OptimalScale / OptimalScale/LMFlow
Pleaee @showgood880702 i need this dataset as text2text .json file to understand the structure correctly
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Description
{
"input": "###Instruction: ....\n\n###human: ....\n\n###chatbot: ....\n\n###human: ....\n\n###chatbot: ....\n\n###human: .....\n\n###chatbot:",
"output": ".....###"
}
Thank you very much for the explanation.
I am still a little confused about the training data structure for a chatbot. For example, here I have a multi-round conversation used as training data. Should I feed it to the model as I showed before, with the end_mark and the end?
{"input": "###Instruction: ....\n\n###human: ....\n\n###:chatbot:", "output": ".....###"}
{"input": "###Instruction: ....\n\n###human: ....\n\n###:chatbot:", "output": ".....###"}
{"input": "###Instruction: ....\n\n###human: ....\n\n###:chatbot:", "output": ".....###"}
or should I split them as pairs of and as different instances, and start with the instruction?
Originally posted by @showgood880702 in #357
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Research direction
Start with the dataset-format question in this issue and review the linked discussion in #357. No repository file, test, or entry point is named, and completion criteria are not defined; clarify whether multi-round conversations should remain one input/output JSON record or be split before implementation.
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Assessment
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100