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ESG Metric Extractor — Design & Code Package
Two files, both copy-paste ready:
| File | Contents |
|---|---|
| DESIGN.md | Full design: task framing, multimodal architecture, model choices with 2026 costs, data strategy, training config (TRL-grounded), evaluation, risks, roadmap |
| CODE.md | All runnable Colab cells: Part A = v1 text-only pipeline (Qwen2.5-3B QLoRA, data prep, training, eval); Part B = v2 multimodal pipeline (Qwen3-VL-4B QLoRA on page images, teacher labeling, PDF pipeline) |
Quick start (Colab)
- Runtime → Change runtime type → L4 GPU.
- Open CODE.md, run Part A cells in order (A1→A4 = setup + data prep; check the metric distribution output; A5→A6 = training, ~1–2 h).
- Part B (v2) requires labeled pages of your own reports — teacher labeling (paid HF Inference credits) or a manual spreadsheet.
- Everything pushes to your Hub namespace:
Siva2022/esg-metric-extractor-qwen2.5-3b(v1) andSiva2022/esg-vlm-extractor-qwen3vl-4b(v2).
Data sources
- Beck et al. (2025) gold GHG dataset, Zenodo 14035800 (handled automatically by A3).
- Your own uploaded ESG report PDFs (real-world experiment material for Part B).
Grounding
Training configuration is taken from TRL's own verified examples (huggingface/trl @ f622980: examples/sft_qwen3_vl/sft_qwen3_vl.ipynb), not from memory.
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