t5-base-finetuned-stocknews_2000_150

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: 0.5246
• Rouge1: 41.1174
• Rouge2: 36.4917
• Rougel: 40.2739
• Rougelsum: 40.5043
• 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: 150
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	211	0.4220	37.4081	29.7287	35.6792	36.0611	19.0
No log	2.0	422	0.4020	37.6979	30.5377	36.0747	36.4168	19.0
0.3832	3.0	633	0.3947	38.258	31.0862	36.5414	37.0213	19.0
0.3832	4.0	844	0.3850	38.4834	31.3747	36.8077	37.2317	19.0
0.2939	5.0	1055	0.3765	38.8131	32.3372	37.3919	37.7305	19.0
0.2939	6.0	1266	0.3762	39.1749	33.0152	37.6824	38.0201	19.0
0.2939	7.0	1477	0.3569	39.2336	32.9984	37.8439	38.1723	19.0
0.2511	8.0	1688	0.3551	39.452	33.6999	38.3731	38.5895	19.0
0.2511	9.0	1899	0.3523	39.8924	34.2746	38.6913	38.9944	19.0
0.2532	10.0	2110	0.3487	39.9155	34.2762	38.8052	39.077	19.0
0.2532	11.0	2321	0.3533	39.7805	34.2195	38.6591	38.9007	19.0
0.2158	12.0	2532	0.3529	39.6286	34.2772	38.5553	38.8225	19.0
0.2158	13.0	2743	0.3506	40.1899	35.0527	39.2227	39.4969	19.0
0.2158	14.0	2954	0.3474	40.666	35.5759	39.6311	39.9267	19.0
0.1882	15.0	3165	0.3488	40.4267	35.2551	39.2486	39.5608	19.0
0.1882	16.0	3376	0.3547	40.6478	35.5519	39.6034	39.8449	19.0
0.1612	17.0	3587	0.3616	40.7061	35.8348	39.8034	40.0508	19.0
0.1612	18.0	3798	0.3621	40.7052	35.8514	39.7689	40.0123	19.0
0.1434	19.0	4009	0.3632	40.5196	35.649	39.5977	39.8099	19.0
0.1434	20.0	4220	0.3667	40.8356	35.9832	39.9295	40.1647	19.0
0.1434	21.0	4431	0.3711	40.75	35.7893	39.7533	40.0671	19.0
0.1248	22.0	4642	0.3714	40.6404	35.8139	39.6508	39.9206	19.0
0.1248	23.0	4853	0.3720	40.596	35.7999	39.7515	39.9484	19.0
0.1097	24.0	5064	0.3766	40.6635	35.8029	39.8031	40.023	19.0
0.1097	25.0	5275	0.3841	40.6312	35.7811	39.7593	40.0159	19.0
0.1097	26.0	5486	0.3874	40.6912	35.85	39.7479	40.0379	19.0
0.0994	27.0	5697	0.3840	40.7263	35.9777	39.8711	40.1549	19.0
0.0994	28.0	5908	0.3935	40.7512	35.8443	39.7654	40.052	19.0
0.0877	29.0	6119	0.3942	40.801	35.9741	39.8594	40.0986	19.0
0.0877	30.0	6330	0.3977	40.9239	36.1363	40.0563	40.319	19.0
0.0786	31.0	6541	0.4009	40.8977	36.1534	40.0016	40.2385	19.0
0.0786	32.0	6752	0.3996	40.7816	36.1552	39.9214	40.1717	19.0
0.0786	33.0	6963	0.4023	40.9965	36.3464	40.1217	40.3481	19.0
0.0723	34.0	7174	0.4086	40.8352	36.1049	39.8852	40.142	19.0
0.0723	35.0	7385	0.4048	40.9399	36.2465	40.0545	40.3178	19.0
0.0654	36.0	7596	0.4097	40.9975	36.2784	40.0802	40.3726	19.0
0.0654	37.0	7807	0.4117	40.851	36.1677	40.0313	40.3027	19.0
0.0592	38.0	8018	0.4164	40.9427	36.2783	40.1323	40.4087	19.0
0.0592	39.0	8229	0.4187	40.6632	36.0088	39.8049	40.0361	19.0
0.0592	40.0	8440	0.4188	41.008	36.3243	40.1924	40.466	19.0
0.0557	41.0	8651	0.4244	40.887	36.2373	40.0544	40.3017	19.0
0.0557	42.0	8862	0.4219	40.8024	36.1323	39.9768	40.2685	19.0
0.0516	43.0	9073	0.4234	40.7758	36.1291	39.9284	40.1658	19.0
0.0516	44.0	9284	0.4268	40.8067	36.1192	39.9735	40.212	19.0
0.0516	45.0	9495	0.4229	40.8445	36.0577	39.9435	40.1851	19.0
0.0473	46.0	9706	0.4343	40.7118	36.1068	39.9453	40.1875	19.0
0.0473	47.0	9917	0.4311	40.7688	36.0953	39.9612	40.1921	19.0
0.0438	48.0	10128	0.4376	40.9327	36.2236	40.0164	40.2675	19.0
0.0438	49.0	10339	0.4360	41.0039	36.3548	40.0958	40.3716	19.0
0.0408	50.0	10550	0.4418	40.9386	36.3116	40.0052	40.2586	19.0
0.0408	51.0	10761	0.4436	41.0744	36.421	40.1518	40.4014	19.0
0.0408	52.0	10972	0.4427	41.1198	36.4495	40.2116	40.4505	19.0
0.0382	53.0	11183	0.4428	41.0544	36.4075	40.1852	40.4269	19.0
0.0382	54.0	11394	0.4468	41.0366	36.3513	40.1403	40.361	19.0
0.0354	55.0	11605	0.4463	40.9558	36.3748	40.1348	40.3447	19.0
0.0354	56.0	11816	0.4508	40.8857	36.3143	40.0455	40.2318	19.0
0.0338	57.0	12027	0.4544	40.8272	36.244	40.0023	40.2384	19.0
0.0338	58.0	12238	0.4555	40.9537	36.1908	40.0228	40.2483	19.0
0.0338	59.0	12449	0.4521	40.9258	36.1708	40.0611	40.3071	19.0
0.031	60.0	12660	0.4555	40.8837	36.147	40.0305	40.2382	19.0
0.031	61.0	12871	0.4566	40.9297	36.2576	40.09	40.2747	19.0
0.0307	62.0	13082	0.4562	40.8585	36.2582	40.0722	40.25	19.0
0.0307	63.0	13293	0.4592	40.9201	36.2751	40.0861	40.3269	19.0
0.0281	64.0	13504	0.4567	40.9232	36.2481	40.0753	40.3216	19.0
0.0281	65.0	13715	0.4606	41.0077	36.3489	40.1395	40.3744	19.0
0.0281	66.0	13926	0.4649	41.0042	36.5452	40.2019	40.4466	19.0
0.0263	67.0	14137	0.4674	40.9152	36.4575	40.2074	40.4128	19.0
0.0263	68.0	14348	0.4638	40.9942	36.4242	40.2192	40.4164	19.0
0.0258	69.0	14559	0.4652	41.0026	36.3871	40.1336	40.3569	19.0
0.0258	70.0	14770	0.4683	40.9275	36.4236	40.0798	40.3247	19.0
0.0258	71.0	14981	0.4729	40.9299	36.2989	40.1179	40.3533	19.0
0.0245	72.0	15192	0.4713	40.8745	36.2617	40.0829	40.3073	19.0
0.0245	73.0	15403	0.4720	40.9534	36.4602	40.1804	40.4279	19.0
0.0231	74.0	15614	0.4762	41.055	36.552	40.2672	40.5027	19.0
0.0231	75.0	15825	0.4776	40.939	36.492	40.1735	40.3718	19.0
0.0219	76.0	16036	0.4814	41.0543	36.6498	40.3146	40.5381	19.0
0.0219	77.0	16247	0.4826	41.0015	36.5925	40.2389	40.4813	19.0
0.0219	78.0	16458	0.4840	41.0486	36.6352	40.3106	40.5603	19.0
0.0213	79.0	16669	0.4848	40.9784	36.4886	40.1903	40.439	19.0
0.0213	80.0	16880	0.4910	41.175	36.6854	40.3474	40.5917	19.0
0.0204	81.0	17091	0.4843	41.0851	36.5354	40.3005	40.5392	19.0
0.0204	82.0	17302	0.4847	41.2714	36.6856	40.4516	40.672	19.0
0.0196	83.0	17513	0.4860	40.9692	36.3916	40.1273	40.3602	19.0
0.0196	84.0	17724	0.4870	40.9497	36.3933	40.1057	40.3926	19.0
0.0196	85.0	17935	0.4827	41.0823	36.5005	40.2376	40.4651	19.0
0.019	86.0	18146	0.4889	41.1902	36.6614	40.3848	40.6069	19.0
0.019	87.0	18357	0.4890	41.186	36.6136	40.4576	40.6462	19.0
0.0179	88.0	18568	0.4940	41.1593	36.5153	40.377	40.5727	19.0
0.0179	89.0	18779	0.4908	40.9712	36.43	40.1811	40.3797	19.0
0.0179	90.0	18990	0.4914	41.0358	36.4656	40.1936	40.4449	19.0
0.0176	91.0	19201	0.4924	40.8918	36.3329	40.0398	40.2895	19.0
0.0176	92.0	19412	0.4913	41.0889	36.3829	40.213	40.4163	19.0
0.0168	93.0	19623	0.4939	41.048	36.407	40.1863	40.4131	19.0
0.0168	94.0	19834	0.4996	41.0211	36.3687	40.1492	40.3375	19.0
0.016	95.0	20045	0.5000	40.8562	36.2496	39.9959	40.2259	19.0
0.016	96.0	20256	0.4989	41.0123	36.3468	40.1217	40.3407	19.0
0.016	97.0	20467	0.5004	41.0992	36.4577	40.1794	40.4175	19.0
0.0163	98.0	20678	0.5009	41.0319	36.3625	40.1331	40.3442	19.0
0.0163	99.0	20889	0.4978	40.8888	36.238	40.0311	40.2348	19.0
0.0154	100.0	21100	0.5059	40.9034	36.2802	40.033	40.2534	19.0
0.0154	101.0	21311	0.5026	41.0808	36.4192	40.211	40.4242	19.0
0.0148	102.0	21522	0.5043	41.1898	36.4732	40.3336	40.5495	19.0
0.0148	103.0	21733	0.5062	41.216	36.6109	40.408	40.6201	19.0
0.0148	104.0	21944	0.5076	40.9136	36.2326	40.043	40.274	19.0
0.0142	105.0	22155	0.5085	41.1476	36.5099	40.3444	40.5131	19.0
0.0142	106.0	22366	0.5087	41.1	36.4271	40.2888	40.4809	19.0
0.0137	107.0	22577	0.5083	40.8868	36.2128	40.0356	40.2519	19.0
0.0137	108.0	22788	0.5097	41.0436	36.4065	40.2004	40.4431	19.0
0.0137	109.0	22999	0.5113	41.1789	36.617	40.3938	40.5925	19.0
0.0137	110.0	23210	0.5127	40.989	36.3659	40.1097	40.3074	19.0
0.0137	111.0	23421	0.5144	41.0157	36.3607	40.1239	40.3237	19.0
0.0132	112.0	23632	0.5153	40.9412	36.3165	40.0601	40.283	19.0
0.0132	113.0	23843	0.5127	41.011	36.3343	40.1059	40.3317	19.0
0.0138	114.0	24054	0.5174	40.9507	36.3226	40.0426	40.2821	19.0
0.0138	115.0	24265	0.5172	40.9169	36.2471	40.0189	40.2581	19.0
0.0138	116.0	24476	0.5191	40.9621	36.2937	40.0859	40.2872	19.0
0.0129	117.0	24687	0.5164	40.9124	36.2428	40.0247	40.2636	19.0
0.0129	118.0	24898	0.5217	40.8482	36.2412	39.983	40.2084	19.0
0.0131	119.0	25109	0.5191	40.9377	36.3549	40.0702	40.303	19.0
0.0131	120.0	25320	0.5206	41.0878	36.5262	40.2577	40.4903	19.0
0.0123	121.0	25531	0.5223	40.9777	36.4348	40.1438	40.3255	19.0
0.0123	122.0	25742	0.5200	40.9512	36.2822	40.0795	40.2998	19.0
0.0123	123.0	25953	0.5244	40.9508	36.3301	40.0726	40.3256	19.0
0.0125	124.0	26164	0.5225	41.1733	36.4561	40.3336	40.5512	19.0
0.0125	125.0	26375	0.5240	41.0364	36.4154	40.189	40.4268	19.0
0.0118	126.0	26586	0.5246	41.1267	36.4904	40.3025	40.5672	19.0
0.0118	127.0	26797	0.5214	40.9609	36.417	40.1255	40.3472	19.0
0.0125	128.0	27008	0.5196	41.1335	36.4937	40.3248	40.5371	19.0
0.0125	129.0	27219	0.5214	41.1757	36.606	40.3908	40.6112	19.0
0.0125	130.0	27430	0.5190	41.1436	36.5116	40.344	40.5505	19.0
0.012	131.0	27641	0.5227	41.0854	36.5638	40.2975	40.5342	19.0
0.012	132.0	27852	0.5233	41.0652	36.5087	40.2447	40.4784	19.0
0.0117	133.0	28063	0.5251	41.1272	36.4621	40.2664	40.4917	19.0
0.0117	134.0	28274	0.5215	41.1819	36.5561	40.3583	40.5515	19.0
0.0117	135.0	28485	0.5219	41.1615	36.5308	40.323	40.5283	19.0
0.0116	136.0	28696	0.5228	41.0947	36.4701	40.2537	40.4725	19.0
0.0116	137.0	28907	0.5211	41.1187	36.4948	40.2711	40.4957	19.0
0.0114	138.0	29118	0.5219	41.0826	36.4684	40.2557	40.4678	19.0
0.0114	139.0	29329	0.5223	41.1453	36.5356	40.3132	40.5333	19.0
0.0111	140.0	29540	0.5237	41.1055	36.4938	40.2656	40.4907	19.0
0.0111	141.0	29751	0.5241	41.1391	36.4983	40.2896	40.5215	19.0
0.0111	142.0	29962	0.5243	41.1702	36.5621	40.3401	40.5579	19.0
0.0112	143.0	30173	0.5242	41.1499	36.5609	40.3355	40.5387	19.0
0.0112	144.0	30384	0.5236	41.1261	36.5274	40.3011	40.522	19.0
0.011	145.0	30595	0.5240	41.1174	36.4917	40.2739	40.5043	19.0
0.011	146.0	30806	0.5248	41.1174	36.4917	40.2739	40.5043	19.0
0.0106	147.0	31017	0.5241	41.1174	36.4917	40.2739	40.5043	19.0
0.0106	148.0	31228	0.5243	41.1174	36.4917	40.2739	40.5043	19.0
0.0106	149.0	31439	0.5245	41.1174	36.4917	40.2739	40.5043	19.0
0.0105	150.0	31650	0.5246	41.1174	36.4917	40.2739	40.5043	19.0

Framework versions

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