Feature Extraction
sentence-transformers
Safetensors
English
bert
sentence-similarity
retrieval
tool-use
llm-agent
r-language
text-embeddings-inference
Instructions to use Stephen-SMJ/DARE-R-Retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Stephen-SMJ/DARE-R-Retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Stephen-SMJ/DARE-R-Retriever") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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# DARE: Distribution-Aware Retrieval for R Functions
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DARE (Distribution-Aware Retrieval) is a specialized bi-encoder model designed to retrieve statistical and data analysis tools (R functions) based on **both natural language user queries and underlying data constraints** (data profiles).
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It is fine-tuned from `sentence-transformers/all-MiniLM-L6-v2` to serve as a high-precision tool retrieval module for Large Language Model (LLM) Agents in automated data science workflows.
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# DARE: Distribution-Aware Retrieval for R Functions
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DARE (Distribution-Aware Retrieval Embedding) is a specialized bi-encoder model designed to retrieve statistical and data analysis tools (R functions) based on **both natural language user queries and underlying data constraints** (data profiles).
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It is fine-tuned from `sentence-transformers/all-MiniLM-L6-v2` to serve as a high-precision tool retrieval module for Large Language Model (LLM) Agents in automated data science workflows.
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