2025-09-09.
During Q1 and Q2 of 2025, I was cooking a model for product property extraction given product description (textual) - with same training data as BERT-CRF, the fine-tuned QWen-2.5-7B models failed to surpass BERT-CRF on supply-side product description (which is longer and more complex) and succeeded on surpass on demand-side product description (which is shorter and more succinct).
The fine-tuning recipe is simple as follows.
- Method: Supervised Fine-Tuning (SFT)
- Data format:
- Input: {Instructions (Task desc + Multi req + Output format)}
- Output: {JSON properties as PropName-PropValList key-value pairs}
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