learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
15.4767	0.5714	1	18.0250
20.3032	1.5714	2	15.4582
18.4399	2.5714	3	13.1504
17.1036	3.5714	4	11.2818
15.6848	4.5714	5	9.9161
13.6358	5.5714	6	8.8008
11.9557	6.5714	7	7.8137
11.0088	7.5714	8	6.8813
9.0408	8.5714	9	6.0711
7.8859	9.5714	10	5.3071
6.9191	10.5714	11	4.8648
6.0631	11.5714	12	4.8851
4.7754	12.5714	13	4.9477
4.1728	13.5714	14	4.9322
3.5864	14.5714	15	4.5304
2.9632	15.5714	16	3.4513
2.7396	16.5714	17	2.1034
2.3785	17.5714	18	1.1440
2.193	18.5714	19	0.6816
2.1194	19.5714	20	0.5207
1.8983	20.5714	21	0.4698
1.8579	21.5714	22	0.4504
1.7537	22.5714	23	0.4418
1.6646	23.5714	24	0.4355
1.5684	24.5714	25	0.4285
1.5147	25.5714	26	0.4223
1.3791	26.5714	27	0.4167
1.2843	27.5714	28	0.4123
1.2089	28.5714	29	0.4094
1.1636	29.5714	30	0.4085
1.0997	30.5714	31	0.4075
1.0206	31.5714	32	0.4064
0.9747	32.5714	33	0.4038
0.9332	33.5714	34	0.4009
0.9319	34.5714	35	0.3970
0.8823	35.5714	36	0.3917
0.8401	36.5714	37	0.3856
0.8527	37.5714	38	0.3770
0.7512	38.5714	39	0.3655
0.797	39.5714	40	0.3536
0.765	40.5714	41	0.3407
0.7556	41.5714	42	0.3280
0.7198	42.5714	43	0.3157
0.7115	43.5714	44	0.3064
0.7074	44.5714	45	0.2981
0.639	45.5714	46	0.2905
0.6821	46.5714	47	0.2846
0.6098	47.5714	48	0.2789
0.6467	48.5714	49	0.2736
0.6593	49.5714	50	0.2677
0.5884	50.5714	51	0.2619
0.6107	51.5714	52	0.2562
0.6082	52.5714	53	0.2512
0.5592	53.5714	54	0.2470
0.6085	54.5714	55	0.2429
0.5684	55.5714	56	0.2396
0.5467	56.5714	57	0.2360
0.5505	57.5714	58	0.2335
0.5196	58.5714	59	0.2307
0.5306	59.5714	60	0.2280
0.5087	60.5714	61	0.2253
0.5083	61.5714	62	0.2229
0.5099	62.5714	63	0.2208
0.4928	63.5714	64	0.2186
0.4974	64.5714	65	0.2166
0.4766	65.5714	66	0.2144
0.4764	66.5714	67	0.2119
0.4599	67.5714	68	0.2091
0.496	68.5714	69	0.2066
0.3969	69.5714	70	0.2042
0.4769	70.5714	71	0.2018
0.4399	71.5714	72	0.1997
0.4417	72.5714	73	0.1977
0.4203	73.5714	74	0.1958
0.4459	74.5714	75	0.1942
0.3907	75.5714	76	0.1927
0.4548	76.5714	77	0.1917
0.3993	77.5714	78	0.1908
0.439	78.5714	79	0.1901
0.4249	79.5714	80	0.1893
0.4237	80.5714	81	0.1886
0.4178	81.5714	82	0.1881
0.4076	82.5714	83	0.1876
0.4216	83.5714	84	0.1870
0.3817	84.5714	85	0.1864
0.3956	85.5714	86	0.1861
0.4046	86.5714	87	0.1858
0.3896	87.5714	88	0.1855
0.3933	88.5714	89	0.1854
0.4152	89.5714	90	0.1852
0.3682	90.5714	91	0.1850
0.4242	91.5714	92	0.1848
0.3866	92.5714	93	0.1847
0.3844	93.5714	94	0.1846
0.3922	94.5714	95	0.1845
0.3621	95.5714	96	0.1844
0.3854	96.5714	97	0.1843
0.3991	97.5714	98	0.1842
0.3591	98.5714	99	0.1841
0.3664	99.5714	100	0.1841

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.0
Tokenizers 0.21.0

devagonal/t5-base-squad-qag-c

t5-base-squad-qag-c

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

devagonal/
t5-base-squad-qag-c