---
language:
- zh
- en
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: cc-by-sa-4.0
tags:
- sentence-transformers
- text-embeddings-inference
- feature-extraction
- semantic-search
- retrieval
- traditional-chinese
- lora
base_model:
- intfloat/multilingual-e5-large
---

# BPVELA-E560M

`BPVELA-E560M` is the accuracy-first BPVELA release line for Traditional Chinese retrieval and embedding use cases.

## 繁體中文說明

`BPVELA-E560M` 是 BPVELA 目前的 accuracy-first 系列，針對繁體中文語意檢索、相似度比對與 retrieval-first RAG 場景做優化。

### 模型摘要

- 系列版本：`v1.0.0`
- 基底模型：`intfloat/multilingual-e5-large`
- 釋出形式：LoRA adapter 加上 SentenceTransformer 組件
- 建議用途：semantic retrieval、retrieval-first RAG、similarity search
- 主要語言：Traditional Chinese / 繁體中文

### 重要說明

這個 repository 釋出的是 LoRA adapter，不是 merged full checkpoint。使用時需要以 base model 為底，再載入這個 adapter。

### 驗證摘要

- Taiwan-md pair benchmark：Spearman `0.8400`、Pearson `0.9224`
- Wrapped retrieval smoke：pass rate `1.0000`、retrieval hit rate `1.0000`、top-1 rate `0.9667`

### Query / Passage 格式

這條模型線基於 E5，做檢索時建議保留標準前綴。

- Query：`query: 你的問題`
- Passage：`passage: 文件內容`

### 備註

- `bpvela_model_config.yaml` 保留了專案內部使用的載入設定。
- 這個公開模型 repo 不需要包含 Taiwan-md corpus 或 FAISS index。
- 公開前請再確認最終 license。

### 授權說明

- Taiwan-MD 內容授權：`CC BY-SA 4.0`
- BPVELA 專案程式碼授權：`MIT`
- 基底模型 `intfloat/multilingual-e5-large`：Hugging Face 標示為 `MIT`
- 本 repo 釋出的 adapter 權重與模型卡內容，建議以 `CC BY-SA 4.0` 方式對外說明

本 repository 公開的是 BPVELA-E560M 的 LoRA adapter 權重、模型卡與相關說明文件，並不包含 `intfloat/multilingual-e5-large` 的完整基底模型權重。

BPVELA-E560M 的訓練與優化過程使用了 Taiwan-MD 內容；依目前資料來源條件，建議將本 adapter 權重與模型卡內容以 `CC BY-SA 4.0` 對外說明與散布。

任何再散布、修改版散布、或以本 adapter 為基礎的公開衍生釋出，建議：

- 保留原始出處與適當署名
- 清楚標示修改情形
- 以相同或相容的分享方式提供衍生內容

使用者在載入與使用本 adapter 時，仍需自行遵守上游基底模型 `intfloat/multilingual-e5-large` 的授權條件。

## Summary

- Series version: `v1.0.0`
- Base model: `intfloat/multilingual-e5-large`
- Release type: LoRA adapter plus SentenceTransformer modules
- Recommended usage: semantic retrieval, retrieval-first RAG, similarity search
- Language focus: Traditional Chinese

## Important

This repository contains a LoRA adapter release, not a merged full checkpoint. Load it on top of the base model.

## Validation Snapshot

- Taiwan-md pair benchmark: Spearman `0.8400`, Pearson `0.9224`
- Wrapped retrieval smoke: pass rate `1.0000`, retrieval hit rate `1.0000`, top-1 rate `0.9667`

## Query And Passage Formatting

This line is based on E5. For retrieval, keep the standard E5 prefixes.

- Query: `query: your question`
- Passage: `passage: your document`

## Loading Example

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Normalize, Pooling, Transformer
from peft import PeftModel

base_model = "intfloat/multilingual-e5-large"
adapter_repo = "BluePlanetAI/BPVELA-E560M"

transformer = Transformer(base_model)
transformer.auto_model = PeftModel.from_pretrained(
    transformer.auto_model,
    adapter_repo,
    is_trainable=False,
)

pooling = Pooling.load(adapter_repo, subfolder="1_Pooling")
normalize = Normalize.load(adapter_repo, subfolder="2_Normalize")

model = SentenceTransformer(modules=[transformer, pooling, normalize])

emb = model.encode(["query: 台灣颱風災害應變流程"], normalize_embeddings=True)
print(len(emb[0]))
```

## Notes

- `bpvela_model_config.yaml` is included as the project-side loading reference.
- This public model repo does not need to include the Taiwan-md corpus or FAISS index.
- Release owner should finalize the public license before publishing.

## License Notes

- Taiwan-MD content license: `CC BY-SA 4.0`
- BPVELA project code license: `MIT`
- Base model `intfloat/multilingual-e5-large`: marked as `MIT` on Hugging Face
- The adapter weights and model card content published in this repo are best documented as `CC BY-SA 4.0`

This repository publishes the BPVELA-E560M LoRA adapter weights, model card, and related documentation only. It does not redistribute the full base-model weights of `intfloat/multilingual-e5-large`.

Because the training and optimization process uses Taiwan-MD content, the adapter release and model card are best documented for public distribution under `CC BY-SA 4.0`.

For redistribution, modified redistribution, or public derivative releases based on this adapter, users should:

- preserve attribution to the original release
- clearly indicate modifications
- keep the share-alike expectations for the released derivative materials

Use of this adapter remains subject to the applicable license terms of the upstream base model.