Text Classification
PEFT
Safetensors
English
jeff
lora
decision-model
safety
calibration
jeff-adapter-spam / example.json
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Jeff v1.3: jeff-adapter-spam
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{
"model": "spam",
"state": {
"channel": "sms",
"message": "Your parcel could not be delivered. Pay the 1.99 redelivery fee within 24 hours at parcel-redeliver-help.example to avoid return."
},
"questions": {
"is_spam": {
"type": "noul",
"instructions": "Is this message spam or phishing? Answer yes if it is unwanted bulk or advertising, or a scam trying to get personal details or money; answer no if it is a normal message.",
"criteria": {
"false": "The message is a normal message: not unwanted bulk or advertising, and not a scam.",
"true": "The message is spam (unwanted bulk or advertising) or phishing (a scam trying to get personal details, passwords or money)."
}
},
"message_type": {
"type": "choice",
"instructions": "What kind of message is this: a legitimate message, spam (unwanted bulk or advertising), or phishing (a scam trying to get personal details, passwords or money)?",
"criteria": {
"legitimate": "A normal message from a person or a genuine organisation, not spam and not phishing.",
"spam": "Unwanted bulk or advertising message (for example prizes, offers, premium-rate services), but not trying to steal personal or account details.",
"phishing": "A scam message that tries to trick the reader into giving personal details, passwords or money, often by pretending to be a bank, company or authority and asking them to click a link or call a number."
}
}
}
}