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]
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")
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