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Datasets:
Data scraped from roleplayerguild and parsed into prompts with a conversation history and associated character bio. Thanks to an anonymous internet stranger for the original scrape.
As usernames can be associated with multiple character biographies, assignment of characters is a little fuzzy. The char_confidence
feature reflects how likely this assignment is to be correct. Not all posts in the conversation history necessarily have an associated character name. The column has_nameless
reflects this.
Each row should fit into 4096 Llama tokens, depending on your prompt format - there's built in slack of 128 tokens + 8 per message.
There are a few configurations available. I highly recommend not using the default configuration as it contains a lot of questionable quality data. The options, in order of increasing usefulness:
default
- ocean of garbage with some gemshigh_confidence
- only entries with no nameless posts that are highly likely to be assigned a correctchar_name
/bio
pruned
- Further filtered fromhigh_confidence
to remove common types of junk repliesgrammar_filtered
- run through a grammar checker to remove rows with too many mistakes
The grammar_filtered
configuration is almost certainly what you want to be using. (Unless you want to do your own processing and filtering.)
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Models trained or fine-tuned on chargoddard/rpguild
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