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Loading a Model

To load a pre-trained model, you can use the Model2Vec.from_pretrained method. This method allows you to load models from the Hugging Face Hub or from a local path. 
from model2vec import Model2Vec

model = Model2Vec.from_pretrained("minishlab/potion-base-8M")
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path
PathLike
The path to load your static model from.
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token
str | None
default:"None"
The Hugging Face token to use when loading a private model. If not provided, no token is used.
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normalize
bool | None
default:"None"
Whether to normalize the embeddings after loading. If True, embeddings will be normalized; if False, they won’t. If None, the model’s default behavior applies.
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subfolder
str | None
default:"None"
The subfolder within the repository or local directory to load the model from. Leave as None to load from the root.
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quantize_to
str | DType | None
default:"None"
The data type to quantize the model to (e.g., "float16" or a torch.dtype). If a string is passed, it’s converted to the corresponding DType. Set to None for no quantization.
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dimensionality
int | None
default:"None"
The dimensionality to load the model at. If None, uses the model’s inherent dimensionality. Useful when loading a model with reduced dimensions (e.g., trained via PCA or MRL).

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Creating Mean Embeddings

To create mean embeddings, you can use the encode method of the Model2Vec class. This method allows you to encode a list of sentences into mean embeddings. 
from model2vec import Model2Vec

model = Model2Vec.from_pretrained("minishlab/potion-base-8M")
embeddings = model.encode(["Hello world", "Static embeddings are great!"])
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sentences
Sequence[str]
The list of sentences to encode. You can also pass a single sentence.
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show_progress_bar
bool
default:"False"
Whether to show the progress bar.
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max_length
int | None
default:"512"
The maximum length of the sentences. Any tokens beyond this length will be truncated. If this is None, no truncation is done.
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batch_size
int
default:"1024"
The batch size to use.
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use_multiprocessing
bool
default:"True"
Whether to use multiprocessing. By default, this is enabled for inputs > multiprocessing_threshold sentences and disabled otherwise.
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multiprocessing_threshold
int
default:"10000"
The threshold in number of sentences for using multiprocessing.
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kwargs
Any
Any additional arguments. These are ignored.

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Creating Sequence Embeddings

To create sequence embeddings, you can use the encode_as_sequence method of the Model2Vec class. This method allows you to encode a list of sentences into sequence embeddings, which are useful for tasks where you need a single embedding per token. 
from model2vec import Model2Vec
model = Model2Vec.from_pretrained("minishlab/potion-base-8M")
embeddings = model.encode_as_sequence(["Hello world", "Static embeddings are great!"], mode="sequence")
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sentences
Sequence[str]
The list of sentences to encode. You can also pass a single sentence.
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show_progress_bar
bool
default:"False"
Whether to show the progress bar.
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max_length
int | None
default:"512"
The maximum length of the sentences. Any tokens beyond this length will be truncated. If this is None, no truncation is done.
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batch_size
int
default:"1024"
The batch size to use.
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use_multiprocessing
bool
default:"True"
Whether to use multiprocessing. By default, this is enabled for inputs > multiprocessing_threshold sentences and disabled otherwise.
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multiprocessing_threshold
int
default:"10000"
The threshold in number of sentences for using multiprocessing.
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kwargs
Any
Any additional arguments. These are ignored.