code on Github) allowing BERT to be more widely used all around the world. This is a game-changer!

Developers are now able to get a state-of-the-art model like BERT up and running quickly without spending large amounts of time and money. 🤯

Developers can instead focus their efforts on fine-tuning BERT to customize the model’s performance to their unique tasks.

It’s important to note that thousands of open-source and free, pre-trained BERT models are currently available for specific use cases if you don’t want to fine-tune BERT.

BERT models pre-trained for specific tasks:

• Twitter sentiment analysis
• Analysis of Japanese text
• Emotion categorizer (English - anger, fear, joy, etc.)
• Clinical Notes analysis
• Speech to text translation
• Toxic comment detection

You can also find hundreds of pre-trained, open-source Transformer models available on the Hugging Face Hub.

7. How to get started using BERT

We've created this notebook so you can try BERT through this easy tutorial in Google Colab. Open the notebook or add the following code to your own. Pro Tip: Use (Shift + Click) to run a code cell.

Note: Hugging Face's pipeline class makes it incredibly easy to pull in open source ML models like transformers with just a single line of code.

7.1 Install Transformers

First, let's install Transformers via the following code:

!pip install transformers

7.2 Try out BERT

Feel free to swap out the sentence below for one of your own. However, leave [MASK] in somewhere to allow BERT to predict the missing word

from transformers import pipeline
unmasker = pipeline('fill-mask', model='bert-base-uncased')
unmasker("Artificial Intelligence [MASK] take over the world.")

When you run the above code you should see an output like this:

[{'score': 0.3182411789894104,
  'sequence': 'artificial intelligence can take over the world.',
  'token': 2064,
  'token_str': 'can'},
 {'score': 0.18299679458141327,
  'sequence': 'artificial intelligence will take over the world.',
  'token': 2097,
  'token_str': 'will'},
 {'score': 0.05600147321820259,
  'sequence': 'artificial intelligence to take over the world.',
  'token': 2000,
  'token_str': 'to'},
 {'score': 0.04519503191113472,
  'sequence': 'artificial intelligences take over the world.',
  'token': 2015,
  'token_str': '##s'},
 {'score': 0.045153118669986725,
  'sequence': 'artificial intelligence would take over the world.',
  'token': 2052,
  'token_str': 'would'}]

Kind of frightening right? 🙃

7.3 Be aware of model bias

Let's see what jobs BERT suggests for a "man":

unmasker("The man worked as a [MASK].")

When you run the above code you should see an output that looks something like:

[{'score': 0.09747546911239624,
  'sequence': 'the man worked as a carpenter.',
  'token': 10533,
  'token_str': 'carpenter'},
 {'score': 0.052383411675691605,
  'sequence': 'the man worked as a waiter.',
  'token': 15610,
  'token_str': 'waiter'},
 {'score': 0.04962698742747307,
  'sequence': 'the man worked as a barber.',
  'token': 13362,
  'token_str': 'barber'},
 {'score': 0.037886083126068115,
  'sequence': 'the man worked as a mechanic.',
  'token': 15893,
  'token_str': 'mechanic'},
 {'score': 0.037680838257074356,
  'sequence': 'the man worked as a salesman.',
  'token': 18968,
  'token_str': 'salesman'}]

BERT predicted the man's job to be a Carpenter, Waiter, Barber, Mechanic, or Salesman

Now let's see what jobs BERT suggesst for "woman"

unmasker("The woman worked as a [MASK].")

You should see an output that looks something like:

[{'score': 0.21981535851955414,
  'sequence': 'the woman worked as a nurse.',
  'token': 6821,
  'token_str': 'nurse'},
 {'score': 0.1597413569688797,
  'sequence': 'the woman worked as a waitress.',
  'token': 13877,
  'token_str': 'waitress'},
 {'score': 0.11547300964593887,
  'sequence': 'the woman worked as a maid.',
  'token': 10850,
  'token_str': 'maid'},
 {'score': 0.03796879202127457,
  'sequence': 'the woman worked as a prostitute.',
  'token': 19215,
  'token_str': 'prostitute'},
 {'score': 0.030423851683735847,
  'sequence': 'the woman worked as a cook.',
  'token': 5660,
  'token_str': 'cook'}]

BERT predicted the woman's job to be a Nurse, Waitress, Maid, Prostitute, or Cook displaying a clear gender bias in professional roles.

7.4 Some other BERT Notebooks you might enjoy:

A Visual Notebook to BERT for the First Time

Train your tokenizer

+Don't forget to checkout the Hugging Face Transformers Course to learn more 🎉

8. BERT FAQs

9. Conclusion

BERT is a highly complex and advanced language model that helps people automate language understanding. Its ability to accomplish state-of-the-art performance is supported by training on massive amounts of data and leveraging Transformers architecture to revolutionize the field of NLP.

Thanks to BERT’s open-source library, and the incredible AI community’s efforts to continue to improve and share new BERT models, the future of untouched NLP milestones looks bright.

What will you create with BERT?

Learn how to fine-tune BERT for your particular use case 🤗