Feature Extraction
sentence-transformers
Safetensors
multilingual
embedding_gemma2
embedding
sentence-similarity
code
Instructions to use jayyun98/embeddinggemma-2-text-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jayyun98/embeddinggemma-2-text-270m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jayyun98/embeddinggemma-2-text-270m") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download verification.json from jayyun98/embeddinggemma-2-text-270m: direct link, hf CLI and curl.
- Browser
- Download file 3.72 kB
-
https://huggingface.co/jayyun98/embeddinggemma-2-text-270m/resolve/main/verification.json
- Command line
-
hf download hf://jayyun98/embeddinggemma-2-text-270m/verification.json
-
curl -L -o verification.json https://huggingface.co/jayyun98/embeddinggemma-2-text-270m/resolve/main/verification.json
3.72 kB
| { | |
| "source_model": "google/embeddinggemma-2", | |
| "source_revision": "914f7f89142e33e77833254d9c9b90c3cef7303b", | |
| "environment": { | |
| "platform": "macOS-26.4-arm64-arm-64bit", | |
| "torch": "2.14.1", | |
| "transformers": "5.19.0", | |
| "sentence_transformers": "6.1.0", | |
| "device": "cpu", | |
| "inference_dtype": "float32", | |
| "attention": "eager" | |
| }, | |
| "variants": { | |
| "embeddinggemma-2-text-270m": { | |
| "status": "passed", | |
| "parameter_count": 271002624, | |
| "excluded_encoders_absent": true, | |
| "runtime_weight_keys_match": true, | |
| "checks": { | |
| "search_english_korean": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999998211860657, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "documents": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999998807907104, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "code": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999999403953552, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "mrl_128": { | |
| "shape": [ | |
| 2, | |
| 128 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 1.4901161193847656e-08 | |
| }, | |
| "mrl_256": { | |
| "shape": [ | |
| 2, | |
| 256 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 2.9802322387695312e-08 | |
| }, | |
| "mrl_512": { | |
| "shape": [ | |
| 2, | |
| 512 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 2.9802322387695312e-08 | |
| } | |
| } | |
| }, | |
| "embeddinggemma-2-text-image-440m": { | |
| "status": "passed", | |
| "parameter_count": 438760448, | |
| "excluded_encoders_absent": true, | |
| "runtime_weight_keys_match": true, | |
| "checks": { | |
| "search_english_korean": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999998211860657, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "documents": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999998807907104, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "code": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999999403953552, | |
| "shape": [ | |
| 2, | |
| 768 | |
| ] | |
| }, | |
| "image": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999997019767761, | |
| "shape": [ | |
| 1, | |
| 768 | |
| ] | |
| }, | |
| "mixed_text_image": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 1.0, | |
| "shape": [ | |
| 1, | |
| 768 | |
| ] | |
| }, | |
| "interleaved_two_images": { | |
| "max_absolute_difference": 0.0, | |
| "min_cosine_similarity": 0.9999998807907104, | |
| "shape": [ | |
| 1, | |
| 768 | |
| ] | |
| }, | |
| "mrl_128": { | |
| "shape": [ | |
| 2, | |
| 128 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 1.4901161193847656e-08 | |
| }, | |
| "mrl_256": { | |
| "shape": [ | |
| 2, | |
| 256 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 2.9802322387695312e-08 | |
| }, | |
| "mrl_512": { | |
| "shape": [ | |
| 2, | |
| 512 | |
| ], | |
| "unit_norm": true, | |
| "max_absolute_difference": 2.9802322387695312e-08 | |
| } | |
| } | |
| } | |
| }, | |
| "elapsed_seconds": 17.205 | |
| } | |