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
- Xet hash:
- c9c6169a4105c6b68b46b3eb282cb28f6cc83142cd7fe8f92e717286334632c0
- Size of remote file:
- 33.4 MB
- SHA256:
- 0087c868b33bad550a78a08d19798cfd7f713cde4f020803b8f51f405503e15f
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