distilbart-xsum-6-6-finetuned-bbc-news-on-abstractive

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: 1.6549
• Rouge1: 38.9186
• Rouge2: 30.2223
• Rougel: 32.6201
• Rougelsum: 37.7502

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.3838	1.0	445	1.4841	39.1621	30.4379	32.6613	37.9963
1.0077	2.0	890	1.5173	39.388	30.9125	33.099	38.2442
0.7983	3.0	1335	1.5726	38.7913	30.0766	32.6092	37.5953
0.6681	4.0	1780	1.6549	38.9186	30.2223	32.6201	37.7502

Framework versions

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