mbart_cnn_summarization_model_10K_8ep_1e-5_batch_8_BEST_warmup

• Loss: 2.9964
• Rouge1: 42.6547
• Rouge2: 20.2259
• Rougel: 29.721
• Rougelsum: 29.7093
• Gen Len: 84.622

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.05
• num_epochs: 8
• mixed_precision_training: Native AMP
• label_smoothing_factor: 0.1

Training results

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.6.0
• Tokenizers 0.22.2