Semantic Router Dataset for Training Tool-Calling LLMs

Synthetic data designed for training lightweight large language models in semantic tool routing. The dataset's author, organization, and size are unspecified. Its last update date is unknown.

Use Cases

• Fine-tuning a model for semantic tool routing based on the described task.
• Benchmarking lightweight LLMs on tool-calling accuracy using synthetic data.
• Developing a classifier for API endpoint selection based on user query semantics.

Strengths

• Data is explicitly designed for a specific machine learning task: semantic tool routing.
• The synthetic nature allows for controlled generation of training examples.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

kaggle

Collection Method

Synthetically generated.