Embedding Atlas
A Python package that provides a command-line tool to visualize a dataset with embeddings. It also includes a Python notebook (e.g., Jupyter) widget and a Streamlit widget.
- Documentation: https://apple.github.io/embedding-atlas
- GitHub: https://github.com/apple/embedding-atlas
Installation
pip install embedding-atlas
and then launch the command line tool:
embedding-atlas [OPTIONS] INPUTS...
Loading Data
You can load your data in two ways: locally or from Hugging Face.
Loading Local Data
To get started with your own data, run:
embedding-atlas path_to_dataset.parquet
Loading Hugging Face Data
You can instead load datasets from Hugging Face:
embedding-atlas huggingface_org/dataset_name
Visualizing Embedding Projections
To visualize embedding projections, pre-compute the X and Y coordinates, and specify the column names with --x and --y, such as:
embedding-atlas path_to_dataset.parquet --x projection_x --y projection_y
You may use the SentenceTransformers package to compute high-dimensional embeddings from text data, and then use the UMAP package to compute 2D projections.
Using Pre-computed Vectors
If you already have pre-computed embedding vectors (but not the 2D projections), you can specify the column containing the vectors with --vector:
embedding-atlas path_to_dataset.parquet --vector embedding_vectors
This will apply UMAP dimensionality reduction to your pre-existing vectors without recomputing embeddings. The vectors should be stored as lists or numpy arrays in your dataset.
You may also specify a column for pre-computed nearest neighbors:
embedding-atlas path_to_dataset.parquet --x projection_x --y projection_y --neighbors neighbors
The neighbors column should have values in the following format: {"ids": [id1, id2, ...], "distances": [d1, d2, ...]}.
If this column is specified, you'll be able to see nearest neighbors for a selected point in the tool.
Local Development
Launch Embedding Atlas with a wine reviews dataset with ./start.sh and the MNIST dataset with ./start_image.sh.
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