Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
modernbert
granite
embeddings
mteb
text-embeddings-inference
Instructions to use ibm-granite/granite-embedding-english-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ibm-granite/granite-embedding-english-r2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibm-granite/granite-embedding-english-r2") 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] - Transformers
How to use ibm-granite/granite-embedding-english-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-granite/granite-embedding-english-r2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-embedding-english-r2") model = AutoModel.from_pretrained("ibm-granite/granite-embedding-english-r2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dfa220fe563d836ef10aa1fe4f9eb80e5a2b8f816ed9df58689ee122861b956
- Size of remote file:
- 298 MB
- SHA256:
- 2ec36f187f6fde9a53f80ed264731d09743e7282ece9ae61a8f5d0b63f01c260
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.