| # Evaluation | |
| The scripts behind the numbers on the model card. You only need [uv](https://docs.astral.sh/uv/getting-started/installation/). | |
| ```bash | |
| uv sync | |
| uv run song_describer.py jaisawhney/SonicEmbed | |
| uv run clap_eval.py | |
| uv run mteb_tasks.py jaisawhney/SonicEmbed --output-dir mteb-results/sonicembed | |
| ``` | |
| Models can be a Hugging Face ID or a local folder, so `google/embeddinggemma-2` scores the base model. Add `--device mps` on Apple silicon or `--device cpu` if you don't have an NVIDIA GPU. | |
| Song Describer downloads 3.3 GB on the first run. | |