Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use Ccre/gemma-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ccre/gemma-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ccre/gemma-embedding-model") 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
Upload 2 files
Browse files- 2_Dense/config.json +6 -0
- 2_Dense/model.safetensors +3 -0
2_Dense/config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"in_features": 768,
|
| 3 |
+
"out_features": 3072,
|
| 4 |
+
"bias": false,
|
| 5 |
+
"activation_function": "torch.nn.modules.linear.Identity"
|
| 6 |
+
}
|
2_Dense/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c327f2acb00149676ade24a75e11eb6ebbd367f9ee050267ba56829d2979f702
|
| 3 |
+
size 9437272
|