Sentence Similarity
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
xlm-roberta
feature-extraction
language
granite
embeddings
multilingual
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use ibm-granite/granite-embedding-278m-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-embedding-278m-multilingual with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-embedding-278m-multilingual") model = AutoModel.from_pretrained("ibm-granite/granite-embedding-278m-multilingual") - sentence-transformers
How to use ibm-granite/granite-embedding-278m-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibm-granite/granite-embedding-278m-multilingual") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Ctrl+K