CALM — credit scoring and risk assessment
Collection
Credit scoring, fraud detection, financial distress and claim analysis tasks from CALM (arXiv:2310.00566). • 10 items • Updated
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Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
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10K - 100K
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Request access to CALM · Lending Club benchmark (credit scoring)
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CALM · Lending Club benchmark (credit scoring)
📄 Paper · 💻 Code · 🌐 The Fin AI
Part of CALM — Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models (arXiv:2310.00566).
| Task | credit scoring |
| Original dataset | Lending Club |
| Evaluation metric | Accuracy, F1, Miss |
| Source license | CC0 1.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-lendingclub-benchmark", split="test")
print(ds[0])
Example prompt
Predict the loan status using the features below. Directly respond with 'fullypaid' if the loan is fully repaid or 'chargeoff' if the loan is charged off.
Text: ' addressState: ga, annualIncome: 66400.00, delinquencyIn2Years: 2.00, employmentLength: 10+Years, ficoRangeHigh: 669.00, ficoRangeLow: 665.00, grade: f, homeOwnership: mortgage, inquiriesIn6Months: 1.00, installment: 267.07, interestRate:…
Dataset Structure
| Split | Rows |
|---|---|
train |
9,417 |
valid |
1,345 |
test |
2,691 |
| Field | Description |
|---|---|
id |
Example id |
query |
Full instruction prompt given to the model |
answer |
Gold answer / label text |
choices |
Label space |
gold |
Index of the gold label in choices |
License
The original data is released under CC0 1.0 (CALM paper, Table 1).
Citation
Please cite CALM and the original dataset (Lending Club):
@misc{feng2024empoweringmanybiasingfew,
title={Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models},
author={Duanyu Feng and Yongfu Dai and Jimin Huang and Yifang Zhang and Qianqian Xie and Weiguang Han and Zhengyu Chen and Alejandro Lopez-Lira and Hao Wang},
year={2024},
eprint={2310.00566},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2310.00566},
}
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