nip.scenario_base.pretrained_models.PretrainedModel#
- class nip.scenario_base.pretrained_models.PretrainedModel(hyper_params: HyperParameters, settings: ExperimentSettings)[source]#
Base class for pretrained models, used to generate embeddings for datasets.
Pretrained models can by used to generate embeddings for datasets, which can then be used by agents instead of raw data. This means the agent starts with a good level of knowledge about the data.
- Parameters:
hyper_params (HyperParameters) – The parameters for the experiment
settings (ExperimentSettings) – The settings for the experiment
attributes (Class)
----------------
name (str) – The name of the model, which should uniquely identify it
dataset (str) – The name of the dataset the model was trained for
allow_other_datasets (bool, default=False) – Whether the model can be used for datasets other than the one it was trained on
Methods Summary
__init__
(hyper_params, settings)generate_dataset_embeddings
(datasets[, ...])Load the model and generate embeddings for the datasets.
Attributes
allow_other_datasets
name
dataset
Methods
- __init__(hyper_params: HyperParameters, settings: ExperimentSettings)[source]#
- abstract generate_dataset_embeddings(datasets: Iterable[TensorDictDataset], delete_model: bool = True) Tensor [source]#
Load the model and generate embeddings for the datasets.
- Parameters:
datasets (Iterable[TensorDictDataset]) – The datasets to generate embeddings for
delete_model (bool, default=True) – Whether to delete the model after generating embeddings
- Returns:
embeddings (torch.Tensor) – The embeddings for the datasets