Text Generation with GPT-2, OpenAI's Recently Released Language Model

Venus, planet of love Was destroyed by global warming, while the other suns have been ravaged by the tides of time. There are no suns and there are no tides, except the sun itself. A few suns exist now in Mars (the moon is now in the orbit of Sol and Jupiter), but they are gone in the future. (It is implied that we can go back home to Earth) Jupiter is the only other planet that is not the source; that planet would be the nearest known red planet to us. So is Earth.

The Earth's only visible source of energy is the sun itself. (In Greek it means "sun" or "heaven.") According to the Old Testament story, Jupiter was so cold that it was able to cause the death of children when they died in a ship. The only real star in the solar system that is capable of causing death is the sun, which must be one of the most powerful stars in the universe. Only the moon can cause death from its star at once, and Venus must be at least one of the most powerful star systems in the entire galaxy (more details here). Earth was never seen as an "open" planet.

Earlier this month, OpenAI released a new text generation model, called GPT-2. GPT-2 stands for “Generative Pre-Training 2”: generative, because we are generating text; pre-training, because instead of training the model for any one specific task, we’re using unsupervised “pre-training” such that the general model can perform on a variety of tasks; and 2, because it’s the second model using this approach, following the first GPT model.

TLDR: The model is pretty good at generating fiction and fantasy, but it’s bad at math and at telling jokes. Skip to the end for my favorite excerpts.

Model Overview

The GPT-2 model uses conditional probability language modeling with a Transformer neural network architecture that relies on self-attention mechanisms (inspired by attention mechanisms from image processing tasks) in lieu of recurrence or convolution. (Side note: interesting to see how advancements in neural networks for image and language processing co-evolve.)

The model is trained on about 8 million documents, or about 40 GB of text, from web pages. The dataset, scraped for this model, is called WebText, and is the result of scraping outbound links from Reddit with at least 3 karma. (Some thoughts on this later. See section on “Training Data”)

In the original GPT model, the unsupervised pre-training was used as an initial step, followed by a supervised fine-tuning step for various tasks, such as question answering. GPT-2, however, is assessed using only the pre-training step, without the supervised fine-tuning. In other words, the model performs well in a zero shot setting.

First Impressions

When I first saw the blog post, I was both very impressed and also highly skeptical of the results.


Read More

Music and Mood: Assessing the Predictive Value of Audio Features on Lyrical Sentiment

 

aka - what's the relationship between the audio features of a song and how positive or negative its lyrics are? 

aka - data analysis of my spotify music data + sentiment analysis + supervised machine learning

aka - my senior thesis

the full jupyter notebook used to conduct this data analysis can be found on my github here: Spotify Data Analysis

(pg. 32 and onward is just the full python jupyter notebook in the appendix.)