| callbacks: | |
| callbacks: [] | |
| data: | |
| datamodule: | |
| _target_: classy.data.data_modules.ClassyDataModule | |
| task: ${task} | |
| dataset_path: data/aida | |
| train_dataset: | |
| _target_: classy.data.dataset.hf.classification.HFQADataset.from_file | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| min_length: 5 | |
| max_length: 500 | |
| tokens_per_batch: 2000 | |
| max_batch_size: 10 | |
| section_size: 10000 | |
| prebatch: true | |
| materialize: false | |
| for_inference: false | |
| validation_dataset: | |
| _target_: classy.data.dataset.hf.classification.HFQADataset.from_file | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| min_length: 5 | |
| max_length: 500 | |
| tokens_per_batch: 2000 | |
| max_batch_size: 10 | |
| section_size: 10000 | |
| prebatch: true | |
| materialize: true | |
| for_inference: true | |
| validation_split_size: 0.1 | |
| test_split_size: 0.1 | |
| max_nontrain_split_size: 10000 | |
| shuffle_dataset: true | |
| device: | |
| gpus: | |
| - 0 | |
| precision: 32 | |
| amp_level: O0 | |
| model: | |
| _target_: classy.pl_modules.hf.classification.HFQAPLModule | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: [] | |
| optim_conf: | |
| _target_: classy.optim.factories.RAdamFactory | |
| lr: 1.0e-05 | |
| weight_decay: 0.01 | |
| no_decay_params: | |
| - bias | |
| - LayerNorm.weight | |
| prediction: | |
| dataset: | |
| _target_: classy.data.dataset.hf.classification.HFQADataset.from_samples | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| min_length: -1 | |
| max_length: -1 | |
| tokens_per_batch: 800 | |
| max_batch_size: -1 | |
| section_size: 10000 | |
| prebatch: true | |
| materialize: false | |
| for_inference: true | |
| training: | |
| seed: 12 | |
| pl_trainer: | |
| _target_: pytorch_lightning.Trainer | |
| accumulate_grad_batches: 4 | |
| gradient_clip_val: 10.0 | |
| val_check_interval: 1.0 | |
| max_steps: 1000000 | |
| early_stopping_callback: | |
| _target_: pytorch_lightning.callbacks.EarlyStopping | |
| monitor: ${callbacks_monitor} | |
| mode: ${callbacks_mode} | |
| patience: 25 | |
| model_checkpoint_callback: | |
| _target_: classy.pl_callbacks.best_checkpoint.ModelCheckpointWithBest | |
| monitor: ${callbacks_monitor} | |
| mode: ${callbacks_mode} | |
| verbose: true | |
| save_top_k: 3 | |
| dirpath: checkpoints | |
| save_last: true | |
| resume_from: null | |
| logging: | |
| wandb: | |
| use_wandb: true | |
| project_name: esc-ed | |
| experiment_name: aida-longformer-large-*sep-gam-cand-shuffle | |
| anonymous: null | |
| run_id: null | |
| task: qa | |
| project_name: classy | |
| exp_name: esc-aida-longformer-large-gam-cand-shuffle | |
| exp_folder: ./experiments/${exp_name} | |
| transformer_model: bert-base-cased | |
| callbacks_monitor: val_accuracy | |
| callbacks_mode: max | |
| profiles: | |
| supported_tasks: | |
| - qa | |
| - sentence-pair | |
| - sequence | |
| - token | |
| - generation | |
| transformer_model: allenai/longformer-large-4096 | |
| candidates_separator: '*' | |
| training: | |
| pl_trainer: | |
| accumulate_grad_batches: 8 | |
| val_check_interval: 2048 | |
| max_steps: 100000 | |
| model: | |
| _target_: src.esc_ed_module.ESCModule | |
| additional_special_tokens: [] | |
| transformer_model: ${transformer_model} | |
| attention_window: 64 | |
| modify_global_attention: true | |
| optim_conf: | |
| _target_: classy.optim.factories.RAdamFactory | |
| lr: 1.0e-05 | |
| weight_decay: 0.01 | |
| no_decay_params: | |
| - bias | |
| - LayerNorm.weight | |
| data: | |
| datamodule: | |
| train_dataset: | |
| _target_: src.data.esc_ed_dataset.ESCEDDataset.from_file | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| candidates_separator: ${candidates_separator} | |
| shuffle_candidates_prob: 0.0 | |
| min_length: 0 | |
| max_length: 1024 | |
| tokens_per_batch: 1024 | |
| max_batch_size: 10 | |
| section_size: 20000 | |
| prebatch: true | |
| materialize: false | |
| for_inference: false | |
| validation_dataset: | |
| _target_: src.data.esc_ed_dataset.ESCEDDataset.from_file | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| candidates_separator: ${candidates_separator} | |
| min_length: 0 | |
| max_length: 1024 | |
| tokens_per_batch: 2048 | |
| max_batch_size: 10 | |
| section_size: 10000 | |
| prebatch: true | |
| materialize: true | |
| for_inference: false | |
| shuffle_dataset: true | |
| prediction: | |
| dataset: | |
| _target_: src.data.esc_ed_dataset.ESCEDDataset.from_samples | |
| transformer_model: ${transformer_model} | |
| additional_special_tokens: ${model.additional_special_tokens} | |
| candidates_separator: ${candidates_separator} | |
| min_length: -1 | |
| max_length: -1 | |
| tokens_per_batch: 2048 | |
| max_batch_size: -1 | |
| section_size: 10000 | |
| prebatch: true | |
| materialize: false | |
| for_inference: true | |