t5-small-finetuned-cnndm-wikihow

This model is a fine-tuned version of Sevil/t5-small-finetuned-cnndm_3epoch_v2 on the wikihow dataset. It achieves the following results on the evaluation set:

• Loss: 2.2653
• Rouge1: 27.5037
• Rouge2: 10.8442
• Rougel: 23.4674
• Rougelsum: 26.7997
• Gen Len: 18.5558

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: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.8459	0.13	5000	2.5755	25.2929	8.7852	21.2379	24.5649	18.4758
2.7251	0.25	10000	2.5189	25.33	9.0505	21.4892	24.6523	18.4513
2.6696	0.38	15000	2.4805	26.3909	9.6858	22.3589	25.7297	18.4649
2.647	0.51	20000	2.4491	25.9234	9.3936	22.0086	25.2342	18.5558
2.5973	0.64	25000	2.4251	26.4988	9.8197	22.6201	25.8407	18.3438
2.5916	0.76	30000	2.4022	26.3149	9.8432	22.3695	25.6581	18.4506
2.5691	0.89	35000	2.3801	26.4198	9.8848	22.4856	25.7847	18.5381
2.5365	1.02	40000	2.3755	26.5846	10.0287	22.667	25.9606	18.5608
2.4649	1.14	45000	2.3663	26.5925	10.0569	22.6191	25.9247	18.5803
2.4539	1.27	50000	2.3490	26.9735	10.2389	22.9536	26.282	18.5126
2.4578	1.4	55000	2.3374	26.7878	10.2275	22.849	26.1188	18.6162
2.4365	1.53	60000	2.3266	27.1171	10.403	23.0596	26.4284	18.6128
2.428	1.65	65000	2.3209	27.1762	10.578	23.1577	26.5007	18.5246
2.4293	1.78	70000	2.3145	27.0896	10.5146	23.1502	26.4338	18.4604
2.4335	1.91	75000	2.2979	27.3373	10.6273	23.2944	26.6725	18.5403
2.3981	2.03	80000	2.3008	27.1857	10.6455	23.1333	26.5203	18.5412
2.3395	2.16	85000	2.2908	27.3123	10.7063	23.3126	26.626	18.4265
2.3463	2.29	90000	2.2869	27.5328	10.7662	23.4527	26.8613	18.5664
2.3481	2.42	95000	2.2802	27.4799	10.7826	23.4538	26.7912	18.5449
2.3345	2.54	100000	2.2774	27.3182	10.724	23.3276	26.669	18.5908
2.3254	2.67	105000	2.2713	27.3942	10.777	23.3918	26.7036	18.5681
2.3369	2.8	110000	2.2666	27.5976	10.9144	23.5832	26.9147	18.5471
2.3269	2.93	115000	2.2653	27.5037	10.8442	23.4674	26.7997	18.5558

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6