task_categories:text-generation language:en size_categories:10K<n<100K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- conversational
---
13,217 sets split to 512 LLaMa tokens. Each output was trimmed to the nearest `\n` at 512 or less tokens, since KoboldCPP's chat format always formats it with names like this;

`\nJerry: Hello. I'm Jerry\nBob: Hi. I'm Bob.`.

Since there's 980 episodes, and it needs to be chunked into 512 tokens, there ends up being 13,217 splits. That's ~13.49 splits for each episode. 

TV Shows Added:
- 114 - Futurama
- 568 - The Simpsons
- 24 - Seinfeld
- 274 - South Park

Plans:
- I need to augment the instructions so that is way more varied, and natural (like maybe some are capitalized, or some spelt slightly wrong, or some have weird wording, etc).
- Possibly make each sample overlap by a small amount like 10-35%, potentially helping it understand contexts better?

Original Datasets:
- [Futurama](https://www.kaggle.com/datasets/josephvm/futurama-seasons-16-transcripts?select=only_spoken_text.csv)
- [The Simpsons](https://www.kaggle.com/datasets/prashant111/the-simpsons-dataset?resource=download&select=simpsons_script_lines.csv)
- [Seinfeld](https://www.kaggle.com/datasets/thec03u5/seinfeld-chronicles?select=scripts.csv)
- [South Park](https://www.kaggle.com/datasets/thedevastator/south-park-scripts-dataset?select=All-seasons.csv)