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.gitattributes

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10-k.zip

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+oid sha256:4e1bff2b5e37ba3429d3ddb8a15d447c7903d4fafd59a4efdbefba08a2036aa0
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README.md

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+---
+license: cc-by-nc-4.0
+dataset_info:
+  features:
+  - name: _id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: reasoning
+    dtype: bool
+  - name: category
+    dtype: string
+  - name: references
+    sequence: string
+  - name: answer
+    dtype: string
+  - name: type
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 26474135
+    num_examples: 5703
+  download_size: 13097307
+  dataset_size: 26474135
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+# FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
+
+**FinDER** is a benchmark dataset designed for evaluating **Retrieval-Augmented Generation (RAG)** in financial question answering. It consists of **5,703 expert-annotated query–evidence–answer triplets** derived from real-world 10-K filings and ambiguous financial queries submitted by industry professionals.
+
+This dataset captures the domain-specific challenges of financial QA, including short, acronym-heavy queries and the need for precise retrieval over lengthy, complex documents.
+
+## 🔗 Paper  
+[arXiv:2504.15800](https://arxiv.org/abs/2504.15800)  
+```
+@misc{choi2025finderfinancialdatasetquestion,
+  title={FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation}, 
+  author={Chanyeol Choi and Jihoon Kwon and Jaeseon Ha and Hojun Choi and Chaewoon Kim and Yongjae Lee and Jy-yong Sohn and Alejandro Lopez-Lira},
+  year={2025},
+  eprint={2504.15800},
+  archivePrefix={arXiv},
+  primaryClass={cs.IR},
+  url={https://arxiv.org/abs/2504.15800}, 
+}
+```
+
+## 📋 Terms of Use  
+By using this dataset, you agree to:
+
+- Provide **proper citation and attribution** to the original authors in any derived work or publication.

data/train-00000-of-00001.parquet

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