summarise_v11

This model is a fine-tuned version of allenai/led-base-16384 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6322
Rouge1 Precision: 0.6059
Rouge1 Recall: 0.6233
Rouge1 Fmeasure: 0.5895
Rouge2 Precision: 0.4192
Rouge2 Recall: 0.4512
Rouge2 Fmeasure: 0.4176
Rougel Precision: 0.4622
Rougel Recall: 0.4946
Rougel Fmeasure: 0.4566
Rougelsum Precision: 0.4622
Rougelsum Recall: 0.4946
Rougelsum Fmeasure: 0.4566

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: 5e-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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1 Precision	Rouge1 Recall	Rouge1 Fmeasure	Rouge2 Precision	Rouge2 Recall	Rouge2 Fmeasure	Rougel Precision	Rougel Recall	Rougel Fmeasure	Rougelsum Precision	Rougelsum Recall	Rougelsum Fmeasure
1.6201	0.45	10	1.4875	0.3203	0.64	0.3932	0.197	0.3839	0.2385	0.1952	0.4051	0.2454	0.1952	0.4051	0.2454
0.9172	0.91	20	1.4404	0.4917	0.5134	0.4699	0.288	0.3095	0.276	0.3371	0.3594	0.3277	0.3371	0.3594	0.3277
1.0923	1.36	30	1.3575	0.519	0.5505	0.4936	0.3114	0.3237	0.2958	0.3569	0.3702	0.3364	0.3569	0.3702	0.3364
1.1287	1.82	40	1.3269	0.4913	0.5997	0.5068	0.3108	0.3964	0.3269	0.3355	0.427	0.3521	0.3355	0.427	0.3521
0.9938	2.27	50	1.3189	0.5339	0.5781	0.4973	0.3555	0.3883	0.3345	0.3914	0.4289	0.3678	0.3914	0.4289	0.3678
0.8659	2.73	60	1.3241	0.525	0.638	0.5165	0.3556	0.4349	0.3535	0.3914	0.4793	0.3886	0.3914	0.4793	0.3886
0.6187	3.18	70	1.3360	0.5875	0.5864	0.5416	0.4005	0.4045	0.3701	0.4485	0.4556	0.414	0.4485	0.4556	0.414
0.3941	3.64	80	1.4176	0.5373	0.6415	0.5328	0.3576	0.446	0.3642	0.3787	0.4586	0.3781	0.3787	0.4586	0.3781
0.4145	4.09	90	1.3936	0.4127	0.6553	0.4568	0.2568	0.4498	0.2988	0.2918	0.4933	0.328	0.2918	0.4933	0.328
0.4203	4.55	100	1.4703	0.6545	0.601	0.5981	0.4789	0.4373	0.438	0.5251	0.4851	0.4818	0.5251	0.4851	0.4818
0.687	5.0	110	1.4304	0.5566	0.6357	0.5637	0.3734	0.4186	0.3748	0.4251	0.4825	0.4286	0.4251	0.4825	0.4286
0.4006	5.45	120	1.5399	0.5994	0.5794	0.5515	0.4215	0.4218	0.398	0.4359	0.4369	0.4084	0.4359	0.4369	0.4084
0.2536	5.91	130	1.5098	0.5074	0.6254	0.4874	0.3369	0.4189	0.3256	0.3802	0.4738	0.3664	0.3802	0.4738	0.3664
0.2218	6.36	140	1.5278	0.5713	0.6059	0.5688	0.3887	0.4233	0.3916	0.4414	0.4795	0.4457	0.4414	0.4795	0.4457
0.2577	6.82	150	1.5469	0.5148	0.5941	0.5175	0.3284	0.3856	0.3335	0.3616	0.4268	0.3681	0.3616	0.4268	0.3681
0.1548	7.27	160	1.5986	0.5983	0.657	0.5862	0.4322	0.4877	0.4287	0.4466	0.5167	0.4482	0.4466	0.5167	0.4482
0.1535	7.73	170	1.5796	0.5609	0.641	0.5616	0.3856	0.4428	0.3892	0.4238	0.4921	0.4263	0.4238	0.4921	0.4263
0.1568	8.18	180	1.6052	0.5669	0.617	0.5679	0.3911	0.4382	0.3969	0.4363	0.4877	0.4417	0.4363	0.4877	0.4417
0.2038	8.64	190	1.6191	0.5466	0.5973	0.5313	0.3543	0.4114	0.3531	0.4061	0.4666	0.404	0.4061	0.4666	0.404
0.1808	9.09	200	1.6165	0.5751	0.5919	0.5587	0.3831	0.4097	0.3817	0.4482	0.4728	0.4405	0.4482	0.4728	0.4405
0.1021	9.55	210	1.6316	0.5316	0.6315	0.535	0.3588	0.4563	0.3697	0.405	0.502	0.4126	0.405	0.502	0.4126
0.1407	10.0	220	1.6322	0.6059	0.6233	0.5895	0.4192	0.4512	0.4176	0.4622	0.4946	0.4566	0.4622	0.4946	0.4566

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 1.2.1
Tokenizers 0.12.1

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summarise_v11

summarise_v11

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Evaluation results