bart-large-cnn-finetuned-laws_articles

This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2619
• Rouge1: 37.4577
• Rouge2: 17.4395
• Rougel: 27.6576
• Rougelsum: 29.1109

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	1.0	86	1.9613	40.6402	20.5834	31.9641	33.0747
No log	1.99	172	2.0573	39.7679	19.7934	29.9615	31.2392
No log	3.0	259	2.1328	38.2225	18.195	28.8951	30.4138
No log	3.99	345	2.2619	37.4577	17.4395	27.6576	29.1109

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.7
• Tokenizers 0.14.1