Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:4992
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use AROY76/Embedding-gemma-300M-skills with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AROY76/Embedding-gemma-300M-skills with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AROY76/Embedding-gemma-300M-skills") sentences = [ "Client onboarding, implementation, project management, communication, Salesforce, G-suite, Asana, Single Sign-On (SSO), SFTP, data analysis", "A software engineer uses Python and GitHub to automate testing processes.", "Setting up clients in Salesforce and G-suite efficiently requires strong project management and clear communication.", "Choosing between cloud storage solutions like Dropbox and Google Drive can be challenging." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!