README.md

metadata

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:
  - google/embeddinggemma-300m

BPVELA-G300M

BPVELA-G300M is the efficiency-first BPVELA release line for Traditional Chinese retrieval and embedding use cases.

繁體中文說明

BPVELA-G300M 是 BPVELA 目前的 efficiency-first 系列，針對繁體中文語意檢索與較低成本部署場景做優化，適合需要兼顧效果與資源效率的使用情境。

模型摘要

• 系列版本：v1.0.0
• 基底模型：google/embeddinggemma-300m
• 釋出形式：LoRA adapter 加上 SentenceTransformer 組件
• 建議用途：semantic retrieval、retrieval-first RAG、較低成本 embedding deployment
• 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。

存取前置條件

BPVELA-G300M 建立於 google/embeddinggemma-300m 之上，因此除了本 adapter repository 之外，使用者也必須能夠存取上游 Gemma base model。

• 請先在 Hugging Face 上完成 google/embeddinggemma-300m 的 gated access 申請與條款同意
• 若使用 fine-grained token，請確認 token 已開啟 public gated repositories 的讀取權限
• 若載入時出現 401 Unauthorized 或 403 Forbidden，且訊息指向 google/embeddinggemma-300m/resolve/...，通常表示缺少上游 Gemma 存取權，而不是本 adapter repository 本身有問題

驗證摘要

• Taiwan-md pair benchmark：Spearman 0.8319、Pearson 0.8953
• Wrapped retrieval smoke：pass rate 1.0000、retrieval hit rate 1.0000、top-1 rate 0.8333

Query / Document 格式

這條模型線基於 EmbeddingGemma，做檢索時建議保留 prompt-style 格式。

• Query：task: search result | query: 你的問題
• Document：title: none | text: 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 google/embeddinggemma-300m：Hugging Face 標示為 gemma，且需同意 Google 的 Gemma 使用條款
• 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-G300M 的 LoRA adapter 權重、模型卡與相關說明文件，並不包含 google/embeddinggemma-300m 的完整基底模型權重。

BPVELA-G300M 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

此外，因本 adapter 建立於 google/embeddinggemma-300m 之上，任何載入、使用、分享或部署行為，仍須另外遵守上游 Gemma 模型的使用條款與限制。

Summary

• Series version: v1.0.0
• Base model: google/embeddinggemma-300m
• Release type: LoRA adapter plus SentenceTransformer modules
• Recommended usage: semantic retrieval, retrieval-first RAG, lower-cost embedding deployment
• 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.

Access Requirements

BPVELA-G300M is built on top of google/embeddinggemma-300m, so users must be able to access the upstream Gemma base model in addition to this adapter repository.

• Request and accept gated access for google/embeddinggemma-300m on Hugging Face first
• If you use a fine-grained token, enable read access to public gated repositories
• If loading fails with 401 Unauthorized or 403 Forbidden against google/embeddinggemma-300m/resolve/..., the issue is usually missing upstream Gemma access rather than a problem with this adapter repository

Validation Snapshot

• Taiwan-md pair benchmark: Spearman 0.8319, Pearson 0.8953
• Wrapped retrieval smoke: pass rate 1.0000, retrieval hit rate 1.0000, top-1 rate 0.8333

Query And Document Formatting

This line is based on EmbeddingGemma. For retrieval, keep the prompt-style formatting.

• Query: task: search result | query: your question
• Document: title: none | text: your document

Loading Example

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

base_model = "google/embeddinggemma-300m"
adapter_repo = "BluePlanetAI/BPVELA-G300M"

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")
dense_1 = Dense.load(adapter_repo, subfolder="2_Dense")
dense_2 = Dense.load(adapter_repo, subfolder="3_Dense")
normalize = Normalize.load(adapter_repo, subfolder="4_Normalize")

model = SentenceTransformer(modules=[transformer, pooling, dense_1, dense_2, normalize])

emb = model.encode(["task: search result | 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 google/embeddinggemma-300m: marked as gemma on Hugging Face and access is gated by Google's Gemma terms
• 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-G300M LoRA adapter weights, model card, and related documentation only. It does not redistribute the full base-model weights of google/embeddinggemma-300m.

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

In addition, any use, sharing, or deployment of this adapter remains subject to the upstream Gemma model terms and restrictions.