As GPT2 models are computationally demanding, generating a decent amount of text with them can take a couple of minutes (on a CPU), which if done inside a Flask route is not ideal. Rather than having a long-running request, we can use celery to queue up a task to generate the text, which means we can immediately return a response to the user containing the id of the task and it can display some sort of visual feedback (e.g. a spinner) while a worker completes the task. Meanwhile, the client periodically polls the server for the status of the task (using the id it was given).

The -webkit-line-clamp style does not properly truncate multi-paragraph text, nor does it handle other markdown elements properly (see https://codepen.io/jimratliff/pen/BaBzzbq). Instead of using this, we should use a JS plugin that is designed to truncate HTML strings (such as truncate-html).

Currently, the generator script assumes that writing prompts should be generated; however, since the generator script can be used on any casual language model, this assumption doesn't make such sense. Thus, we need to allow the script to take in any form of prompt with the proper tokenization and formatting.

This is already (sort of) implement using the prompt argument; however, this argument does not prepend the beginning of sentence token which is required. Since this token may be unknown before loading in the tokenizer, we need to add in a prompt argument that DOES have the BOS token prepended to it.

Currently, if you stop scraping a subreddit (or if there is an error and the program crashes), you have to restart scraping from scratch (including everything you've already scraped). It would be very useful if the scrapper CLI had an option to continue from file. Where it would skip all the submissions it has already scraped.

Rewrite the trainer script to use the transformers.Trainer class to enable TPU support.