descope.arguments
- class DeSCOPEDataArguments(tokenized_datasets_dir: str = './tokenized_dataset/K562', keep_in_memory: bool = False, ctrl_name: str = 'control', gene_embs_file: str = './ESM2_pert_features.pt')[source]
Bases:
object- ctrl_name: str = 'control'
- gene_embs_file: str = './ESM2_pert_features.pt'
- keep_in_memory: bool = False
- tokenized_datasets_dir: str = './tokenized_dataset/K562'
- class DeSCOPEModelArguments(hidden_act: str = 'gelu', hidden_size: int = 672, hidden_dropout: float = 0, pert_gene_encoder_layers: int = 1, variational_encoder_layers: int = 4, variational_decoder_layers: int = 4, add_layernorm: bool = True)[source]
Bases:
object- add_layernorm: bool = True
- pert_gene_encoder_layers: int = 1
- variational_decoder_layers: int = 4
- variational_encoder_layers: int = 4
- class DeSCOPETrainingArguments(seed: int = 42, output_dir: str | None = None, num_train_epochs: int = 3, max_train_steps: int | None = None, logging_steps: int = 500, eval_every_n_epochs: int = 1, earlystop_patience: int | None = None, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, gradient_accumulation_steps: int = 1, max_grad_norm: float = 1.0, learning_rate: float = 5e-05, lr_scheduler_type: transformers.trainer_utils.SchedulerType | str = 'linear', weight_decay: float = 0.0, num_warmup_steps: int = 0, num_warmup_ratio: float | None = None, mixed_precision: str = 'bf16', with_tracking: bool = True, report_to: str = 'all', checkpointing_steps: int | str | NoneType = None, resume_from_checkpoint: str | None = None, dataloader_pin_memory: bool = True, dataloader_persistent_workers: bool = False, dataloader_num_workers: int = 0, dataloader_prefetch_factor: int | None = None, deepspeed: str | None = None, alpha_mse_loss: float = 1.0, alpha_kl_loss: float = 1.0, pretrained_model_name_or_path: str | None = None)[source]
Bases:
TrainingArguments- alpha_kl_loss: float = 1.0
- alpha_mse_loss: float = 1.0
- pretrained_model_name_or_path: str | None = None