Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use krishanusinha20/multi-agentic-sql-generator-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use krishanusinha20/multi-agentic-sql-generator-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("krishanusinha20/multi-agentic-sql-generator-model") sentences = [ "Find the most popular payment method used in 2024.", "SELECT * FROM orders WHERE customer_id = 42;", "SELECT customer_id, COUNT(order_id) AS order_count FROM orders WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31' GROUP BY customer_id HAVING order_count >= 3;", "SELECT payment_method, COUNT(*) AS usage_count FROM payments WHERE payment_date BETWEEN '2024-01-01' AND '2024-12-31' GROUP BY payment_method ORDER BY usage_count DESC LIMIT 1;" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18504e4797dd5bb9de84272164e8f320cdf8e6866d0fed1295a3277631c2ba32
- Size of remote file:
- 90.9 MB
- SHA256:
- dfdc6cf54776c94f6f8e25957c93425a2868c8f95373014922dc6645bbff0cb9
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