clincolnoz/bert-base-uncased-edos

final-lr2e-5-bs16-fp16-2

This model is a fine-tuned version of bert-base-uncased on an https://github.com/rewire-online/edos dataset. It achieves the following results on the evaluation set:

• Loss: 0.4823
• F1 Macro: 0.8301
• F1 Weighted: 0.8772
• F1: 0.7388
• Accuracy: 0.8792
• Confusion Matrix: [[2834 196] [ 287 683]]
• Confusion Matrix Norm: [[0.93531353 0.06468647] [0.29587629 0.70412371]]
• Classification Report: precision recall f1-score support 0 0.908042 0.935314 0.921476 3030.00000

1 0.777019 0.704124 0.738778 970.00000 accuracy 0.879250 0.879250 0.879250 0.87925 macro avg 0.842531 0.819719 0.830127 4000.00000 weighted avg 0.876269 0.879250 0.877172 4000.00000

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: 12345
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Weighted	F1	Accuracy	Confusion Matrix	Confusion Matrix Norm	Classification Report
0.3333	1.0	1000	0.3064	0.8165	0.8672	0.7181	0.8692	[[2811 219]
[ 304 666]]	[[0.92772277 0.07227723]
[0.31340206 0.68659794]]	precision recall f1-score support
0 0.902408 0.927723 0.914890 3030.00000
1 0.752542 0.686598 0.718059 970.00000
accuracy 0.869250 0.869250 0.869250 0.86925
macro avg 0.827475 0.807160 0.816475 4000.00000
weighted avg 0.866065 0.869250 0.867159 4000.00000
0.2271	2.0	2000	0.3905	0.8238	0.8708	0.7326	0.871	[[2777 253]
[ 263 707]]	[[0.91650165 0.08349835]
[0.27113402 0.72886598]]	precision recall f1-score support
0 0.913487 0.916502 0.914992 3030.000
1 0.736458 0.728866 0.732642 970.000
accuracy 0.871000 0.871000 0.871000 0.871
macro avg 0.824973 0.822684 0.823817 4000.000
weighted avg 0.870557 0.871000 0.870772 4000.000
0.1435	3.0	3000	0.4823	0.8301	0.8772	0.7388	0.8792	[[2834 196]
[ 287 683]]	[[0.93531353 0.06468647]
[0.29587629 0.70412371]]	precision recall f1-score support
0 0.908042 0.935314 0.921476 3030.00000
1 0.777019 0.704124 0.738778 970.00000
accuracy 0.879250 0.879250 0.879250 0.87925
macro avg 0.842531 0.819719 0.830127 4000.00000
weighted avg 0.876269 0.879250 0.877172 4000.00000

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu117
• Datasets 2.9.0
• Tokenizers 0.13.2