metadata

tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: AraBERT_token_classification_AraEval24_back_translation_mlm1k_augmented
    results: []

AraBERT_token_classification_AraEval24_back_translation_mlm1k_augmented

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

Loss: 0.9905
Precision: 0.0511
Recall: 0.0181
F1: 0.0267
Accuracy: 0.8621

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: 8
eval_batch_size: 8
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	Precision	Recall	F1	Accuracy
0.4984	1.0	7951	0.7412	0.0194	0.0011	0.0020	0.8715
0.4115	2.0	15902	0.7585	0.0571	0.0035	0.0066	0.8721
0.3718	3.0	23853	0.7859	0.0720	0.0049	0.0092	0.8724
0.3331	4.0	31804	0.8117	0.0431	0.0062	0.0108	0.8679
0.3017	5.0	39755	0.8332	0.0477	0.0097	0.0161	0.8658
0.2682	6.0	47706	0.8462	0.0540	0.0123	0.0200	0.8628
0.2627	7.0	55657	0.8597	0.0553	0.0125	0.0204	0.8636
0.2372	8.0	63608	0.9231	0.0556	0.0149	0.0236	0.8646
0.2208	9.0	71559	0.9553	0.0567	0.0160	0.0250	0.8657
0.2206	10.0	79510	0.9905	0.0511	0.0181	0.0267	0.8621

Framework versions

Transformers 4.30.2
Pytorch 1.12.1
Datasets 2.13.2
Tokenizers 0.13.3