correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1801
• Precision: 0.6153
• Recall: 0.7301
• F1: 0.6678
• Accuracy: 0.9346

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	11	0.2746	0.4586	0.5922	0.5169	0.9031
No log	2.0	22	0.2223	0.5233	0.6181	0.5668	0.9148
No log	3.0	33	0.2162	0.5335	0.6699	0.5940	0.9274
No log	4.0	44	0.2053	0.5989	0.7055	0.6478	0.9237
No log	5.0	55	0.2123	0.5671	0.7249	0.6364	0.9267

Framework versions

• Transformers 4.15.0
• Pytorch 1.10.1+cu113
• Datasets 1.18.0
• Tokenizers 0.10.3