|
|
--- |
|
|
pipeline_tag: feature-extraction |
|
|
tags: |
|
|
- NPU |
|
|
--- |
|
|
# EmbeddingGemma-300M (NPU) |
|
|
|
|
|
|
|
|
## Model Description |
|
|
**EmbeddingGemma** is a 300M-parameter open embedding model developed by **Google DeepMind**. |
|
|
It is built from **Gemma 3** (with T5Gemma initialization) and the same research and technology used in **Gemini models**. |
|
|
|
|
|
The model produces **vector representations of text**, making it well-suited for **search, retrieval, classification, clustering, and semantic similarity tasks**. |
|
|
It was trained on **100+ languages** with ~320B tokens, optimized for **on-device efficiency** (mobile, laptops, desktops). |
|
|
|
|
|
|
|
|
## Features |
|
|
- **Compact and efficient**: 300M parameters, optimized for on-device use. |
|
|
- **Multilingual**: trained on 100+ spoken languages. |
|
|
- **Flexible embeddings**: default dimension **768**, with support for **512, 256, 128** via Matryoshka Representation Learning (MRL). |
|
|
- **Wide task coverage**: retrieval, QA, fact-checking, classification, clustering, similarity. |
|
|
- **Commercial-friendly**: open weights available for research and production. |
|
|
|
|
|
|
|
|
## Use Cases |
|
|
- Semantic similarity and recommendation systems |
|
|
- Document, code, and web search |
|
|
- Clustering for organization, research, and anomaly detection |
|
|
- Classification (e.g., sentiment, spam detection) |
|
|
- Fact verification and QA embeddings |
|
|
- Code retrieval for programming assistance |
|
|
|
|
|
|
|
|
## Inputs and Outputs |
|
|
**Input**: |
|
|
- **Type**: Text string (e.g., query, prompt, document) |
|
|
- **Max Length**: 2048 tokens |
|
|
|
|
|
**Output**: |
|
|
- **Type**: Embedding vector (default 768d) |
|
|
- **Options**: 512 / 256 / 128 dimensions via truncation & re-normalization (MRL) |
|
|
|
|
|
|
|
|
## Limitations & Responsible Use |
|
|
This model has known limitations: |
|
|
- **Bias & coverage**: quality depends on training data diversity. |
|
|
- **Nuance & ambiguity**: may struggle with sarcasm, figurative language. |
|
|
- **Ethical concerns**: risk of bias perpetuation, privacy leakage, or malicious misuse. |
|
|
|
|
|
Mitigations: |
|
|
- CSAM and sensitive data filtering applied. |
|
|
- Users should adhere to **Gemma Responsible AI guidelines** and **Prohibited Use Policy**. |
|
|
|
|
|
|
|
|
## License |
|
|
This model is released under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license. |
|
|
Non-commercial use, modification, and redistribution are permitted with attribution. |
|
|
For commercial licensing, please contact **dev@nexa.ai**. |
|
|
|
|
|
|
|
|
## References |
|
|
- [nexaSDK](https://sdk.nexa.ai) |
|
|
|
|
|
|
|
|
## Support |
|
|
For SDK-related issues, visit [sdk.nexa.ai](https://sdk.nexa.ai). |
|
|
For model-specific questions, open an issue in this repository. |
