Update README.md

README.md

@@ -1,3 +1,47 @@
 ---
 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/}
+}
+```