metadata

license: apache-2.0
base_model: t5-3b
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-3b_cola_dense_epochs-8_without_distillation_50
    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: 0.8561840843720039

t5-3b_cola_dense_epochs-8_without_distillation_50

This model is a fine-tuned version of t5-3b on the glue dataset. It achieves the following results on the evaluation set:

Loss: 5.6314
Accuracy: 0.8562

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
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.508	0.19	50	0.4814	0.8054
0.4158	0.37	100	0.3697	0.8399
0.4471	0.56	150	0.3512	0.8543
0.3381	0.75	200	0.3653	0.8399
0.428	0.93	250	0.3373	0.8591
0.2148	1.12	300	1.6354	0.8533
0.1962	1.31	350	1.9031	0.8610
0.2383	1.5	400	0.6977	0.8600
0.2276	1.68	450	0.7896	0.8543
0.2574	1.87	500	0.5960	0.8571
0.0955	2.06	550	6.3365	0.8543
0.1537	2.24	600	0.7912	0.8667
0.0846	2.43	650	0.8280	0.8658
0.1852	2.62	700	0.4582	0.8581
0.1836	2.8	750	5.0320	0.8485
0.7772	2.99	800	1.2307	0.8600
0.0544	3.18	850	6.9846	0.8466
0.1017	3.36	900	1.1242	0.8552
0.0783	3.55	950	0.6369	0.8667
0.0627	3.74	1000	3.8335	0.8600
0.7314	3.93	1050	2.0148	0.8706
0.024	4.11	1100	5.1811	0.8648
0.0627	4.3	1150	4.7943	0.8773
0.069	4.49	1200	4.1017	0.8639
0.0443	4.67	1250	2.4810	0.8648
0.0295	4.86	1300	2.5363	0.8485
0.0411	5.05	1350	3.3954	0.8581
1.2558	5.23	1400	5.3373	0.8495
0.064	5.42	1450	6.3714	0.8658
0.0259	5.61	1500	7.3145	0.8639
0.0413	5.79	1550	6.4314	0.8667
0.0568	5.98	1600	4.7175	0.8648
0.049	6.17	1650	6.4853	0.8523
0.0689	6.36	1700	3.8090	0.8677
0.6785	6.54	1750	4.8987	0.8600
0.6287	6.73	1800	3.7412	0.8658
0.1197	6.92	1850	5.6841	0.8629
0.0528	7.1	1900	4.6580	0.8591
0.6495	7.29	1950	5.2935	0.8619
0.0764	7.48	2000	4.2176	0.8466
0.0438	7.66	2050	6.9325	0.8533
0.0583	7.85	2100	4.7150	0.8591

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.9.0
Tokenizers 0.14.1