flan-t5-rouge-squad-qg-testb

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

Loss: 1.4778
Rouge1: 0.3588
Rouge2: 0.1173
Rougel: 0.3192
Rougelsum: 0.3430

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: 5e-05
train_batch_size: 80
eval_batch_size: 80
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 320
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 160

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
66.9315	1.0	3	33.8039	0.0551	0.0150	0.0500	0.0511
59.6739	2.0	6	29.7314	0.0864	0.0288	0.0776	0.0784
54.5656	3.0	9	26.4939	0.0875	0.0310	0.0801	0.0805
48.9503	4.0	12	23.5018	0.0705	0.0230	0.0658	0.0661
44.6287	5.0	15	20.5282	0.0584	0.0179	0.0536	0.0541
39.5637	6.0	18	17.2638	0.0729	0.0340	0.0726	0.0724
35.0541	7.0	21	13.6554	0.0856	0.0389	0.0853	0.0853
29.799	8.0	24	10.2408	0.0821	0.0411	0.0816	0.0815
24.9435	9.0	27	8.2909	0.0705	0.0387	0.0707	0.0703
21.8868	10.0	30	7.8624	0.0764	0.0412	0.0765	0.0765
19.6007	11.0	33	7.6396	0.0789	0.0421	0.0791	0.0788
17.6708	12.0	36	7.3917	0.0786	0.0402	0.0757	0.0768
16.0957	13.0	39	7.0298	0.0874	0.0411	0.0845	0.0858
14.8993	14.0	42	6.3918	0.0933	0.0416	0.0885	0.0905
13.8388	15.0	45	5.6045	0.0940	0.0442	0.0889	0.0910
12.7787	16.0	48	5.0419	0.0962	0.0445	0.0899	0.0927
11.9806	17.0	51	4.7749	0.0887	0.0399	0.0822	0.0850
11.2918	18.0	54	4.6137	0.0965	0.0459	0.0910	0.0943
10.7497	19.0	57	4.4894	0.0952	0.0427	0.0875	0.0913
10.3252	20.0	60	4.3848	0.1051	0.0553	0.0968	0.1011
9.8977	21.0	63	4.2909	0.1068	0.0520	0.0945	0.1002
9.6954	22.0	66	4.2021	0.0953	0.0412	0.0822	0.0884
9.4192	23.0	69	4.1174	0.1043	0.0404	0.0887	0.0947
9.147	24.0	72	4.0337	0.1089	0.0466	0.0961	0.1016
8.8825	25.0	75	3.9481	0.1026	0.0436	0.0911	0.0958
8.6989	26.0	78	3.8586	0.1082	0.0477	0.0955	0.1004
8.4876	27.0	81	3.7634	0.1088	0.0479	0.0960	0.1008
8.3068	28.0	84	3.6638	0.1119	0.0432	0.0943	0.1017
8.1205	29.0	87	3.5645	0.1067	0.0409	0.0900	0.0973
7.985	30.0	90	3.4753	0.1205	0.0425	0.0991	0.1075
7.8097	31.0	93	3.4016	0.1024	0.0422	0.0855	0.0925
7.6231	32.0	96	3.3417	0.1108	0.0415	0.0910	0.0985
7.5175	33.0	99	3.2915	0.0932	0.0333	0.0757	0.0833
7.3729	34.0	102	3.2466	0.0965	0.0335	0.0779	0.0864
7.1983	35.0	105	3.2033	0.0967	0.0333	0.0781	0.0859
7.0976	36.0	108	3.1594	0.0861	0.0293	0.0711	0.0760
6.9906	37.0	111	3.1135	0.0861	0.0293	0.0711	0.0760
6.8668	38.0	114	3.0645	0.0910	0.0304	0.0762	0.0812
6.7592	39.0	117	3.0116	0.0985	0.0314	0.0823	0.0873
6.6407	40.0	120	2.9563	0.1002	0.0304	0.0837	0.0887
6.5709	41.0	123	2.9008	0.0981	0.0295	0.0831	0.0871
6.4844	42.0	126	2.8449	0.1117	0.0370	0.0939	0.0979
6.3734	43.0	129	2.7906	0.1034	0.0351	0.0864	0.0907
6.2984	44.0	132	2.7383	0.0929	0.0322	0.0795	0.0826
6.203	45.0	135	2.6884	0.1004	0.0350	0.0840	0.0892
6.1458	46.0	138	2.6409	0.1054	0.0354	0.0885	0.0935
6.0371	47.0	141	2.5963	0.0916	0.0315	0.0783	0.0831
5.9271	48.0	144	2.5539	0.0775	0.0312	0.0657	0.0709
5.9151	49.0	147	2.5119	0.0842	0.0323	0.0717	0.0772
5.7851	50.0	150	2.4719	0.0893	0.0312	0.0726	0.0786
5.6981	51.0	153	2.4329	0.0789	0.0245	0.0634	0.0694
5.6468	52.0	156	2.3949	0.0868	0.0273	0.0677	0.0751
5.5874	53.0	159	2.3584	0.0868	0.0273	0.0677	0.0751
5.5238	54.0	162	2.3235	0.1064	0.0309	0.0853	0.0941
5.4892	55.0	165	2.2884	0.1061	0.0313	0.0851	0.0950
5.3768	56.0	168	2.2536	0.1061	0.0313	0.0851	0.0950
5.2819	57.0	171	2.2191	0.1061	0.0313	0.0851	0.0950
5.2262	58.0	174	2.1852	0.1428	0.0442	0.1169	0.1310
5.2138	59.0	177	2.1516	0.1436	0.0406	0.1190	0.1322
5.1761	60.0	180	2.1187	0.1621	0.0517	0.1353	0.1491
5.1171	61.0	183	2.0865	0.1798	0.0564	0.1551	0.1679
5.0242	62.0	186	2.0561	0.1858	0.0582	0.1624	0.1748
4.9751	63.0	189	2.0281	0.2180	0.0703	0.1937	0.2056
4.9801	64.0	192	2.0015	0.2425	0.0763	0.2178	0.2297
4.8846	65.0	195	1.9753	0.2721	0.0891	0.2489	0.2600
4.8342	66.0	198	1.9501	0.2923	0.0940	0.2652	0.2783
4.8251	67.0	201	1.9253	0.3031	0.0969	0.2759	0.2883
4.7383	68.0	204	1.9017	0.3175	0.1024	0.2882	0.3027
4.7067	69.0	207	1.8781	0.3314	0.1042	0.2996	0.3144
4.6585	70.0	210	1.8545	0.3303	0.1029	0.2995	0.3138
4.5942	71.0	213	1.8311	0.3281	0.1015	0.2993	0.3130
4.554	72.0	216	1.8087	0.3281	0.1015	0.2993	0.3130
4.5399	73.0	219	1.7882	0.3281	0.1015	0.2993	0.3130
4.5289	74.0	222	1.7686	0.3300	0.1048	0.3018	0.3164
4.4602	75.0	225	1.7503	0.3301	0.1053	0.3021	0.3164
4.4387	76.0	228	1.7330	0.3301	0.1053	0.3021	0.3164
4.391	77.0	231	1.7164	0.3288	0.1045	0.3012	0.3158
4.3463	78.0	234	1.7008	0.3401	0.1067	0.3106	0.3253
4.2849	79.0	237	1.6855	0.3467	0.1082	0.3139	0.3309
4.3224	80.0	240	1.6701	0.3467	0.1082	0.3139	0.3309
4.2964	81.0	243	1.6547	0.3467	0.1082	0.3139	0.3309
4.2256	82.0	246	1.6403	0.3467	0.1082	0.3139	0.3309
4.2265	83.0	249	1.6268	0.3474	0.1092	0.3144	0.3316
4.1872	84.0	252	1.6139	0.3468	0.1092	0.3140	0.3310
4.1531	85.0	255	1.6014	0.3529	0.1152	0.3158	0.3370
4.1586	86.0	258	1.5896	0.3529	0.1152	0.3158	0.3370
4.1404	87.0	261	1.5787	0.3529	0.1152	0.3158	0.3370
4.0821	88.0	264	1.5687	0.3529	0.1152	0.3158	0.3370
4.1139	89.0	267	1.5593	0.3557	0.1174	0.3174	0.3401
4.0456	90.0	270	1.5503	0.3587	0.1180	0.3201	0.3426
4.0538	91.0	273	1.5418	0.3587	0.1180	0.3201	0.3426
4.072	92.0	276	1.5339	0.3587	0.1180	0.3201	0.3426
3.9976	93.0	279	1.5264	0.3587	0.1180	0.3201	0.3426
4.0005	94.0	282	1.5194	0.3587	0.1180	0.3201	0.3426
3.9937	95.0	285	1.5132	0.3587	0.1180	0.3201	0.3426
3.9968	96.0	288	1.5077	0.3587	0.1180	0.3201	0.3426
3.9667	97.0	291	1.5027	0.3588	0.1173	0.3192	0.3430
3.9796	98.0	294	1.4982	0.3588	0.1173	0.3192	0.3430
3.9699	99.0	297	1.4941	0.3588	0.1173	0.3192	0.3430
3.8984	100.0	300	1.4903	0.3588	0.1173	0.3192	0.3430
3.898	101.0	303	1.4870	0.3588	0.1173	0.3192	0.3430
3.9476	102.0	306	1.4842	0.3588	0.1173	0.3192	0.3430
3.9353	103.0	309	1.4818	0.3588	0.1173	0.3192	0.3430
3.9377	104.0	312	1.4800	0.3588	0.1173	0.3192	0.3430
3.9396	105.0	315	1.4788	0.3588	0.1173	0.3192	0.3430
3.9439	106.0	318	1.4780	0.3588	0.1173	0.3192	0.3430
7.8954	106.8	320	1.4778	0.3588	0.1173	0.3192	0.3430

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

devagonal/flan-t5-rouge-squad-qg-testb

flan-t5-rouge-squad-qg-testb

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model