Feature Extraction
Transformers
Safetensors
PyTorch
sentence-transformers
multilingual
gemma3_text
retrieval
semantic-similarity
classification
clustering
bitext-mining
reranking
text-embeddings-inference
Instructions to use JBrightmanAI/GEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JBrightmanAI/GEmbedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="JBrightmanAI/GEmbedder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JBrightmanAI/GEmbedder") model = AutoModel.from_pretrained("JBrightmanAI/GEmbedder", device_map="auto") - sentence-transformers
How to use JBrightmanAI/GEmbedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JBrightmanAI/GEmbedder") 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
| { | |
| "prompts": { | |
| "web_search_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: ", | |
| "sts_query": "Instruct: Retrieve semantically similar text\nQuery: ", | |
| "bitext_query": "Instruct: Retrieve parallel sentences\nQuery: " | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |