t5-base-finetuned-ehealth

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

• Loss: 1.3953
• Rouge1: 16.9989
• Rouge2: 4.8395
• Rougel: 13.1702
• Rougelsum: 15.6472
• 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

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	22	4.2413	9.137	1.2333	6.9806	8.1957	18.6901
No log	2.0	44	3.5352	9.5584	1.2176	7.2081	8.5048	18.8187
No log	3.0	66	3.3124	9.9504	1.2105	7.4652	8.7962	18.8187
No log	4.0	88	3.2065	10.3375	1.1847	7.7904	9.1801	18.8947
No log	5.0	110	3.1208	10.777	1.326	8.1305	9.6488	18.8947
No log	6.0	132	3.0495	11.1502	1.4947	8.4386	9.9076	18.924
No log	7.0	154	2.9851	11.1759	1.5744	8.4744	9.9534	18.924
No log	8.0	176	2.9232	10.5745	1.5079	8.1888	9.4731	18.8363
No log	9.0	198	2.8663	10.3156	1.452	8.1662	9.385	18.8947
No log	10.0	220	2.8110	10.5445	1.6067	8.3821	9.6755	18.8538
No log	11.0	242	2.7625	11.0628	1.6957	8.7832	10.1425	18.8947
No log	12.0	264	2.7129	10.9152	1.8386	8.7865	10.0545	18.8538
No log	13.0	286	2.6680	10.8689	1.9024	8.6892	9.883	18.8889
No log	14.0	308	2.6235	10.4118	1.9101	8.2442	9.4505	18.8947
No log	15.0	330	2.5810	11.2578	2.0742	8.7641	10.2349	18.8947
No log	16.0	352	2.5412	11.815	2.1727	9.2403	10.6655	18.9591
No log	17.0	374	2.5056	11.8324	2.1849	9.2089	10.7361	18.9649
No log	18.0	396	2.4710	11.4611	2.1406	8.9329	10.4319	18.8246
No log	19.0	418	2.4365	12.0309	2.4387	9.3966	11.0327	18.8655
No log	20.0	440	2.4039	11.9636	2.4332	9.3448	11.0055	18.8363
No log	21.0	462	2.3734	12.709	2.6945	9.8722	11.572	18.7602
No log	22.0	484	2.3414	13.2227	2.6249	10.1069	11.968	18.7895
3.1829	23.0	506	2.3132	13.3682	2.6082	10.1546	12.0317	18.8246
3.1829	24.0	528	2.2861	14.3195	3.0288	10.8036	12.8973	18.8713
3.1829	25.0	550	2.2592	14.1227	2.6271	10.6826	12.7174	18.9064
3.1829	26.0	572	2.2324	14.3697	2.8314	10.9239	13.0199	18.9064
3.1829	27.0	594	2.2054	14.4512	2.9546	11.0853	13.1193	18.9474
3.1829	28.0	616	2.1810	15.12	3.3732	11.5842	13.6805	18.9474
3.1829	29.0	638	2.1563	14.8242	3.2998	11.2467	13.3076	18.9474
3.1829	30.0	660	2.1333	15.0384	3.3988	11.4676	13.6825	18.9123
3.1829	31.0	682	2.1102	14.9877	3.3844	11.4417	13.5657	18.9591
3.1829	32.0	704	2.0884	14.9699	3.4128	11.4893	13.6109	18.9591
3.1829	33.0	726	2.0646	14.7391	3.0552	11.2351	13.3809	18.9591
3.1829	34.0	748	2.0419	14.9203	3.1074	11.2239	13.4966	18.9591
3.1829	35.0	770	2.0203	15.1875	3.2249	11.3843	13.8011	18.9591
3.1829	36.0	792	1.9988	15.1457	3.1865	11.5238	13.7114	18.9591
3.1829	37.0	814	1.9786	15.2334	3.3739	11.6124	13.8956	18.9591
3.1829	38.0	836	1.9580	15.7105	3.4331	11.8577	14.2217	18.9474
3.1829	39.0	858	1.9387	15.6612	3.5588	12.0279	14.2183	18.9474
3.1829	40.0	880	1.9210	15.8692	3.5665	12.0078	14.3505	18.9591
3.1829	41.0	902	1.9041	15.9888	3.6914	12.0342	14.3375	18.9591
3.1829	42.0	924	1.8834	15.9551	3.6863	12.0562	14.5444	18.9591
3.1829	43.0	946	1.8648	15.9107	3.9128	12.1663	14.5029	18.9591
3.1829	44.0	968	1.8468	15.9831	3.8588	12.196	14.5114	18.9591
3.1829	45.0	990	1.8290	15.9072	3.6844	12.1007	14.5031	18.9591
2.4484	46.0	1012	1.8127	15.9918	3.792	12.2569	14.5287	18.9591
2.4484	47.0	1034	1.7959	15.9685	3.7664	12.1033	14.473	18.9591
2.4484	48.0	1056	1.7799	15.7128	3.505	11.9947	14.216	18.9591
2.4484	49.0	1078	1.7636	15.8033	3.6874	12.1043	14.37	18.9591
2.4484	50.0	1100	1.7487	15.914	3.758	12.1635	14.4603	18.9591
2.4484	51.0	1122	1.7338	15.7088	3.7272	11.951	14.2862	18.9591
2.4484	52.0	1144	1.7202	15.7231	3.6274	12.0492	14.3036	18.9591
2.4484	53.0	1166	1.7081	15.6734	3.5837	11.9265	14.2674	18.9591
2.4484	54.0	1188	1.6935	15.6501	3.5574	11.8579	14.2387	18.9591
2.4484	55.0	1210	1.6793	15.8984	3.8029	12.0981	14.3888	18.9591
2.4484	56.0	1232	1.6666	15.7263	3.6691	12.0325	14.3152	18.9591
2.4484	57.0	1254	1.6516	15.8016	3.6151	12.0349	14.3556	18.9591
2.4484	58.0	1276	1.6385	15.8773	3.7501	12.1887	14.456	18.9591
2.4484	59.0	1298	1.6266	16.0252	3.8027	12.3099	14.5017	18.9591
2.4484	60.0	1320	1.6151	16.29	3.9544	12.5391	14.7691	18.9591
2.4484	61.0	1342	1.6034	16.2891	4.0512	12.5053	14.8155	18.9591
2.4484	62.0	1364	1.5925	16.1871	4.0482	12.4821	14.6986	18.9591
2.4484	63.0	1386	1.5812	16.1774	3.9903	12.4861	14.7798	18.9591
2.4484	64.0	1408	1.5716	16.1663	3.9399	12.4316	14.7449	18.9591
2.4484	65.0	1430	1.5623	16.4455	4.2777	12.7206	14.9193	18.9591
2.4484	66.0	1452	1.5517	16.466	4.2148	12.7613	15.052	18.9591
2.4484	67.0	1474	1.5414	16.5696	4.193	12.6949	15.1064	18.9591
2.4484	68.0	1496	1.5347	16.7602	4.4803	12.938	15.3339	18.9649
2.1379	69.0	1518	1.5278	16.6684	4.3943	12.9152	15.2626	18.9649
2.1379	70.0	1540	1.5193	16.7462	4.4151	12.9251	15.3619	18.9649
2.1379	71.0	1562	1.5104	16.658	4.4187	12.8792	15.2538	18.9591
2.1379	72.0	1584	1.5026	16.8475	4.481	13.0381	15.4041	18.9591
2.1379	73.0	1606	1.4944	16.9066	4.6433	13.1838	15.489	18.9591
2.1379	74.0	1628	1.4864	16.9434	4.6401	13.0527	15.4966	18.9591
2.1379	75.0	1650	1.4801	16.9744	4.694	13.1585	15.5739	19.0
2.1379	76.0	1672	1.4733	17.0546	4.6971	13.0968	15.633	19.0
2.1379	77.0	1694	1.4668	17.1603	4.7771	13.2896	15.7112	19.0
2.1379	78.0	1716	1.4607	17.086	4.7411	13.2587	15.6842	19.0
2.1379	79.0	1738	1.4552	17.0322	4.7652	13.2693	15.711	19.0
2.1379	80.0	1760	1.4493	17.1045	4.8492	13.2752	15.7876	19.0
2.1379	81.0	1782	1.4445	17.0275	4.8688	13.2621	15.7825	19.0
2.1379	82.0	1804	1.4392	17.0985	4.8148	13.2498	15.7718	19.0
2.1379	83.0	1826	1.4337	17.1395	4.8482	13.357	15.8122	19.0
2.1379	84.0	1848	1.4294	17.0411	4.8237	13.3126	15.7736	19.0
2.1379	85.0	1870	1.4254	17.1265	4.8691	13.3033	15.81	19.0
2.1379	86.0	1892	1.4212	16.9899	4.7712	13.1785	15.6416	19.0
2.1379	87.0	1914	1.4176	17.0389	4.7936	13.219	15.7048	19.0
2.1379	88.0	1936	1.4141	17.2266	4.9339	13.3935	15.8629	19.0
2.1379	89.0	1958	1.4108	17.0176	4.8752	13.2829	15.7145	19.0
2.1379	90.0	1980	1.4084	17.154	4.9912	13.3718	15.8255	19.0
1.9718	91.0	2002	1.4061	17.0783	4.9171	13.2617	15.7864	19.0
1.9718	92.0	2024	1.4037	17.0967	4.9393	13.2608	15.8054	19.0
1.9718	93.0	2046	1.4020	17.1524	4.995	13.332	15.8315	19.0
1.9718	94.0	2068	1.4001	17.1357	4.9699	13.3064	15.7932	19.0
1.9718	95.0	2090	1.3988	17.0758	4.8899	13.2231	15.7124	19.0
1.9718	96.0	2112	1.3976	16.9842	4.8395	13.173	15.653	19.0
1.9718	97.0	2134	1.3967	17.0425	4.8395	13.2243	15.6976	19.0
1.9718	98.0	2156	1.3960	16.9842	4.8395	13.173	15.653	19.0
1.9718	99.0	2178	1.3955	16.9842	4.8395	13.173	15.653	19.0
1.9718	100.0	2200	1.3953	16.9989	4.8395	13.1702	15.6472	19.0

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.1
• Tokenizers 0.13.3