Text Classification
PEFT
Safetensors
English
Chinese
openjev
lora
custom-code
experimental
probabilistic-decisions
Instructions to use IamBusy/OpenJev-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use IamBusy/OpenJev-0.6B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B") model = PeftModel.from_pretrained(base_model, "IamBusy/OpenJev-0.6B") - Notebooks
- Google Colab
- Kaggle
File size: 1,278 Bytes
aa720d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"state": "I used my debit card yesterday, but the merchant charged the same payment twice. Please help me recover the duplicate payment.",
"questions": {
"department": {
"type": "choice",
"instructions": "Which service best matches the customer's request?",
"criteria": {
"duplicate_payment": "The customer is asking about a card payment charged twice.",
"cash_withdrawal": "The customer is asking about cash withdrawal at an ATM.",
"card_delivery": "The customer is asking about the arrival of a new card.",
"pin_change": "The customer is asking about changing a card PIN."
}
},
"duplicate_charge": {
"type": "noul",
"instructions": "The customer is asking about a card payment charged twice.",
"criteria": {
"false": "The customer is not asking about a card payment charged twice.",
"true": "The customer is asking about a card payment charged twice."
}
},
"sentiment": {
"type": "score",
"instructions": "Rate the sentiment from negative to positive.",
"criteria": [
"The text expresses negative sentiment.",
"The text expresses neutral sentiment.",
"The text expresses positive sentiment."
]
}
}
}
|