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
|
@@ -10,6 +10,8 @@ description: EmbeddingGemma is a 300M parameter, state-of-the-art for its size,
|
|
| 10 |
readme: https://huggingface.co/RedHatAI/embeddinggemma-300m/main/README.md
|
| 11 |
tasks:
|
| 12 |
- text-embedding
|
|
|
|
|
|
|
| 13 |
tags:
|
| 14 |
- sentence-transformers
|
| 15 |
- sentence-similarity
|
|
|
|
| 10 |
readme: https://huggingface.co/RedHatAI/embeddinggemma-300m/main/README.md
|
| 11 |
tasks:
|
| 12 |
- text-embedding
|
| 13 |
+
hardware_tag:
|
| 14 |
+
- Intel Xeon
|
| 15 |
tags:
|
| 16 |
- sentence-transformers
|
| 17 |
- sentence-similarity
|