MR-Eder/GRAG-BGE-M3-Triples-Basic-Autotrain-v1

library_name: sentence-transformers tags:

• —sentence-transformers
• —sentence-similarity
• —feature-extraction
• —autotrain base_model: BAAI/bge-m3 widget:
• —sourcesentence: 'searchquery: i love autotrain' sentences:
• —'search_query: huggingface auto train'
• —'search_query: hugging face auto train'
• —'searchquery: i love autotrain' pipelinetag: sentence-similarity datasets:
• —MR-Eder/embedding-triples ---

Model Trained Using AutoTrain

• —Problem type: Sentence Transformers

Validation Metrics

No validation metrics available

Usage

Direct Usage (Sentence Transformers)

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)