MR-Eder/embedding-triples
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How to use MR-Eder/GRAG-BGE-M3-Triples-Basic-Autotrain-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("MR-Eder/GRAG-BGE-M3-Triples-Basic-Autotrain-v1")
sentences = [
"search_query: i love autotrain",
"search_query: huggingface auto train",
"search_query: hugging face auto train",
"search_query: i love autotrain"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]No validation metrics available
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the Hugging Face Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'search_query: autotrain',
'search_query: auto train',
'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Base model
BAAI/bge-m3