README.md

metadata

license: apache-2.0
base_model: t5-large
tags:
  - generated_from_trainer
datasets:
  - super_glue
metrics:
  - accuracy
model-index:
  - name: t5-large_boolq_dense_epochs-5
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: super_glue
          type: super_glue
          config: boolq
          split: validation
          args: boolq
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.846177370030581

t5-large_boolq_dense_epochs-5

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

• Loss: 0.3715
• Accuracy: 0.8462

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: 16
• seed: 0
• gradient_accumulation_steps: 4
• 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6792	0.17	50	0.6652	0.6217
0.66	0.34	100	0.6595	0.6220
0.6614	0.51	150	0.6548	0.6232
0.636	0.68	200	0.6122	0.6985
0.4882	0.85	250	0.4702	0.7847
0.5068	1.02	300	0.4639	0.7862
0.3332	1.19	350	0.5297	0.7908
0.4296	1.36	400	0.3955	0.8373
0.356	1.53	450	0.4013	0.8410
0.3227	1.7	500	0.3715	0.8462
0.3516	1.87	550	0.3724	0.8428
0.2169	2.04	600	0.3906	0.8477
0.2199	2.21	650	0.4061	0.8572
0.1969	2.37	700	0.4351	0.8550
0.2713	2.54	750	0.5411	0.8584
0.2458	2.71	800	0.3924	0.8627
0.2134	2.88	850	0.3973	0.8630
0.1636	3.05	900	0.4933	0.8590
0.1108	3.22	950	0.9926	0.8621
0.1433	3.39	1000	0.6679	0.8602

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1