Feature Extraction
sentence-transformers
ONNX
Safetensors
OpenVINO
Transformers
modernbert
granite
embeddings
multilingual
mteb
sentence-similarity
matryoshka
text-embeddings-inference
Instructions to use ibm-granite/granite-embedding-311m-multilingual-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-311m-multilingual-r2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibm-granite/granite-embedding-311m-multilingual-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-311m-multilingual-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-granite/granite-embedding-311m-multilingual-r2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-embedding-311m-multilingual-r2") model = AutoModel.from_pretrained("ibm-granite/granite-embedding-311m-multilingual-r2") - Inference
- Notebooks
- Google Colab
- Kaggle
Reranker model for multilingual
#2
by Duonglv - opened
Hello IBM team,
Your embedding model is great. It's outperform many bigger models.
I have tried it in my language - vietnamese, it works well, better than many bigger one.
However, could you train a re-ranker model for multilingual?
I see you released ibm-granite/granite-embedding-reranker-english-r2, but it's only for English.
Thank a lots.