relevanceai.operations_new.processing.feature_extractor#
Module Contents#
- class relevanceai.operations_new.processing.feature_extractor.FeatureExtractor(model: torch.nn.Module, layer_name: Optional[str] = None)#
- save_outputs_hook(self, layer_name)#
It returns a function that takes 3 arguments (inputs, outputs, and a layer name) and assigns the output to the layer name in the _features dictionary
- Parameters
layer_name – The name of the layer to save the output of.
- Return type
A function that takes 3 arguments.
- forward(self, x: Union[torch.Tensor, List[Any]]) List[Any]#
The function takes in an input tensor x and passes it through the model. The output of the model is not used, but the output of the model’s features is returned
- Parameters
x – input tensor
- Return type
The features of the model
- class relevanceai.operations_new.processing.feature_extractor.EmbeddingsExtractor(model: torch.nn.Module)#
- forward(self, tokens: Union[torch.Tensor, List[int]], embedding_layer: Optional[str] = None) List[List[float]]#
It takes a list of tokens, and returns a list of embeddings for those tokens
- Parameters
tokens (List[int]) – List[int]
embedding_layer (Optional[str]) – The name of the embedding layer to use.
- Return type
A list of lists of floats.