distilbart-xsum-6-6-finetuned-bbc-news

This model is a fine-tuned version of sshleifer/distilbart-xsum-6-6 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2624
• Rouge1: 62.1927
• Rouge2: 54.4754
• Rougel: 55.868
• Rougelsum: 60.9345

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: 5.6e-05
• 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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
0.4213	1.0	445	0.2005	59.4886	51.7791	53.5126	58.3405
0.1355	2.0	890	0.1887	61.7474	54.2823	55.7324	60.5787
0.0891	3.0	1335	0.1932	61.1312	53.103	54.6992	59.8923
0.0571	4.0	1780	0.2141	60.8797	52.6195	54.4402	59.5298
0.0375	5.0	2225	0.2318	61.7875	53.8753	55.5068	60.5448
0.0251	6.0	2670	0.2484	62.3535	54.6029	56.2804	61.031
0.0175	7.0	3115	0.2542	61.6351	53.8248	55.6399	60.3765
0.0133	8.0	3560	0.2624	62.1927	54.4754	55.868	60.9345

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1