t5-large_cola_sp0_ar0

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

• Loss: 0.4179
• Accuracy: 0.8809

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: 16
• eval_batch_size: 32
• seed: 1
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5885	0.05	25	0.6751	0.6913
0.5475	0.11	50	0.5338	0.6913
0.5122	0.16	75	0.4847	0.7919
0.4486	0.21	100	0.5089	0.7996
0.4087	0.27	125	0.5139	0.8063
0.4022	0.32	150	0.5188	0.8035
0.4245	0.37	175	0.5196	0.7987
0.4298	0.42	200	0.6226	0.8006
0.4326	0.48	225	0.6169	0.8015
0.4321	0.53	250	0.6173	0.7987
0.4288	0.58	275	0.4786	0.8102
0.3914	0.64	300	0.5147	0.8054
0.3519	0.69	325	0.5691	0.8150
0.4036	0.74	350	0.4560	0.8236
0.3706	0.8	375	0.4640	0.8245
0.3584	0.85	400	0.4605	0.8207
0.3539	0.9	425	0.4932	0.8217
0.3982	0.96	450	0.5397	0.8073
0.3352	1.01	475	0.5490	0.8150
0.2631	1.06	500	0.9244	0.8121
0.2992	1.11	525	0.5666	0.8169
0.2308	1.17	550	0.7285	0.8178
0.2893	1.22	575	0.6907	0.8198
0.2809	1.27	600	0.4998	0.8140
0.2469	1.33	625	0.7260	0.8236
0.331	1.38	650	0.5812	0.8293
0.286	1.43	675	0.5102	0.8360
0.347	1.49	700	0.5696	0.8255
0.2971	1.54	725	0.4114	0.8380
0.3048	1.59	750	0.5466	0.8169
0.3168	1.65	775	0.4787	0.8274
0.2247	1.7	800	0.7926	0.8063
0.2666	1.75	825	0.5763	0.8274
0.2856	1.8	850	0.5131	0.8303
0.2967	1.86	875	0.4970	0.8293
0.296	1.91	900	0.5532	0.8293
0.2828	1.96	925	0.4777	0.8274
0.2708	2.02	950	0.5433	0.8351
0.1406	2.07	975	0.6351	0.8351
0.2046	2.12	1000	0.6058	0.8332
0.2227	2.18	1025	0.5616	0.8408
0.1551	2.23	1050	1.0299	0.8360
0.1465	2.28	1075	0.7842	0.8380
0.2171	2.34	1100	0.6329	0.8437
0.1588	2.39	1125	0.7575	0.8418
0.4245	2.44	1150	0.7603	0.8351
0.2124	2.49	1175	0.5838	0.8447
0.2333	2.55	1200	0.4896	0.8418
0.1943	2.6	1225	0.6343	0.8332
0.1961	2.65	1250	0.6343	0.8284
0.1981	2.71	1275	0.6145	0.8332
0.2151	2.76	1300	0.6335	0.8360
0.1634	2.81	1325	1.1357	0.8399
0.1526	2.87	1350	1.0044	0.8293
0.2096	2.92	1375	0.7761	0.8360
0.2135	2.97	1400	0.9338	0.8351
0.155	3.03	1425	3.3297	0.8360
0.3667	3.08	1450	4.0564	0.8370
0.5925	3.13	1475	6.7411	0.8408
0.5866	3.18	1500	7.1940	0.8399
0.3812	3.24	1525	7.0097	0.8351
0.1041	3.29	1550	7.0157	0.8351
0.3451	3.34	1575	6.2653	0.8418
0.1121	3.4	1600	4.2608	0.8485

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.11.6