9/9/2023 0 Comments Adapter# Deactivate skipping layers during evaluation (otherwise it would use the t_active_adapters("rotten_tomatoes", skip_layers=skip_layers)ĭef on_evaluate(self, args, state, control, **kwargs): Skip_layers = list(range(np.random.randint(0, 11))) Order to train a model with AdapterDrop, we specify a callback for the Trainer class that sets the skip_layersĪrgument to the layers that should be skipped in each step as follows:Ĭlass AdapterDropTrainerCallback(TrainerCallback):ĭef on_step_begin(self, args, state, control, **kwargs): It specifies for which layers the adapters should be skipped during a forward pass. Check out the full working example in the Colab notebook.ĪdapterDrop allows us to remove adapters on lower layers during training and inference. We ensure this by first activating all adapters in the setup and then calling train_fusion(): adapter_setup = Fuse("multinli", "qqp", "qnli")įrom here on, the training procedure is identical to training a single adapters or a full model. On top of the loaded adapters, we add a new fusion layer using add_fusion().įor this purpose, we first define the adapter setup using the Fuse composition block.ĭuring training, only the weights of the fusion layer will be updated. model = load_as="multinli", with_head=False) Using AdapterFusion, we can combine the knowledge of multiple pre-trained adapters on a downstream task.įirst, we load a pre-trained model and a couple of pre-trained adapters.Īs we discard the prediction heads of the pre-trained adapters, we add a new head afterwards.
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