---
license: mit
base_model:
  - BAAI/bge-reranker-v2-m3
pipeline_tag: sentence-similarity
---

# Model Card — BGE-Reranker-VietFinance

## Overview

This is a cross-encoder reranker finetuned from `BAAI/bge-reranker-v2-m3` to score (query, passage) pairs for retrieval reranking in Vietnamese financial/news search systems.

## Intended Use

- Primary: improve Hit@K by re-ranking candidate passages produced by an upstream retriever (BM25 + embedding-based).
- Not for: standalone generation, non-Vietnamese domains, or high-stakes automated decisions without human review.

## Essential Statistics

- Base model: `BAAI/bge-reranker-v2-m3`
- Embedding model (used for retrieval/hard-negative mining): `BAAI/bge-m3`
- Max sequence length for reranking: 1536 tokens (inputs longer than this are truncated)
- Retrieval strategy: temporal-aware hybrid (BM25 + dense embeddings with temporal boosting)
- Saved artifacts in this folder: `model.safetensors`, `tokenizer.json`, `tokenizer_config.json`, `config.json`.

## Evaluation (concise)

- Procedure: retrieve candidate passages (temporal-aware hybrid) → rerank with cross-encoder → compute Hit@K for K ∈ {1,3,5,10,20}.
- Numeric results are saved in run outputs (summary CSVs / JSONL); include them here if you want the actual Hit@K values embedded.

## Limitations & Risks

- Domain-specific: optimized for Vietnamese financial/news passages; generalization outside this domain/language is uncertain.
- Retrieval dependency: reranker cannot recover gold passages not present among retrieval candidates.
- Truncation risk: 1536-token truncation may drop important context for long passages.
- Data & license: dataset provenance and license are not specified here — verify before public distribution.

## Bias & Safety

- Model reflects biases in the source news corpus (topic/regional biases).
- Temporal heuristics can misinterpret ambiguous locale-specific dates and cause incorrect boosts.
- Do not rely on reranker outputs alone for automated financial, legal, or medical decisions.

## Quick usage (inference)

Load this checkpoint with `AutoTokenizer` / `AutoModelForSequenceClassification`, tokenize (query, passage) pairs, score in eval mode, and sort candidates by descending score (higher = more relevant).

## License & Citation

- License: not specified in the checkpoint — confirm before redistribution.
- Cite the base models `BAAI/bge-reranker-v2-m3` and `BAAI/bge-m3` when reporting results.