Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bert precision	Bert recall	Average word count	Max word count	Min word count	Average token count	% shortened texts with length > 12
1.1052	1.0	100	1.7596	0.4856	0.282	0.458	0.458	0.8852	0.8717	9.1485	19	5	13.7642	12.2271
0.8757	2.0	200	1.6688	0.4887	0.2853	0.4624	0.4634	0.8881	0.867	7.9956	16	4	12.607	5.2402
0.7611	3.0	300	1.6431	0.4559	0.2503	0.422	0.4226	0.8732	0.8655	9.4017	17	3	13.7598	15.2838
0.6987	4.0	400	1.7128	0.4505	0.2426	0.4204	0.4216	0.8773	0.8591	8.1135	15	3	12.9869	2.1834
0.675	5.0	500	1.7736	0.458	0.2581	0.4317	0.4328	0.8717	0.8638	9.2183	19	4	14.0699	11.7904
0.6318	6.0	600	1.8170	0.4631	0.2566	0.4304	0.4323	0.8728	0.8652	9.441	17	3	14.2926	12.2271
0.6309	7.0	700	1.9121	0.4423	0.2243	0.4084	0.4102	0.8712	0.8581	8.393	16	4	13.1921	3.9301
0.556	8.0	800	2.0421	0.4406	0.2387	0.4099	0.4111	0.8614	0.8605	10.2445	18	5	15.2707	18.3406
0.5823	9.0	900	2.0004	0.4101	0.2159	0.3759	0.3768	0.8561	0.854	9.6638	16	5	14.6638	18.3406
0.5624	10.0	1000	2.0062	0.4706	0.2497	0.4325	0.4333	0.873	0.8676	9.4367	16	4	14.3275	12.6638
0.554	11.0	1100	2.0794	0.4706	0.2634	0.4386	0.4389	0.8777	0.8687	9.3799	17	5	14.1616	11.3537
0.5548	12.0	1200	2.1752	0.4463	0.2377	0.416	0.4164	0.8722	0.8602	9.0262	17	3	13.738	9.607
0.5444	13.0	1300	2.2306	0.4307	0.229	0.3985	0.4001	0.8698	0.8553	8.5677	16	3	13.2271	5.2402
0.5351	14.0	1400	2.1538	0.4326	0.2189	0.3974	0.398	0.8702	0.8571	8.7642	19	4	13.4148	6.1135
0.5389	15.0	1500	2.2735	0.4334	0.237	0.4046	0.4057	0.8659	0.8567	9.1616	19	4	14.0175	8.7336
0.5601	16.0	1600	2.3076	0.4291	0.2171	0.3936	0.395	0.8657	0.8578	9.0699	16	1	14.1659	8.2969
0.5361	17.0	1700	2.3043	0.4435	0.2307	0.4074	0.4078	0.871	0.8626	9.1266	16	4	13.8341	9.1703
0.5117	18.0	1800	2.3479	0.4221	0.212	0.3879	0.3881	0.8683	0.8523	8.5633	18	4	12.7686	6.1135
0.5009	19.0	1900	2.3773	0.4457	0.2405	0.4143	0.4147	0.8712	0.8621	8.8428	17	5	13.6288	6.9869
0.542	20.0	2000	2.3419	0.4312	0.2129	0.3902	0.391	0.8647	0.8562	9.1878	16	4	14.2445	10.0437
0.4985	21.0	2100	2.3961	0.43	0.2276	0.3991	0.4011	0.8679	0.8554	8.5197	15	3	13.3974	4.3668
0.5095	22.0	2200	2.4267	0.4373	0.2298	0.4072	0.4077	0.8667	0.8588	9.1616	18	4	13.8253	12.6638
0.51	23.0	2300	2.4633	0.4505	0.2298	0.413	0.4145	0.8712	0.8602	9.0	17	2	13.7074	10.4803
0.4877	24.0	2400	2.5496	0.4266	0.2273	0.3931	0.394	0.8627	0.854	8.7118	14	2	13.6157	4.3668
0.5164	25.0	2500	2.5375	0.438	0.2246	0.4033	0.4055	0.8677	0.8584	8.9476	15	3	13.7467	10.0437
0.5019	26.0	2600	2.6164	0.4145	0.2084	0.3833	0.3837	0.8595	0.8519	9.3057	15	4	14.1135	10.917
0.4905	27.0	2700	2.5586	0.4372	0.2201	0.4043	0.4053	0.8671	0.857	8.6463	15	4	13.3231	5.2402
0.5008	28.0	2800	2.5457	0.4022	0.2011	0.3676	0.3684	0.8576	0.8513	9.1703	16	4	14.0742	6.5502
0.5014	29.0	2900	2.5506	0.413	0.2108	0.3771	0.3776	0.8635	0.8545	9.1004	16	3	13.6463	8.2969
0.5128	30.0	3000	2.5791	0.4121	0.2082	0.3794	0.3794	0.8692	0.8552	8.4585	14	2	12.9083	4.3668
0.5237	31.0	3100	2.6008	0.4219	0.2114	0.3924	0.3918	0.8634	0.8555	9.1921	16	5	13.8646	7.4236
0.4643	32.0	3200	2.6541	0.4304	0.2343	0.4015	0.4016	0.8664	0.8575	8.9127	16	3	13.7249	5.2402
0.4891	33.0	3300	2.6072	0.4205	0.2072	0.3854	0.387	0.8613	0.8548	9.4236	19	3	14.1528	12.2271
0.4981	34.0	3400	2.6505	0.4255	0.2084	0.3926	0.3931	0.865	0.8544	8.952	17	4	13.4672	6.9869
0.4895	35.0	3500	2.5491	0.4192	0.2046	0.3854	0.3862	0.8653	0.8527	8.3843	15	2	13.0218	3.4934
0.5119	36.0	3600	2.5115	0.4088	0.1994	0.3837	0.3838	0.8629	0.8536	9.5415	15	5	14.1179	15.2838
0.5064	37.0	3700	2.4837	0.422	0.2161	0.3923	0.393	0.8655	0.8562	9.0961	15	4	13.6681	6.9869
0.5064	38.0	3800	2.5476	0.4126	0.2085	0.3773	0.3778	0.8605	0.8555	9.0131	15	4	13.8253	6.5502
0.4768	39.0	3900	2.6396	0.4528	0.2268	0.4063	0.4072	0.8706	0.8603	8.7511	15	4	13.2926	3.4934
0.487	40.0	4000	2.4817	0.4272	0.227	0.3991	0.4003	0.868	0.8573	8.8384	18	4	13.393	7.4236
0.4969	41.0	4100	2.5901	0.4211	0.2133	0.387	0.3872	0.8635	0.8543	9.179	18	4	13.6419	10.917
0.4781	42.0	4200	2.6128	0.4286	0.2229	0.3889	0.3901	0.862	0.8578	9.6769	16	5	14.7773	14.4105
0.4865	43.0	4300	2.5942	0.4097	0.2064	0.3789	0.3793	0.8612	0.8535	8.8777	19	2	13.7336	5.6769
0.4833	44.0	4400	2.6585	0.4119	0.2116	0.3796	0.3806	0.859	0.8515	9.3624	19	4	14.1441	9.607
0.4687	45.0	4500	2.7545	0.415	0.2065	0.3862	0.3872	0.8649	0.8534	8.8341	15	4	13.2533	4.8035
0.4832	46.0	4600	2.6578	0.4273	0.2156	0.3935	0.395	0.866	0.8573	9.2445	19	5	14.345	11.3537
0.471	47.0	4700	2.6619	0.4316	0.2274	0.4015	0.4036	0.8658	0.8578	8.9956	15	4	13.8515	7.8603
0.469	48.0	4800	2.7021	0.4328	0.2244	0.3887	0.3897	0.8641	0.854	8.9869	18	5	13.8122	7.4236
0.4784	49.0	4900	2.5634	0.4217	0.2111	0.3871	0.3882	0.8588	0.8553	9.4454	19	5	14.476	12.6638
0.4947	50.0	5000	2.6781	0.4435	0.2288	0.405	0.4066	0.8709	0.861	8.9258	17	4	13.7467	6.9869
0.4819	51.0	5100	2.6497	0.4255	0.2175	0.3921	0.3932	0.8646	0.8572	9.2096	17	5	14.1354	6.5502
0.4594	52.0	5200	2.7126	0.4246	0.2121	0.3875	0.3891	0.8705	0.854	8.4367	16	4	13.2009	5.2402
0.4804	53.0	5300	2.6285	0.4148	0.2099	0.3833	0.3845	0.8643	0.855	8.6681	15	2	13.441	5.6769
0.4923	54.0	5400	2.6453	0.4343	0.2333	0.4017	0.4026	0.8698	0.8591	8.952	17	2	13.4061	6.5502
0.4712	55.0	5500	2.7145	0.4269	0.2163	0.3927	0.3941	0.8657	0.8577	9.0699	19	3	13.821	7.8603
0.467	56.0	5600	2.7005	0.4241	0.2118	0.3907	0.3903	0.8627	0.8571	9.5371	16	4	14.393	11.7904
0.4584	57.0	5700	2.7004	0.4291	0.2233	0.3956	0.3959	0.865	0.8573	9.4105	18	4	14.214	9.1703
0.4714	58.0	5800	2.5910	0.4306	0.2317	0.3952	0.3957	0.8635	0.8595	9.2969	15	5	14.3188	10.4803
0.4743	59.0	5900	2.6688	0.4328	0.2209	0.395	0.396	0.8668	0.8585	8.9258	16	3	13.7467	5.6769
0.4613	60.0	6000	2.7094	0.4342	0.2294	0.4003	0.4019	0.8673	0.8602	9.0524	18	4	13.9782	7.4236
0.4597	61.0	6100	2.6848	0.4162	0.2217	0.3858	0.3866	0.8612	0.8544	9.2096	17	3	14.0175	9.607
0.4725	62.0	6200	2.7496	0.4348	0.2176	0.395	0.3954	0.8628	0.8636	10.0524	16	5	14.9869	16.5939
0.4324	63.0	6300	2.6998	0.4256	0.2158	0.3946	0.3956	0.8682	0.8557	8.3144	15	4	13.1135	1.7467
0.4315	64.0	6400	2.7197	0.4313	0.2263	0.3892	0.3904	0.866	0.8571	9.1528	14	4	14.0568	8.7336
0.4401	65.0	6500	2.7221	0.4193	0.2151	0.3842	0.3851	0.8622	0.8564	9.1528	17	4	14.0262	6.5502
0.4167	66.0	6600	2.7048	0.4401	0.2327	0.408	0.4084	0.8689	0.8603	9.1921	17	4	13.8035	6.5502
0.4339	67.0	6700	2.7436	0.4373	0.2286	0.4041	0.405	0.8668	0.8586	8.9039	19	3	13.7773	6.5502
0.4435	68.0	6800	2.6951	0.4191	0.2135	0.3827	0.3855	0.8649	0.8538	8.5852	14	3	13.7336	3.0568
0.4513	69.0	6900	2.7253	0.4188	0.2078	0.3865	0.3865	0.8631	0.8539	8.8734	15	5	13.4803	5.2402
0.4457	70.0	7000	2.6112	0.4273	0.2166	0.3882	0.3887	0.8652	0.8573	9.0917	16	5	14.0742	6.5502
0.4456	71.0	7100	2.6492	0.4198	0.2217	0.3916	0.3927	0.868	0.8573	8.6288	14	2	13.3712	5.6769
0.4249	72.0	7200	2.6881	0.4293	0.2178	0.386	0.3874	0.8638	0.8581	9.2926	15	4	14.2882	7.4236
0.439	73.0	7300	2.7046	0.4275	0.2171	0.3917	0.3934	0.8701	0.8556	8.5852	15	3	13.2751	5.2402
0.435	74.0	7400	2.6745	0.4323	0.2235	0.3961	0.3966	0.8637	0.8578	9.1878	19	5	14.2402	9.607
0.448	75.0	7500	2.7169	0.4262	0.2233	0.3904	0.3923	0.8643	0.855	8.821	15	4	13.5808	4.8035
0.4468	76.0	7600	2.6498	0.4368	0.225	0.3994	0.4016	0.8647	0.8598	9.2358	15	4	14.5109	8.7336
0.4544	77.0	7700	2.7268	0.4358	0.2318	0.4038	0.406	0.8704	0.859	8.6201	13	2	13.1616	1.31
0.4511	78.0	7800	2.7418	0.4381	0.2254	0.3993	0.4006	0.8698	0.8593	8.6769	16	4	13.3319	2.6201
0.4472	79.0	7900	2.7356	0.4332	0.2193	0.3957	0.3956	0.8653	0.8592	9.3231	15	5	14.2227	8.7336
0.4471	80.0	8000	2.6328	0.4383	0.2207	0.4031	0.4047	0.8682	0.86	9.0524	14	4	14.0044	3.9301
0.4312	81.0	8100	2.6819	0.4153	0.2013	0.3764	0.3774	0.862	0.852	8.7511	15	2	13.6856	4.8035
0.4289	82.0	8200	2.7087	0.4327	0.2171	0.3964	0.3971	0.8664	0.856	8.7074	15	4	13.31	5.2402
0.4531	83.0	8300	2.6771	0.4282	0.2195	0.39	0.3911	0.866	0.855	8.6507	15	2	13.2576	3.9301
0.4355	84.0	8400	2.6833	0.4345	0.2162	0.3951	0.3961	0.8659	0.8574	8.8777	14	5	13.6507	4.3668
0.4511	85.0	8500	2.7157	0.4331	0.2185	0.397	0.3983	0.8672	0.859	9.0262	15	5	13.7511	3.9301
0.4383	86.0	8600	2.7073	0.4272	0.2202	0.3929	0.3939	0.8654	0.8566	8.8734	15	5	13.5939	4.8035
0.4221	87.0	8700	2.7001	0.4325	0.2264	0.3977	0.399	0.8667	0.8571	8.7293	14	4	13.4279	3.0568
0.4395	88.0	8800	2.7394	0.4349	0.2188	0.3961	0.3972	0.8667	0.8591	8.8734	14	4	13.7074	2.1834
0.4365	89.0	8900	2.7430	0.4368	0.2272	0.4004	0.4013	0.867	0.8591	9.0524	15	4	13.7598	5.6769
0.4501	90.0	9000	2.7777	0.4327	0.22	0.3962	0.3972	0.8667	0.8562	8.6812	15	4	13.4323	3.0568
0.4359	91.0	9100	2.7498	0.4401	0.2273	0.4028	0.4042	0.8668	0.861	9.3188	15	4	14.0306	9.607
0.4445	92.0	9200	2.7315	0.4339	0.2214	0.3947	0.3957	0.865	0.8594	9.1004	15	4	13.8952	4.8035
0.445	93.0	9300	2.7602	0.4392	0.2258	0.3994	0.4007	0.867	0.8601	8.9869	15	4	13.7424	3.9301
0.4197	94.0	9400	2.7757	0.4431	0.2259	0.3992	0.4007	0.8676	0.8611	9.1485	16	4	13.8646	5.2402
0.4425	95.0	9500	2.7751	0.4373	0.2202	0.3946	0.3961	0.8661	0.86	9.1092	15	3	13.8297	5.2402
0.4337	96.0	9600	2.7765	0.4426	0.227	0.4005	0.4021	0.8681	0.8615	9.0175	15	4	13.7467	4.8035
0.439	97.0	9700	2.7823	0.443	0.2272	0.4013	0.4028	0.8685	0.8613	9.048	15	4	13.7555	5.2402
0.4519	98.0	9800	2.7894	0.4446	0.2294	0.4035	0.4046	0.8686	0.8611	8.9956	15	4	13.6507	5.2402
0.4563	99.0	9900	2.7929	0.4453	0.2327	0.4054	0.4072	0.869	0.8618	9.0393	16	4	13.7249	4.8035
0.4316	100.0	10000	2.7906	0.4431	0.2299	0.4035	0.4054	0.8678	0.8614	9.0699	15	4	13.7991	4.8035

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

ldos/text_shortening_model_v24

text_shortening_model_v24

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ldos/
text_shortening_model_v24