bart-abs-1509-0313-lr-0.0003-bs-4-maxep-10

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: 4.7482
• Rouge/rouge1: 0.4015
• Rouge/rouge2: 0.1493
• Rouge/rougel: 0.329
• Rouge/rougelsum: 0.3294
• Bertscore/bertscore-precision: 0.8894
• Bertscore/bertscore-recall: 0.8807
• Bertscore/bertscore-f1: 0.8849
• Meteor: 0.3397
• Gen Len: 33.2

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: 10
• 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
1.722	1.0	217	2.6756	0.4274	0.1866	0.3588	0.3592	0.8936	0.884	0.8886	0.3527	32.9545
1.7652	2.0	434	2.7321	0.416	0.1726	0.3511	0.3521	0.8944	0.8818	0.888	0.3352	31.5909
1.135	3.0	651	2.9372	0.3752	0.1441	0.3163	0.3158	0.8968	0.8736	0.8849	0.2976	26.4
0.9762	4.0	868	3.1311	0.3959	0.1535	0.3344	0.3353	0.8893	0.8777	0.8833	0.3296	33.1273
0.7207	5.0	1085	3.3741	0.4028	0.1562	0.3388	0.3389	0.8889	0.8818	0.8852	0.3324	34.3273
0.3986	6.0	1302	3.4504	0.4245	0.1689	0.3493	0.3501	0.892	0.8834	0.8876	0.351	34.4727
0.2471	7.0	1519	3.8316	0.4096	0.1536	0.3384	0.3389	0.8922	0.8814	0.8867	0.3376	32.7909
0.1613	8.0	1736	4.2439	0.4201	0.1621	0.346	0.347	0.8921	0.8815	0.8866	0.3503	33.3
0.0989	9.0	1953	4.4784	0.4115	0.1499	0.3394	0.3408	0.8904	0.8825	0.8863	0.3409	34.0
0.0644	10.0	2170	4.7482	0.4015	0.1493	0.329	0.3294	0.8894	0.8807	0.8849	0.3397	33.2

Framework versions

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