t5-base-finetuned-stocknews_1

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

• Loss: 1.4299
• Rouge1: 31.2675
• Rouge2: 18.3987
• Rougel: 27.1272
• Rougelsum: 28.0372
• 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: 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: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	99	1.1909	26.7564	14.0847	23.0574	24.0225	19.0
No log	2.0	198	1.1513	26.8525	14.3487	23.0252	24.0357	19.0
No log	3.0	297	1.1358	27.9251	15.4858	24.1529	25.0564	19.0
No log	4.0	396	1.1249	28.9647	16.322	25.1393	25.9351	19.0
No log	5.0	495	1.1230	29.3277	16.643	25.3965	26.3924	19.0
1.1304	6.0	594	1.1257	29.3298	16.6756	25.2931	26.3113	19.0
1.1304	7.0	693	1.1274	29.8143	17.0961	25.8392	26.7922	19.0
1.1304	8.0	792	1.1349	29.7039	16.8019	25.7436	26.7177	19.0
1.1304	9.0	891	1.1398	29.7954	17.0393	25.9506	26.6055	19.0
1.1304	10.0	990	1.1436	30.2308	17.5247	26.6431	27.2773	19.0
0.8223	11.0	1089	1.1646	30.1807	17.4666	26.4978	27.1534	19.0
0.8223	12.0	1188	1.1700	30.1808	17.7926	26.5241	27.2625	19.0
0.8223	13.0	1287	1.1811	30.5494	18.0376	26.7185	27.5291	19.0
0.8223	14.0	1386	1.1847	30.4785	18.0418	26.8702	27.5021	19.0
0.8223	15.0	1485	1.2043	30.5933	18.3907	27.1218	27.8091	19.0
0.6312	16.0	1584	1.2219	30.5586	18.5247	26.8513	27.6566	19.0
0.6312	17.0	1683	1.2214	30.5018	18.1947	26.9409	27.7452	19.0
0.6312	18.0	1782	1.2322	30.6322	18.1167	26.6699	27.509	19.0
0.6312	19.0	1881	1.2421	31.0753	18.5194	27.0614	27.912	19.0
0.6312	20.0	1980	1.2566	30.8549	18.3715	27.0343	27.8685	19.0
0.513	21.0	2079	1.2740	30.7621	18.5321	26.9539	27.7937	19.0
0.513	22.0	2178	1.2798	31.6185	18.7955	27.4786	28.2485	19.0
0.513	23.0	2277	1.2859	31.0127	18.438	27.0895	27.833	19.0
0.513	24.0	2376	1.3103	31.4955	18.4432	27.3754	28.1693	19.0
0.513	25.0	2475	1.3260	31.6346	18.3461	27.2447	28.1406	19.0
0.4278	26.0	2574	1.3191	31.6779	18.5516	27.5072	28.3363	19.0
0.4278	27.0	2673	1.3293	31.2316	18.2088	27.0875	27.9376	19.0
0.4278	28.0	2772	1.3313	31.2469	18.3832	27.2194	27.9704	19.0
0.4278	29.0	2871	1.3440	31.6021	18.5638	27.328	28.2197	19.0
0.4278	30.0	2970	1.3473	31.7773	18.5585	27.5498	28.3816	19.0
0.3693	31.0	3069	1.3598	31.2278	18.5905	27.0409	27.8962	19.0
0.3693	32.0	3168	1.3686	31.0198	18.4271	26.8683	27.9364	19.0
0.3693	33.0	3267	1.3798	30.8732	18.5114	26.9202	27.8493	19.0
0.3693	34.0	3366	1.3805	31.2322	18.7093	27.3125	28.1878	19.0
0.3693	35.0	3465	1.3870	31.0199	18.5469	27.1357	27.9645	19.0
0.3289	36.0	3564	1.3916	31.3317	18.7421	27.3709	28.2084	19.0
0.3289	37.0	3663	1.3961	31.2699	18.7424	27.3036	28.1781	19.0
0.3289	38.0	3762	1.4041	31.0176	18.4756	27.1868	27.9935	19.0
0.3289	39.0	3861	1.4104	31.1198	18.3739	27.1332	27.979	19.0
0.3289	40.0	3960	1.4142	30.9397	18.4267	27.1613	27.952	19.0
0.2963	41.0	4059	1.4191	31.2112	18.5405	27.2365	28.0131	19.0
0.2963	42.0	4158	1.4159	31.4348	18.6802	27.2705	28.1629	19.0
0.2963	43.0	4257	1.4217	31.3161	18.4061	27.1797	27.9911	19.0
0.2963	44.0	4356	1.4221	31.2979	18.6064	27.2486	28.1006	19.0
0.2963	45.0	4455	1.4231	31.24	18.4439	27.1825	28.0577	19.0
0.2796	46.0	4554	1.4251	31.24	18.4439	27.1825	28.0577	19.0
0.2796	47.0	4653	1.4278	31.3015	18.4439	27.213	28.1327	19.0
0.2796	48.0	4752	1.4292	31.2708	18.3724	27.1466	28.0132	19.0
0.2796	49.0	4851	1.4297	31.2675	18.3987	27.1272	28.0372	19.0
0.2796	50.0	4950	1.4299	31.2675	18.3987	27.1272	28.0372	19.0

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2