HH c]}盇A brain.Event:2R. ,tensorboard.summary.writer.event_file_writer)ׂ`/# }ō盇A*  hp_metric5%*5 a盇A* _hparams_/experimentJ hparams  " hidden_dim " transformer_dim " transformer_dim_scale " initial_embedding_dim " position_embedding_dim " num_embedding_layers " num_encoder_layers " num_branch_embedding_layers " num_branch_encoder_layers " num_jet_embedding_layers " num_jet_encoder_layers " num_detector_layers " num_regression_layers " num_classification_layers " split_symmetric_attention " num_attention_heads " transformer_activation " skip_connections "& "initial_embedding_skip_connections " linear_block_type " transformer_type " linear_activation " normalization " masking " linear_prelu_activation " use_pairwise_interactions " num_pairwise_features " pairwise_embedding_dim " pairwise_input_source " use_moe " num_experts " num_experts_per_tok " moe_loss_scale " event_info_file " training_file " validation_file " testing_file " normalize_features " limit_to_num_jets " balance_particles " balance_jets " balance_classifications " partial_events " dataset_limit " dataset_randomization " train_validation_split " limit_val_batches " batch_size " num_dataloader_workers " mask_sequence_vectors " combine_pair_loss " optimizer " learning_rate " focal_gamma " combinatorial_scale " learning_rate_warmup_epochs " learning_rate_cycles " assignment_loss_scale " detection_loss_scale " kl_loss_scale " regression_loss_scale " classification_loss_scale " use_mass_classification " balance_losses " l2_penalty " gradient_clip " dropout " epochs " num_gpu " verbose_output " usable_gpus " trial_time " trial_output_dir *  hp_metric3z07 : 盇A*  _hparams_/session_start_infoJ  hparams & num_classification_layers @  l2_penalty -C6*? % num_jet_embedding_layers  # trial_output_dir ./test_output # num_dataloader_workers  @  transformer_typeGated num_detector_layers @ " transformer_dim_scale @  masking Filling  validation_file combinatorial_scale   balance_losses ? " initial_embedding_dim (@ num_attention_heads @  partial_events ?  normalization  LayerNorm " regression_loss_scale   normalize_features ? transformer_activationgelu  limit_to_num_jets  $ balance_classifications   moe_loss_scale {Gz? ! learning_rate_cycles ?  pairwise_input_source  optimizerAdamW  linear_block_typeGRU  combine_pair_lossmin " mask_sequence_vectors ? # num_jet_encoder_layers @ & classification_loss_scale  " num_pairwise_features @ " num_regression_layers @  verbose_output  $ linear_prelu_activation ? " dataset_randomization  H training_file75/data/spatop/tpm70_wpm30_FB350_training/all_merged.h5 # position_embedding_dim (@ ! num_embedding_layers $@  num_encoder_layers @ ( learning_rate_warmup_epochs ?  batch_size @ $ use_mass_classification  " assignment_loss_scale ?  limit_val_batches ?  dropout ? ( num_branch_embedding_layers @  balance_jets   epochs @@  dataset_limit ? & use_pairwise_interactions   num_experts  @  balance_particles   learning_rate -C6:? num_experts_per_tok @  hidden_dim 6@  focal_gamma  # pairwise_embedding_dim  @  gradient_clip $@ & split_symmetric_attention ? J event_info_file75/data/spatop/event_files/v11/tt_hadronic_v7_full.yaml  linear_activationgelu # train_validation_split ffffff?  kl_loss_scale   trial_time  num_gpu ?  usable_gpus  skip_connections ? / "initial_embedding_skip_connections ?  transformer_dim 6@ & num_branch_encoder_layers @  testing_file ! detection_loss_scale ?  use_moe =P<ȷR 盇A*1 / _hparams_/session_end_infoJ  hparams"