bart-large-aeslc-rouge-3-loss-differentiable-100-cnt-supervised

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 5.9278
• Rouge1: 0.31
• Rouge2: 0.1577
• Rougel: 0.3039
• Rougelsum: 0.3036

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: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• 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_ratio: 0.06
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
4.6006	1.56	20	5.0770	0.2559	0.1268	0.253	0.2529
2.9074	3.08	40	5.1504	0.2786	0.1408	0.2753	0.2752
2.2547	4.64	60	5.2866	0.2945	0.1433	0.289	0.2893
3.2227	6.16	80	5.9278	0.31	0.1577	0.3039	0.3036
1.7171	7.72	100	5.9767	0.3046	0.1471	0.2977	0.2978
1.5884	9.24	120	6.1892	0.2943	0.1416	0.2893	0.289
1.3678	10.8	140	6.4147	0.2908	0.1432	0.2844	0.2845
1.181	12.32	160	6.4444	0.2971	0.1476	0.2894	0.2892
1.0218	13.88	180	6.7040	0.2911	0.1423	0.2842	0.2835

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 3.6.0
• Tokenizers 0.22.1