metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - matthews_correlation
model-index:
  - name: cola-pixel-handwritten-mean-vatrpp-256-64-4-5e-5-15000-42
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE COLA
          type: glue
          args: cola
        metrics:
          - name: Matthews Correlation
            type: matthews_correlation
            value: 0.07568068132313144

cola-pixel-handwritten-mean-vatrpp-256-64-4-5e-5-15000-42

This model is a fine-tuned version of noniewiem/pixel-handwritten on the GLUE COLA dataset. It achieves the following results on the evaluation set:

Loss: 1.7009
Matthews Correlation: 0.0757

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: 64
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
training_steps: 15000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Matthews Correlation
0.6426	3.03	100	0.6255	0.0
0.6176	6.06	200	0.6308	0.0
0.6183	9.09	300	0.6187	0.0
0.6162	12.12	400	0.6158	0.0
0.614	15.15	500	0.6250	-0.0293
0.6096	18.18	600	0.6185	0.0
0.6055	21.21	700	0.6224	0.0175
0.6001	24.24	800	0.6551	0.1301
0.5909	27.27	900	0.6534	0.0566
0.5726	30.3	1000	0.6679	0.1029
0.5524	33.33	1100	0.6901	0.0631
0.5167	36.36	1200	0.7027	0.0948
0.4779	39.39	1300	0.7578	0.1012
0.4271	42.42	1400	0.8021	0.1108
0.3888	45.45	1500	0.8813	0.1025
0.3428	48.48	1600	0.9362	0.1437
0.2977	51.51	1700	1.0786	0.1118
0.2642	54.54	1800	1.0610	0.0901
0.2272	57.57	1900	1.1835	0.1155
0.1915	60.6	2000	1.2531	0.1224
0.1691	63.63	2100	1.3903	0.0754
0.1491	66.66	2200	1.4947	0.0674
0.1339	69.69	2300	1.5434	0.0736
0.1164	72.72	2400	1.5793	0.1165
0.1078	75.75	2500	1.5938	0.0995
0.0974	78.78	2600	1.7009	0.0757

Framework versions

Transformers 4.17.0
Pytorch 2.3.0+cu121
Datasets 2.0.0
Tokenizers 0.13.3