I got this as my third which seemed either prophetic or deterministic.
HackerNewsReplyGuy:
>from hackernews_response_guy import HackerNewsReplyGuy
>model = HackerNewsReplyGuy(1)
>model.predict_comments(comments, [u'comment_id'])
The pre-generated set is hand-curated, but they are still 100% generated by the GPT-J model behind the scenes. More info -> https://github.com/thesephist/modelexicon
“Back in my day, we had to train our own models..” already sounds anachronistic.
Nicely polished.
Looks like bmk (nabla theta) was right that arxiv was an impactful addition to The Pile. I bet that’s where J got its knowledge in this case.
---
Proceedings of Deep Learning Advancements Conference, list of accepted deep learning models
1. [StyleGAN] StyleGAN is a generative adversarial network for style transfer between artworks. It uses a traditional GAN architecture and is trained on a dataset of 150,000 traditional and modern art. StyleGAN shows improved style transfer performance while reducing computational complexity.
2. [GPT-2] GPT-2 is a decoder-only transformer model trained on WebText, OpenAI\'s proprietary clean text corpus based on Wikipedia, Google News, Reddit, and others comprising a 2TB dataset for autoregressive training. GPT-2 demonstrates state-of-the-art performance on several language modeling and conversational tasks.
3. [$MODELNAME]
It almost seems like the code is properly related to the names. GAN code seems to look like GAN code. But I’m not sure.
The idea here was to give the model a prompt that felt like a tutorial or some kind, and try to minimize non-Python non-ML-y code.
---
$MODEL_DESCRIPTION_FROM_EARLIER
Let\'s use this model. The basic use case takes only a few lines of Python to run the inference. Here are the first few lines.
```python
Sorry, I didn't mean to imply these were not produced as described, I was just curious. Tho think of it it was a silly question as it would have otherwise implied they're generated in a blink.
Quick, generate your own before the server goes down! I don’t think the model can withstand HN for too long unless they have some beefy servers.
Aaand it’s dead. Fun while it lasted.