t5-base_cola_dense_mare_mlp_einsum

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

• Loss: 0.7682
• Accuracy: 0.7517

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: 8
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5856	0.19	50	0.6260	0.6913
0.5836	0.37	100	0.6029	0.6913
0.5724	0.56	150	0.6055	0.6932
0.6635	0.75	200	0.6171	0.6922
0.5634	0.93	250	0.6162	0.6999
0.5361	1.12	300	0.6142	0.6932
0.5426	1.31	350	0.5920	0.7057
0.6255	1.5	400	0.5884	0.7095
0.6312	1.68	450	0.5723	0.7095
0.5686	1.87	500	0.5894	0.7057
0.5486	2.06	550	0.5590	0.7124
0.4436	2.24	600	0.5838	0.7220
0.4405	2.43	650	0.6176	0.7315
0.4785	2.62	700	0.6236	0.7296
0.5759	2.8	750	0.6233	0.7191
0.6156	2.99	800	0.6807	0.7392
0.4843	3.18	850	0.6337	0.7373
0.5408	3.36	900	0.7107	0.7392
0.4327	3.55	950	0.6256	0.7239
0.4318	3.74	1000	0.6951	0.7478
0.4047	3.93	1050	0.6566	0.7430
0.423	4.11	1100	0.6731	0.7440
0.3919	4.3	1150	0.6750	0.7392
0.4041	4.49	1200	0.6464	0.7421
0.3941	4.67	1250	0.6580	0.7517
0.3834	4.86	1300	0.6257	0.7459
0.2678	5.05	1350	0.6464	0.7555
0.3202	5.23	1400	0.7048	0.7507
0.2869	5.42	1450	0.7405	0.7565
0.3359	5.61	1500	0.6393	0.7593
0.3528	5.79	1550	0.6249	0.7555
0.3304	5.98	1600	0.6349	0.7565
0.2862	6.17	1650	0.7497	0.7670
0.2315	6.36	1700	0.7787	0.7622
0.3251	6.54	1750	0.7038	0.7555
0.3584	6.73	1800	0.7732	0.7603
0.1804	6.92	1850	0.8226	0.7584
0.2264	7.1	1900	0.7420	0.7613
0.2374	7.29	1950	0.7825	0.7507
0.203	7.48	2000	0.7575	0.7641
0.238	7.66	2050	1.9945	0.7603
0.2328	7.85	2100	0.7682	0.7517

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu117
• Datasets 2.9.0
• Tokenizers 0.11.6