Sentence Similarity
Transformers
Safetensors
sentence-transformers
English
Korean
gemma3_text
feature-extraction
embedding
gemma
text-embedding
retrieval
matryoshka
academic-search
scientific-search
Instructions to use LinerAI/embeddinggemma-300m-academic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinerAI/embeddinggemma-300m-academic with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LinerAI/embeddinggemma-300m-academic") model = AutoModel.from_pretrained("LinerAI/embeddinggemma-300m-academic") - sentence-transformers
How to use LinerAI/embeddinggemma-300m-academic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LinerAI/embeddinggemma-300m-academic") 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
- Xet hash:
- 07566b3f6683b7f7db77265086857ebfc171667ab0f1c8e2302ae985d61585c8
- Size of remote file:
- 8.53 kB
- SHA256:
- 5810ea23d2e9f4ae0f1a8b7211c4a3d5c233b9eddb42d388ab26d597029fa94a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.