BART_large_Synthetic_Gameplan

This model is a fine-tuned version of Koshti10/BART-large-ET-Synthetic on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2557
• Rouge1: 38.2255
• Rouge2: 27.7473
• Rougel: 35.4392
• Rougelsum: 35.414
• Gen Len: 19.2554

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 20
• label_smoothing_factor: 0.1

Training results

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3