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  A dataset consisting of Pokémon embeddings: text embeddings encoded from large amount of Pokémon metadata using [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5), and image embeddings encoded from the official Pokémon artwork using [nomic-ai/nomic-embed-vision-v1.5](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5).
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- No other metadata is present to avoid invoking the Nintendo Ninjas. To see how to retrieve additional metadata, see the "Get More Metadata" section.
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  ## Fields
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- - `id`: Pokémon National Dex ID, which mapes to PokeAPI. "Special" Pokemon will have an id > 10000.
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- - `text_embedding`: 768D embedding of Pokémon medatadata
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  - `image_embedding`: 768D embedding of Pokémon images.
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  - `umap_2d_x`: x-coordinate value of a UMAP projection to 2D by concatenating the embeddings.
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  - `umap_2d_y`: y-coordinate value of a UMAP projection to 2D by concatenating the embeddings.
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  ## Get More Metadata
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- You can retrieve more metadata using PokeAPI. An example:
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- ```py3
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  import requests
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  graphql_query = """
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  pokemon = r.json()["data"]["pokemon_v2_pokemon"]
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  pokemon[0:10]
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- ```
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  A dataset consisting of Pokémon embeddings: text embeddings encoded from large amount of Pokémon metadata using [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5), and image embeddings encoded from the official Pokémon artwork using [nomic-ai/nomic-embed-vision-v1.5](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5).
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+ No other metadata besides the Pokémon ID is present to avoid invoking the Nintendo Ninjas. To see how to retrieve additional metadata, see the "Get More Metadata" section.
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  ## Fields
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+ - `id`: Pokémon National Dex ID, which maps to PokeAPI. "Special" Pokemon will have an id > 10000.
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+ - `text_embedding`: 768D embedding of Pokémon metadata.
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  - `image_embedding`: 768D embedding of Pokémon images.
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  - `umap_2d_x`: x-coordinate value of a UMAP projection to 2D by concatenating the embeddings.
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  - `umap_2d_y`: y-coordinate value of a UMAP projection to 2D by concatenating the embeddings.
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  ## Get More Metadata
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+ You can retrieve more metadata using PokeAPI. An example to get the id and name of all Pokémon:
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+ ```python
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  import requests
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  graphql_query = """
 
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  pokemon = r.json()["data"]["pokemon_v2_pokemon"]
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  pokemon[0:10]
 
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+ # [{'id': 1, 'name': 'bulbasaur'},
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+ # {'id': 2, 'name': 'ivysaur'},
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+ # {'id': 3, 'name': 'venusaur'},
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+ # ...
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+ ```