Datasets:
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license: mit
pretty_name: ChillText v2
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
tags:
- style-transfer
- conversational
- synthetic
- discord
- imessage
configs:
- config_name: default
data_files:
- split: train
path: chilltext-v2.jsonl
---
# ChillText v2
ChillText v2 is a 200,000-example synthetic style-transfer dataset for making an
assistant's existing answer sound like a real, current Discord or iMessage reply without
changing its meaning. It adds the original user message as context, optional natural-language
personality control, strict preservation of links and identifiers, and four times as many rows
as [ChillText v1](https://huggingface.co/datasets/ProCreations/chilltext).
## Format
```json
{"system":"","input":"the user's actual question","agentOutput":"the assistant's normal answer","output":"casual message 1\n\ncasual message 2","n_msgs":2,"batch_id":123}
```
- `system`: optional English personality request. An empty string selects the original
ChillText personality: lowercase, extremely casual, no emojis or em dashes, and restrained,
context-appropriate current slang.
- `input`: the user's real message or question to the assistant.
- `agentOutput`: the assistant's normal answer before style transfer.
- `output`: the same answer rewritten in ChillText style. Separate chat bubbles are joined by
a blank line (`\n\n`).
- `n_msgs`: number of output bubbles, always 1–4.
- `batch_id`: generation bookkeeping.
The optional `system` prompt changes personality rather than factual content. Examples include
warm, dry, snarky, concise, patient, deadpan, playful, or direct styles. A deliberately small
subset explicitly permits ordinary profanity. Those prompts teach occasional, context-aware
swearing while prohibiting slurs, hate, threats, and abusive targeting. Profanity is rejected in
all empty-system and ordinary-system rows.
## Release statistics
| measure | value |
|---|---:|
| rows | 200,000 |
| default/empty-system rows | 121,551 |
| custom-system rows | 78,449 |
| profanity-permitted system rows | 4,061 |
| rows containing exact-copy invariants | 80,216 |
| 1 / 2 / 3 / 4 message rows | 68,635 / 98,734 / 26,566 / 6,065 |
## Quality controls
Rows were generated with GPT-5.6 Luna at low reasoning. Reasoning was ignored at the transport
layer and is not included in the dataset. Every retained row passed deterministic checks for:
- exact preservation of URLs, emails, paths, commands, model names, versions, dates, times,
prices, percentages, measurements, ticket/order IDs, and other numeric identifiers;
- coherent 1–4-message output formatting;
- no emoji, em dash, en dash, or dated/forced internet slang;
- lowercase default output except case-sensitive exact-copy spans;
- no duplicate interactions and no unchanged `agentOutput`/`output` pairs;
- profanity only when the explicit system prompt permits it.
Release candidates also received a Luna major-defect review for incoherence, contradictions,
invented or unrelated details, changed uncertainty or refusal behavior, and safety-critical
omissions. Introduced-anchor candidates received a separate relevance review. Rows rejected by
either review were regenerated, and every replacement tail was reviewed again before release.
Automated validators cannot prove perfect semantic equivalence. Applications should keep their
normal safety checks and evaluate the model on their own domain, especially for medical, legal,
financial, or other high-stakes text.
## Suggested training prompt
Use `system` as the model's system message when it is nonempty, omit the system turn otherwise,
and send this user message:
```text
rewrite the agent output in chilltext style using the user's message as context.
user:
{input}
agent output:
{agentOutput}
return only the rewritten chat message or messages, separated by a blank line. use 1 to 4 messages total and never more than 4.
```
Train only on `output` as the assistant completion. Gemma 4 natively supports system messages,
so personality prompts can be supplied directly in the `system` role.
## Generation and intended use
The data is intended for supervised style-transfer training and evaluation. Facts are synthetic;
do not treat examples as real news, medical records, transactions, or operational instructions.
The `.example.com`/`.example` resources and 555 phone numbers are deliberately non-production.
|