Update README.md
Browse filesMove "model overview" to below bullet points to match formatting for other Gemma models.
README.md
CHANGED
|
@@ -36,9 +36,6 @@ library_name: transformers
|
|
| 36 |
|
| 37 |
Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.
|
| 38 |
|
| 39 |
-
|
| 40 |
-
## **Model Overview**
|
| 41 |
-
|
| 42 |
EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features:
|
| 43 |
|
| 44 |
* **Native multimodality:** Native multimodality: Unifies 4 modalities (text, images, video, and audio) in a single shared 768-dimensional embedding space.
|
|
@@ -48,7 +45,7 @@ EmbeddingGemma 2 builds upon the architectural and capability advancements of Ge
|
|
| 48 |
* **Context length:** 8K token context window, capable of processing minutes of audio or video.
|
| 49 |
* **Task-steered representations:** Uses lightweight text instruction prefixes to optimize embeddings for different tasks (search, classification, clustering, semantic similarity, etc.).
|
| 50 |
|
| 51 |
-
###
|
| 52 |
|
| 53 |
| Parameters | Total | 740M |
|
| 54 |
| :---- | :---- | :---- |
|
|
|
|
| 36 |
|
| 37 |
Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.
|
| 38 |
|
|
|
|
|
|
|
|
|
|
| 39 |
EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features:
|
| 40 |
|
| 41 |
* **Native multimodality:** Native multimodality: Unifies 4 modalities (text, images, video, and audio) in a single shared 768-dimensional embedding space.
|
|
|
|
| 45 |
* **Context length:** 8K token context window, capable of processing minutes of audio or video.
|
| 46 |
* **Task-steered representations:** Uses lightweight text instruction prefixes to optimize embeddings for different tasks (search, classification, clustering, semantic similarity, etc.).
|
| 47 |
|
| 48 |
+
### Model Overview
|
| 49 |
|
| 50 |
| Parameters | Total | 740M |
|
| 51 |
| :---- | :---- | :---- |
|