t5-base-finetuned-stocknews_1900_100

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: 0.4554
• Rouge1: 40.9735
• Rouge2: 36.4343
• Rougel: 40.1125
• Rougelsum: 40.3384
• 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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	211	0.7350	31.7308	20.2914	28.657	29.3167	18.9596
No log	2.0	422	0.6345	33.1681	22.6637	30.5277	31.1213	19.0
0.9162	3.0	633	0.5706	34.6997	24.847	32.2288	32.8098	19.0
0.9162	4.0	844	0.5268	35.4092	26.2862	33.1822	33.6119	19.0
0.6423	5.0	1055	0.4858	36.1444	27.7265	34.1005	34.4616	19.0
0.6423	6.0	1266	0.4560	36.7437	28.449	34.6735	35.1349	19.0
0.6423	7.0	1477	0.4323	37.33	29.5265	35.4853	35.9323	19.0
0.5063	8.0	1688	0.4142	37.1593	29.6064	35.4064	35.8123	19.0
0.5063	9.0	1899	0.3991	38.1553	30.5752	36.2114	36.7167	19.0
0.4102	10.0	2110	0.3864	38.3045	31.2785	36.6248	36.9254	19.0
0.4102	11.0	2321	0.3789	38.2719	31.5007	36.7926	37.0642	19.0
0.3415	12.0	2532	0.3703	38.8466	32.1912	37.3333	37.6131	19.0
0.3415	13.0	2743	0.3618	38.6865	32.2025	37.2779	37.5144	19.0
0.3415	14.0	2954	0.3522	39.3257	33.1793	38.0203	38.2379	19.0
0.2912	15.0	3165	0.3508	39.4422	33.4813	38.2943	38.4649	19.0
0.2912	16.0	3376	0.3506	39.8056	34.1172	38.6625	38.8293	19.0
0.2453	17.0	3587	0.3519	39.9209	34.5123	38.9012	39.0863	19.0
0.2453	18.0	3798	0.3498	40.1987	34.8962	39.2082	39.3708	19.0
0.216	19.0	4009	0.3544	39.6724	34.2613	38.6566	38.7859	19.0
0.216	20.0	4220	0.3539	40.1049	34.8915	39.0681	39.2354	19.0
0.216	21.0	4431	0.3561	40.0241	34.6788	38.9621	39.112	19.0
0.186	22.0	4642	0.3548	40.144	34.8856	39.1343	39.3265	19.0
0.186	23.0	4853	0.3564	40.3022	35.2446	39.3555	39.5398	19.0
0.1626	24.0	5064	0.3575	40.2556	35.1322	39.2923	39.4501	19.0
0.1626	25.0	5275	0.3655	40.4588	35.4231	39.5008	39.6855	19.0
0.1626	26.0	5486	0.3687	40.3751	35.4048	39.4194	39.6334	19.0
0.1463	27.0	5697	0.3636	40.5556	35.6104	39.646	39.8315	19.0
0.1463	28.0	5908	0.3724	40.6704	35.7873	39.645	39.8934	19.0
0.1291	29.0	6119	0.3721	40.7764	35.9434	39.8896	40.0641	19.0
0.1291	30.0	6330	0.3767	40.6911	35.868	39.7979	40.0009	19.0
0.115	31.0	6541	0.3776	40.5145	35.7139	39.6426	39.814	19.0
0.115	32.0	6752	0.3752	40.6776	35.8839	39.7995	39.9986	19.0
0.115	33.0	6963	0.3793	40.5806	35.7407	39.6819	39.8721	19.0
0.1051	34.0	7174	0.3871	40.652	35.8792	39.7158	39.9167	19.0
0.1051	35.0	7385	0.3828	40.8275	36.0878	39.9195	40.1043	19.0
0.095	36.0	7596	0.3886	40.9392	36.2701	40.0753	40.2416	19.0
0.095	37.0	7807	0.3908	40.6987	35.9383	39.8522	40.0252	19.0
0.0864	38.0	8018	0.3937	40.9136	36.1533	40.0212	40.1877	19.0
0.0864	39.0	8229	0.3979	40.5823	35.9301	39.7841	39.9357	19.0
0.0864	40.0	8440	0.3971	40.9144	36.1874	40.036	40.2312	19.0
0.0812	41.0	8651	0.4008	40.8206	36.1899	40.0098	40.185	19.0
0.0812	42.0	8862	0.4007	40.6012	35.8957	39.7683	39.932	19.0
0.0747	43.0	9073	0.4001	40.8324	36.0613	39.9346	40.119	19.0
0.0747	44.0	9284	0.4057	40.8783	36.0747	39.9939	40.1931	19.0
0.0747	45.0	9495	0.4026	40.9583	36.2066	40.1362	40.3269	19.0
0.0689	46.0	9706	0.4132	40.6396	36.0119	39.8226	40.0266	19.0
0.0689	47.0	9917	0.4092	40.8679	36.2276	40.0419	40.2269	19.0
0.0643	48.0	10128	0.4131	41.0975	36.4785	40.2175	40.4088	19.0
0.0643	49.0	10339	0.4142	41.084	36.4548	40.1774	40.3793	19.0
0.0599	50.0	10550	0.4162	41.0003	36.4144	40.0912	40.3021	19.0
0.0599	51.0	10761	0.4201	41.123	36.4406	40.2193	40.4498	19.0
0.0599	52.0	10972	0.4185	41.1181	36.4871	40.2354	40.4111	19.0
0.0563	53.0	11183	0.4183	41.0662	36.471	40.2436	40.4196	19.0
0.0563	54.0	11394	0.4222	40.9644	36.3705	40.0994	40.2857	19.0
0.053	55.0	11605	0.4219	41.0366	36.4104	40.2024	40.3756	19.0
0.053	56.0	11816	0.4238	40.9543	36.2944	40.0546	40.2509	19.0
0.0502	57.0	12027	0.4260	40.8299	36.173	39.9556	40.1762	19.0
0.0502	58.0	12238	0.4281	40.7226	36.0612	39.8837	40.0788	19.0
0.0502	59.0	12449	0.4281	40.8293	36.1924	39.9873	40.1796	19.0
0.0466	60.0	12660	0.4276	40.8576	36.1387	40.0215	40.2374	19.0
0.0466	61.0	12871	0.4311	41.0218	36.4164	40.1375	40.3726	19.0
0.0462	62.0	13082	0.4310	41.006	36.333	40.1393	40.3476	19.0
0.0462	63.0	13293	0.4343	41.0375	36.2933	40.1381	40.3135	19.0
0.0423	64.0	13504	0.4315	41.004	36.2703	40.0982	40.31	19.0
0.0423	65.0	13715	0.4346	41.0361	36.3826	40.1206	40.3346	19.0
0.0423	66.0	13926	0.4381	40.8662	36.347	40.0537	40.2147	19.0
0.0405	67.0	14137	0.4383	41.0513	36.4805	40.1781	40.397	19.0
0.0405	68.0	14348	0.4373	40.9528	36.3512	40.0602	40.2812	19.0
0.0398	69.0	14559	0.4385	40.9879	36.3848	40.1668	40.3769	19.0
0.0398	70.0	14770	0.4414	40.9653	36.4555	40.1602	40.3589	19.0
0.0398	71.0	14981	0.4433	41.0236	36.5146	40.1889	40.4139	19.0
0.0378	72.0	15192	0.4423	40.9979	36.3904	40.1236	40.3669	19.0
0.0378	73.0	15403	0.4435	41.0081	36.4075	40.1324	40.3675	19.0
0.0361	74.0	15614	0.4423	41.0208	36.4193	40.1883	40.4144	19.0
0.0361	75.0	15825	0.4449	40.9626	36.3828	40.1797	40.3773	19.0
0.0354	76.0	16036	0.4479	40.9415	36.3803	40.1269	40.3357	19.0
0.0354	77.0	16247	0.4464	41.0229	36.5098	40.2163	40.4094	19.0
0.0354	78.0	16458	0.4464	40.9558	36.413	40.1258	40.3388	19.0
0.0345	79.0	16669	0.4465	40.9385	36.3516	40.0814	40.3247	19.0
0.0345	80.0	16880	0.4531	41.0034	36.4385	40.1536	40.3875	19.0
0.0332	81.0	17091	0.4492	41.0399	36.4823	40.1741	40.4126	19.0
0.0332	82.0	17302	0.4486	41.065	36.5245	40.2065	40.4218	19.0
0.0326	83.0	17513	0.4512	40.9513	36.3926	40.0856	40.3274	19.0
0.0326	84.0	17724	0.4515	40.9202	36.3954	40.0657	40.2837	19.0
0.0326	85.0	17935	0.4504	40.9972	36.518	40.1999	40.4031	19.0
0.0319	86.0	18146	0.4533	40.9467	36.391	40.1257	40.3422	19.0
0.0319	87.0	18357	0.4527	40.9682	36.4798	40.1442	40.3529	19.0
0.0306	88.0	18568	0.4544	40.9622	36.4381	40.149	40.3599	19.0
0.0306	89.0	18779	0.4549	40.9742	36.4306	40.15	40.3669	19.0
0.0306	90.0	18990	0.4531	40.9875	36.4958	40.1809	40.3876	19.0
0.031	91.0	19201	0.4551	40.9555	36.4406	40.144	40.3408	19.0
0.031	92.0	19412	0.4531	40.9665	36.4446	40.1594	40.3673	19.0
0.0299	93.0	19623	0.4544	40.9272	36.3767	40.0731	40.2899	19.0
0.0299	94.0	19834	0.4549	40.9021	36.3566	40.0557	40.2726	19.0
0.0291	95.0	20045	0.4544	40.9254	36.3759	40.0779	40.2962	19.0
0.0291	96.0	20256	0.4546	40.9254	36.3759	40.0779	40.2962	19.0
0.0291	97.0	20467	0.4551	40.9465	36.3891	40.0831	40.3071	19.0
0.0299	98.0	20678	0.4553	40.9465	36.3891	40.0831	40.3071	19.0
0.0299	99.0	20889	0.4554	40.9465	36.3891	40.0831	40.3071	19.0
0.0292	100.0	21100	0.4554	40.9735	36.4343	40.1125	40.3384	19.0

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2