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
| { | |
| "boi_token": "<start_of_image>", | |
| "bos_token": { | |
| "content": "<bos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eoi_token": "<end_of_image>", | |
| "eos_token": { | |
| "content": "<eos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "image_token": "<image_soft_token>", | |
| "pad_token": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |