t5-small-finetuned-xsum-ashish-5000

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.6200
• Rouge1: 14.8258
• Rouge2: 4.7741
• Rougel: 11.3583
• Rougelsum: 13.2147
• Gen Len: 19.0

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 40
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	313	2.8460	13.7208	4.2759	10.4447	12.1604	19.0
2.8939	2.0	626	2.7686	14.0884	4.4571	10.8946	12.6399	19.0
2.8939	3.0	939	2.7323	14.249	4.4839	10.9701	12.7336	19.0
2.6857	4.0	1252	2.7140	14.4123	4.5447	11.09	12.8468	19.0
2.6353	5.0	1565	2.6962	14.4931	4.6524	11.1552	12.9235	19.0
2.6353	6.0	1878	2.6827	14.6765	4.6571	11.2099	13.0457	19.0
2.6005	7.0	2191	2.6743	14.6923	4.6506	11.1972	13.0305	19.0
2.5721	8.0	2504	2.6691	14.8242	4.7211	11.2794	13.1706	19.0
2.5721	9.0	2817	2.6598	14.9018	4.7961	11.3472	13.2632	19.0
2.5526	10.0	3130	2.6559	14.8855	4.8159	11.3402	13.2578	19.0
2.5526	11.0	3443	2.6533	14.8022	4.7367	11.2253	13.1308	19.0
2.5352	12.0	3756	2.6490	14.7306	4.6719	11.158	13.1083	19.0
2.5238	13.0	4069	2.6460	14.7908	4.6958	11.2061	13.1103	19.0
2.5238	14.0	4382	2.6436	14.7332	4.7132	11.1581	13.0709	19.0
2.5067	15.0	4695	2.6403	14.7062	4.7363	11.1275	13.0921	19.0
2.4922	16.0	5008	2.6382	14.735	4.6939	11.1301	13.0941	19.0
2.4922	17.0	5321	2.6353	14.8166	4.7615	11.2635	13.1526	19.0
2.4841	18.0	5634	2.6334	14.8517	4.8063	11.2705	13.1878	19.0
2.4841	19.0	5947	2.6306	14.7038	4.6747	11.1493	13.0818	19.0
2.4789	20.0	6260	2.6312	14.8127	4.7543	11.2775	13.1812	19.0
2.4644	21.0	6573	2.6285	14.7922	4.7114	11.2655	13.1716	19.0
2.4644	22.0	6886	2.6270	14.8587	4.78	11.3163	13.2017	19.0
2.4506	23.0	7199	2.6264	14.7304	4.6852	11.2138	13.1306	19.0
2.4595	24.0	7512	2.6258	14.7294	4.6597	11.2354	13.1126	19.0
2.4595	25.0	7825	2.6257	14.6318	4.6467	11.1913	13.0587	19.0
2.4523	26.0	8138	2.6250	14.7609	4.7037	11.2777	13.1711	19.0
2.4523	27.0	8451	2.6231	14.7342	4.7566	11.2569	13.1351	19.0
2.4317	28.0	8764	2.6223	14.725	4.7248	11.247	13.1234	19.0
2.4374	29.0	9077	2.6231	14.6911	4.7196	11.2372	13.0854	19.0
2.4374	30.0	9390	2.6234	14.6889	4.7202	11.2565	13.1003	19.0
2.4323	31.0	9703	2.6222	14.7264	4.7543	11.2752	13.1442	19.0
2.4295	32.0	10016	2.6215	14.7613	4.723	11.2632	13.1389	19.0
2.4295	33.0	10329	2.6212	14.7716	4.7676	11.3014	13.1637	19.0
2.4282	34.0	10642	2.6211	14.7547	4.7437	11.296	13.1552	19.0
2.4282	35.0	10955	2.6203	14.7717	4.7502	11.2999	13.1498	19.0
2.4265	36.0	11268	2.6208	14.7952	4.7795	11.3294	13.1866	19.0
2.4145	37.0	11581	2.6203	14.8122	4.7814	11.3385	13.1882	19.0
2.4145	38.0	11894	2.6202	14.8281	4.7798	11.3381	13.2065	19.0
2.4241	39.0	12207	2.6202	14.8163	4.7801	11.3492	13.2034	19.0
2.4163	40.0	12520	2.6200	14.8258	4.7741	11.3583	13.2147	19.0

Framework versions

• Transformers 4.17.0
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.2