Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use RedHatAI/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RedHatAI/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RedHatAI/embeddinggemma-300m") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -2,6 +2,12 @@
|
|
| 2 |
license: gemma
|
| 3 |
pipeline_tag: sentence-similarity
|
| 4 |
library_name: sentence-transformers
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
tags:
|
| 6 |
- sentence-transformers
|
| 7 |
- sentence-similarity
|
|
|
|
| 2 |
license: gemma
|
| 3 |
pipeline_tag: sentence-similarity
|
| 4 |
library_name: sentence-transformers
|
| 5 |
+
license_name: gemma
|
| 6 |
+
provider: Google
|
| 7 |
+
name: RedHatAI/embeddinggemma-300m
|
| 8 |
+
license_link: https://ai.google.dev/gemma/terms
|
| 9 |
+
description: EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models.
|
| 10 |
+
readme: https://huggingface.co/RedHatAI/embeddinggemma-300m/main/README.md
|
| 11 |
tags:
|
| 12 |
- sentence-transformers
|
| 13 |
- sentence-similarity
|