Again, transfer learning has been hugely successful and is likely to continue throughout 2019. Pre-trained Language Models Photo by Kelly SikkemaĪnother trend that the NLP community picked up in 2018 was the idea of transfer learning, which had been going on for years in the computer vision world, but has only recently picked up the pace for NLP tasks. Though in actuality, that’s a gross abstraction, so I’d encourage you to read this transformer article before you continue. So for the purposes of this article, treat the transformer as a black box- it defines a structure for performing computations. As I mentioned already, the details of this model are … fairly detailed. The transformer is an awesome neural network architecture. If you’re already aware of the technologies that led up to GPT-2, congratulations! You basically now understand what it takes to invent a state of the art NLP model! 🎉🎉īut for the rest of you that were daydreaming about a Sesame Street-Michael Bay crossover, let’s get into it. To top that, I’ve also left out essential ideas like ELMo and BERT that while not immediately relevant when talking about GPT-2, were instrumental to its eventual development. there’s a fair amount of background knowledge required to get all of that. I’ll illustrate it using some insanely advanced math:Ģ018 OpenAI Transformer v1(akaGPT-1) = ULMFiT+ TransformerĢ019 GPT-2 = GPT-1 + reddit + A lot of compute So here’s a summary of all the 2018 NLP breakthroughs that you need to understand before getting into GPT-2. “ 2” means this isn’t the first time they’re trying this whole GPT thing out.I’m not going to discuss the transformer architecture in detail since there’s already another great article on the FloydHub blog that explains how it works. “ Transformer” means OpenAI used the transformer architecture, as opposed to an RNN, LSTM, GRU or any other 3/4 letter acronym you have in mind.
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This retraining approach became quite popular in 2018 and is very likely to be a trend that continues throughout 2019. This is kind of like transfer learning with Imagenet, except it’s for NLP.
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Ready to build, train, and deploy AI? Get started with FloydHub's collaborative AI platform for free Try FloydHub for free What GPT-2 Actually IsĪs has become the norm when there is a breakthrough in deep learning research, there’s been a fair share of terminator imagery accompanying popular articles that describe OpenAI’s latest set of matrix multiplications. So unless you’ve got that kind of computing power, it’s a feat if your mini-GPT can get subject-verb agreement right. The original model was trained for months, harnessing the power of 100+ GPUs. Note, however, that the GPT-2 model that we’re going to build won’t start generating fake Brexit campaigns.
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Here, I’ll show you how exactly humanity’s greatest text generator (at the time of this writing, at least) works, and how to build your own in just a few lines of code. As the great Stan Lee once said, “nuff said” about that. In this post, I’m not going to talk about better language models and their implications. What you just read was written entirely by OpenAI’s GPT-2 language model, prompted only with the word “Today”.Īpart from another fancy acronym, GPT-2 brought along somewhat coherent (semantically, at least) language generation capabilities, some semblance of hope for zero-shot transfer learning, and a transformer network trained with approximately 1.5 billion parameters on a text corpus with over 40 gigabytes of internet wisdom.īut of course, what really broke the internet was talking, four-horned, half-breed unicorns in the Andes… A text generation sample from OpenAI’s GPT-2 language model The sentence you just read wasn’t written by me, the author of this article, nor was it written by the editor.
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