quaterion_models.encoders.extras.fasttext_encoder module

class FasttextEncoder(model_path: str, on_disk: bool, aggregations: Optional[List[str]] = None)[source]

Bases: Encoder

Creates a fasttext encoder, which generates vector for a list of tokens based in given fasttext model

Parameters:

• model_path – Path to model to load

• on_disk – If True - use mmap to keep embeddings out of RAM

• aggregations – What types of aggregations to use to combine multiple vectors into one. If multiple aggregations are specified - concatenation of all of them will be used as a result.

classmethod aggregate(embeddings: Tensor, operation: str) → Tensor[source]

Apply aggregation operation to embeddings along the first dimension

Parameters:

• embeddings – embeddings to aggregate

• operation – one of aggregation_options

Returns:

Tensor – aggregated embeddings

forward(batch: List[List[str]]) → Tensor[source]

Infer encoder - convert input batch to embeddings

Parameters:

batch – processed batch

Returns:

embeddings – shape: (batch_size, embedding_size)

get_collate_fn() → CollateFnType[source]

Provides function that converts raw data batch into suitable model input

Returns:

CollateFnType – model’s collate function

classmethod get_tokens(batch: List[Any]) → List[List[str]][source]

classmethod load(input_path: str) → Encoder[source]

Instantiate encoder from saved state.

If no state required - just call create instead

Parameters:

input_path – path to load from

Returns:

Encoder – loaded encoder

save(output_path: str)[source]

Persist current state to the provided directory

Parameters:

output_path – path to save model

aggregation_options = ['min', 'max', 'avg']

property embedding_size: int

Size of resulting embedding

property trainable: bool

Defines if encoder is trainable.

This flag affects caching and checkpoint saving of the encoder.

training: bool

load_fasttext_model(path: str) → Union[FastText, KeyedVectors][source]

Load fasttext model in a universal way

Try to find possible way of loading FastText model and load it

Parameters:

path – path to FastText model or vectors

Returns:

FastText or KeyedVectors – loaded model