esg-cid-plus
Supporting code for the EMNLP 2026 paper: ESG-CID-Plus: A Stratified Benchmark for Disclosure Content Index Table Extraction from Corporate Sustainability Reports
Install
uv pip install -e .
Modules
| Module | Purpose |
|---|---|
cid |
Data models (CIRow, ContentIndex, FrameworkSpec) and normalization helpers |
data |
HuggingFace loaders — load_split, load_framework, resolve_pdf_path |
pdf_toolkit |
PDFSession — text extraction, lunr search, camelot tables, page labels |
pdf_toolkit_ocr |
OCRPDFSession — extends above with image rendering, rapidocr, img2table |
extract |
Rule-based pipeline: S1 detect → S2/S3 draft → S4 resolve → ContentIndex |
eval |
Tuple-level P/R/F1 — evaluate_predictions, format_feedback |
Quickstart
from esg_cid_plus.data import load_split, load_framework, resolve_pdf_path
from esg_cid_plus.extract import extract
from esg_cid_plus.eval import evaluate_predictions
pdf_rows, cid_rows = load_split("train_small")
fw = load_framework("gri_2021")
pdf_path = resolve_pdf_path(pdf_rows[0])
ci = extract(pdf_path, fw)
gt = [r for r in cid_rows if r["report_name"] == pdf_rows[0]["report_name"]]
pred = [{"report_name": ci.report_name, "standard_id": r.standard_id,
"page_location_indices": r.page_location_indices} for r in ci.rows]
_, overall = evaluate_predictions(gt, pred)
print(overall)
Tests
uv run pytest # fast (unit)
uv run pytest -m slow # integration — requires HuggingFace access
1 maintainer
Metadata
1 maintainer
Release files for esg-cid-plus 0.1.0
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