Tasks

Tasks define data and model usage. They provide a simple interface for swapping backbones, adapters, and data without any code changes, enabling rapid and reproducible experimentation. They are specified with the --model argument in the CLI or in the model section of a configuration file.

Tasks automatically configure backbones and adapters for training with mgen fit, evaluation with mgen test/validate, and inference with mgen predict. They cover a range of use-cases for information extraction, domain adaptation, supervised prediction, generative modeling, and zero-shot applications.

This reference overviews the available no-code tasks for finetuning and inference. If you would like to develop new tasks, see Experiment Design.

# Example Task Configuration
model:
  class_path: SequenceClassification
  init_args:
    backbone:
      class_path: aido_dna_7b
      init_args:
        use_peft: true
        lora_r: 16
        lora_alpha: 32
        lora_dropout: 0.1  
    adapter:
      class_path: modelgenerator.adapters.MLPPoolAdapter
      init_args:
        pooling: mean_pooling
        hidden_sizes: 
        - 512
        - 256
        bias: true
        dropout: 0.1
        dropout_in_middle: false
    optimizer:
      class_path: torch.optim.AdamW
      init_args:
        lr: 1e-4
    lr_scheduler:
      class_path: torch.optim.lr_scheduler.StepLR
      init_args:
        step_size: 1
        gamma: 0.1
data:
  ...
trainer:
  ...

Note: Adapters and Backbones are typed as Callables, since some args are reserved to be automatically configured within the task. As a general rule, positional arguments are reserved while keyword arguments are free to use. For example, the backbone, adapter, optimizer, and lr_scheduler can be configured as

Extract

modelgenerator.tasks.Embed

Bases: TaskInterface

Task for getting embeddings from a pretrained backbone. This task is used only for inference.

Note

Must be used with PredictionWriter. Embeddings are stored under "predictions".

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
**kwargs		Additional keyword arguments for the parent class. use_legacy_adapter=False is always overridden.	{}

modelgenerator.tasks.Inference

Bases: MLM

Task for performing inference with a pretrained backbone end-to-end, including the backbone's original adapter.

Note

Must be used with PredictionWriter. Model outputs are stored under "predictions".

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
use_legacy_adapter	bool	Whether to use the adapter from the backbone (HF head support). Warning: This is not supported for all tasks and will be depreciated in the future.	True
**kwargs		Additional arguments passed to the parent class.	{}

Adapt

modelgenerator.tasks.MLM

Bases: TaskInterface

Task for performing masked language modeling (MLM) with a pretrained backbone. Can be used to train from scratch or for domain adaptation. Uses the MLMDataModule. Evaluates in terms of reconstruction accuracy on all tokens and cross-entropy loss.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
use_legacy_adapter	bool	Whether to use the adapter from the backbone (HF head support). Warning: This is not supported for all tasks and will be depreciated in the future.	True
**kwargs		Additional arguments passed to the parent class.	{}

modelgenerator.tasks.ConditionalMLM

Bases: TaskInterface

Task for masked language modeling with extra condition inputs. Evaluates in terms of reconstruction accuracy on all tokens and cross-entropy loss.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int, int, Module], ConditionalGenerationAdapter]]	A ConditionalGenerationAdapter for the model.	ConditionalLMAdapter
use_legacy_adapter	bool	Whether to use the pre-trained legacy adapter within the conditional decoder.	True
condition_dim	int	The dimension of the condition.	1
**kwargs		Additional arguments passed to the parent class.	{}

Predict

modelgenerator.tasks.SequenceClassification

Bases: TaskInterface

Task for fine-tuning a sequence model for classification. Evaluates in terms of accuracy, F1 score, Matthews correlation coefficient (MCC), and AUROC.

Note

Supports binary, multiclass, and binary multi-label classification tasks.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int], SequenceAdapter]]	A SequenceAdapter for the model.	LinearCLSAdapter
n_classes	int	The number of classes in the classification task.	2
multilabel	bool	Indicate whether multiple labels can be positive, turning this into a multi-way binary classification task.	False
**kwargs		Additional arguments passed to the parent class.	{}

modelgenerator.tasks.TokenClassification

Bases: SequenceClassification

Task for fine-tuning a model for token-wise classification. Evaluates in terms of accuracy, F1 score, Matthews correlation coefficient (MCC), and AUROC.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int], TokenAdapter]]	A TokenAdapter for the model.	LinearAdapter
n_classes	int	The number of classes in the classification task.	2
**kwargs		Additional keyword arguments for the parent class. multilabel=False is always overridden.	{}

Attributes:

Name	Type	Description
legacy_adapter_type	LegacyAdapterType	The LegacyAdapterType.TOKEN_CLS legacy adapter.

modelgenerator.tasks.PairwiseTokenClassification

Bases: SequenceClassification

Task for fine-tuning a model for pairwise token classification. Evaluates in terms of accuracy, F1 score, Matthews correlation coefficient (MCC), AUROC, and top-k accuracy for k=2,5,10.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int], TokenAdapter]]	A TokenAdapter for the model.	LinearAdapter
adapter_dim_multiplier	int	The multiplier for the adapter dimension. Defaults to 2.	2
**kwargs		Additional keyword arguments for the parent class. n_classes=2 and multilabel=False are always overridden.	{}

Attributes:

Name	Type	Description
legacy_adapter_type	LegacyAdapterType	The LegacyAdapterType.TOKEN_CLS legacy adapter.

modelgenerator.tasks.MMSequenceRegression

Bases: TaskInterface

Task for fine-tuning multiple models on single-/multi-task regression. Evaluates in terms of mean absolute error, mean squared error, R2 score, Pearson correlation, and Spearman correlation.

Note

Supports any combination of DNA, RNA and protein backbones

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
backbone1	BackboneCallable	A second pretrained backbone from the modelgenerator library.	required
backbone2	Optional[BackboneCallable]	An optional third pretrained backbone from the modelgenerator library.	None
backbone_order	list	A list of data columns in order of the backbones. Defaults to ["dna_seq", "rna_seq", "protein_seq"].	['dna_seq', 'rna_seq']
adapter	Optional[Callable[[int, int, int, int], FusionAdapter]]	A callable that returns a FusionAdapter.	MMFusionTokenAdapter
num_outputs	int	The number of outputs for the regression task.	1
loss_func	Callable[..., Module]	A callable that returns a loss function.	MSELoss
**kwargs		Additional arguments passed to the parent class.	{}

Generate

modelgenerator.tasks.Diffusion

Bases: TaskInterface

Masked Diffusion Language Modeling training and generation on sequences. Evaluates in terms of reconstruction accuracy on masked tokens and cross-entropy loss.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int], TokenAdapter]]	A TokenAdapter for the model.	None
use_legacy_adapter	bool	Whether to use the adapter from the backbone (HF head support). Warning: This is not supported for all tasks and will be depreciated in the future.	True
sample_seq	bool	Whether to sample tokens during denoising, instead of always using the most likely token.	False
num_denoise_steps	int	Granularity of the denoising process. Less steps makes fewer forward passes and denoises more aggressively.	4
sampling_temperature	float	The temperature for sampling tokens, if sample_seq is True.	1.0
normalize_posterior_weights	bool	Whether to normalize posterior weights. Experimental feature to help training stability.	False
verbose	bool	Print while denoising (warning: fun to watch).	False
**kwargs		Additional arguments passed to the parent class.	{}

modelgenerator.tasks.ConditionalDiffusion

Bases: Diffusion

Task for masked diffusion language modeling with extra condition inputs. Evaluates in terms of reconstruction accuracy on masked tokens and cross-entropy loss.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
adapter	Optional[Callable[[int, int, int, Module], ConditionalGenerationAdapter]]	A ConditionalGenerationAdapter for the model.	ConditionalLMAdapter
use_legacy_adapter	bool	Whether to use the pre-trained legacy adapter within the conditional decoder.	True
condition_dim	int	The dimension of the condition.	1
**kwargs		Additional arguments passed to the parent class.	{}

Zero-Shot

modelgenerator.tasks.ZeroshotPredictionDiff

Bases: TaskInterface

Task for zero-shot prediction with a languange model that produces token logits. Computes the log-likelihood difference between probability of ref and alt at the mutated position. Evaluates in terms of AUROC and AUPRC.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
**kwargs		Additional keyword arguments for the parent class. use_legacy_adapter=True is always overridden.	{}

modelgenerator.tasks.ZeroshotPredictionDistance

Bases: TaskInterface

Task for zero-shot prediction with a model that produces embeddings. Computes the L1 and L2 distance between the reference and mutated sequence embeddings. Evaluates in terms of AUROC and AUPRC of the embedding distance.

Parameters:

Name	Type	Description	Default
backbone	BackboneCallable	A pretrained backbone from the modelgenerator library.	required
all_hidden_states	bool	Whether to run the test on all available hidden layers. Defaults to False, only using the last layer.	False
shared_ref	bool	Whether to use a shared reference sequence to accelerate zero-shot computation. Uses a separate reference sequence for each mutated sequence by default.	False
**kwargs		Additional keyword arguments for the parent class. use_legacy_adapter=True is always overridden.	{}