1993658fb2
Builds a relationship-rich finance dataset for text-to-RDF-triple extraction
from SEC fund disclosures, the dataset for the thesis 'Magical RDF Triples and
how to synthetize them'.
- build_rdf_dataset.py: gold (N-CEN graphs), fetch (EDGAR prospectus prose,
all books per trust), samples (per-fund segmentation, marker + plain
serializations), split (trust-level 80/10/10, no leakage)
- score_baseline.py: no-model string-match baseline + strong-model scorer
- dataset_description.{tex,pdf}: scientific description of the dataset
- data/rdf_poc/gold_graphs.jsonl: structured gold knowledge graph (2025Q3)
- Large prose/sample files and raw SEC downloads are gitignored (reproducible)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
39 lines
643 B
Plaintext
39 lines
643 B
Plaintext
# ---- Large / derived data (reproducible via build_rdf_dataset.py) ----
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# Raw prospectus prose fetched from EDGAR (GBs)
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data/rdf_poc/prose/
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# Generated training samples and splits (embed raw SEC text, 100s of MB)
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data/rdf_poc/samples.jsonl
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data/rdf_poc/train.jsonl
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data/rdf_poc/val.jsonl
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data/rdf_poc/test.jsonl
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# Raw SEC bulk downloads (re-downloadable from sec.gov)
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data/ncen/
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data/nport/
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data/xbrl_rr/
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# SQLite working DB
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fund_data.db
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fund_data.db-shm
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fund_data.db-wal
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# Archives
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*.zip
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# Python
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__pycache__/
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*.pyc
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*.pyo
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# LaTeX build artifacts
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*.aux
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*.log
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*.out
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*.toc
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*.fls
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*.fdb_latexmk
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*.synctex.gz
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# OS / editor
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.DS_Store
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.claude/
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