---
license: apache-2.0
language:
- en
base_model:
- naver/trecdl22-crossencoder-debertav3
pipeline_tag: text-classification
tags:
- reranker
- cross-encoder
- financial-qa
library_name: transformers
---

# FinQA-Table-random-DeBERTa-Reranker

A passage reranker for the **HiREC** framework, fine-tuned from `naver/trecdl22-crossencoder-debertav3` on table data from the FinQA training set. General-purpose rerankers often fail to capture table-specific cues (titles, periods, indicators) that matter more than raw numerical values; this model is adapted to address that gap.

- 📄 Paper: [ACL 2025 Findings](https://aclanthology.org/2025.findings-acl.855/)
- 💻 Code: [LOFin-bench-HiREC](https://github.com/deep-over/LOFin-bench-HiREC)

## Training Data

Constructed from the **FinQA** training set, where each question is paired with an evidence page containing the gold table.

- **Positive passages:** tables located on the evidence page of each question.
- **Negative passages:** tables sampled from pages *other than* the evidence page within the same document (**random** negative sampling).
- For each positive, `n_neg = 8` negatives are drawn.

## Training Setup

- **Base model:** `naver/trecdl22-crossencoder-debertav3`
- **Objective:** Binary cross-entropy on `(query, passage)` pairs; the cross-encoder applies an internal sigmoid, producing relevance scores in [0, 1].
- Batch size: 128 / Epochs: 3 / Learning rate: 2e-7
- **Hardware:** 1× NVIDIA GeForce RTX 4090

## Citation

```bibtex
@inproceedings{choe-etal-2025-hierarchical,
    title     = {Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents},
    author    = {Choe, Jaeyoung and Kim, Jihoon and Jung, Woohwan},
    booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
    year      = {2025},
    url       = {https://aclanthology.org/2025.findings-acl.855/}
}
```