metadata

license: apache-2.0
base_model: lukeleeai/t5-base_cola_densedense_baseline
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-base_cola_dense_mare_mlp_einsum
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: cola
          split: validation
          args: cola
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7516778523489933

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