bert_gec_detect

This model was trained from scratch on the QALB GEC dataset for a binary classification task, which is classifying whether a generated/given text is grammatically sound/correct.

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2169	1.0	1864	0.2219	0.9330
0.1933	2.0	3728	0.2413	0.9321
0.1632	3.0	5592	0.2905	0.9295
0.1323	4.0	7456	0.2807	0.9346
0.1168	5.0	9320	0.3174	0.9334
0.1018	6.0	11184	0.3848	0.9346
0.0688	7.0	13048	0.4739	0.9325
0.0585	8.0	14912	0.4750	0.9347
0.0545	9.0	16776	0.4894	0.9337
0.0497	10.0	18640	0.5135	0.9349

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.14.7
• Tokenizers 0.15.0