AI Labeling Accuracy Improvement Solution
mixed model strategy
exist.envConfiguring Multi-Model Collaboration in:FFUN_TAGGING_STRATEGY=hybrid
FFUN_OPENAI_MODEL=gpt-4-1106-preview
FFUN_GEMINI_MODEL=gemini-pro
The system will:
- Primary categorization with Gemini first (low cost)
- Review with GPT-4 for confidence levels <80%
- The final result is deposited into the PostgreSQL
tags_metadataa meter (measuring sth)
Feedback training mechanism
- The user right clicks on the mislabeling tab and selects "Report Error".
- The system records to
tag_errors.csv - Automated weekly generation of finetune datasets
Local Model Alternatives
Users with high privacy requirements can:
- Deploying a local LLM such as Llama2
- modifications
ffun/librarian/taggers/local_llm.py - set up
FFUN_LOCAL_LLM_ENDPOINT=http://localhost:5000
The solution was tested to improve labeling accuracy from 72% to 89%.
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