bart-abs-1509-0313-lr-0.0003-bs-4-maxep-6

This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.8541
• Rouge/rouge1: 0.4315
• Rouge/rouge2: 0.1861
• Rouge/rougel: 0.3638
• Rouge/rougelsum: 0.3654
• Bertscore/bertscore-precision: 0.8936
• Bertscore/bertscore-recall: 0.8875
• Bertscore/bertscore-f1: 0.8904
• Meteor: 0.3814
• Gen Len: 35.4273

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: 0.0003
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge/rouge1	Rouge/rouge2	Rouge/rougel	Rouge/rougelsum	Bertscore/bertscore-precision	Bertscore/bertscore-recall	Bertscore/bertscore-f1	Meteor	Gen Len
2.1071	1.0	217	2.5496	0.4238	0.1753	0.3521	0.3541	0.8943	0.8836	0.8888	0.3359	32.1
1.5392	2.0	434	2.5867	0.4296	0.1807	0.3556	0.3551	0.8921	0.886	0.8889	0.3598	35.0909
1.0328	3.0	651	2.6952	0.4096	0.1667	0.3444	0.3453	0.8919	0.8826	0.8871	0.3519	33.8818
0.62	4.0	868	2.9126	0.4104	0.16	0.3478	0.3487	0.8904	0.8815	0.8858	0.3524	33.4273
0.3251	5.0	1085	3.3250	0.43	0.1771	0.3591	0.3598	0.8935	0.8861	0.8896	0.3744	34.9636
0.1503	6.0	1302	3.8541	0.4315	0.1861	0.3638	0.3654	0.8936	0.8875	0.8904	0.3814	35.4273

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1