Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
roberta
feature-extraction
language
granite
embeddings
mteb
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use ibm-granite/granite-embedding-125m-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ibm-granite/granite-embedding-125m-english with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibm-granite/granite-embedding-125m-english") 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] - Transformers
How to use ibm-granite/granite-embedding-125m-english with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-embedding-125m-english") model = AutoModel.from_pretrained("ibm-granite/granite-embedding-125m-english", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2588e0b460f734278c25cdd3c23633792e03df5c04a466fcb2d1e301fc4927a2
- Size of remote file:
- 249 MB
- SHA256:
- ee70cb80637b769409e4ccfde7d188bb2c564495d1a63cde3ca60741b2a7302d
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