metadata

tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: AraBERT_token_classification__AraEval24_merged_rassd_aratweets
    results: []

AraBERT_token_classification__AraEval24_merged_rassd_aratweets

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: 0.8783
Precision: 0.0736
Recall: 0.0243
F1: 0.0365
Accuracy: 0.8564

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
0.7095	1.0	3105	0.8134	1.0	0.0001	0.0002	0.8633
0.6521	2.0	6210	0.7728	0.1149	0.0021	0.0041	0.8631
0.5857	3.0	9315	0.7770	0.0383	0.0009	0.0017	0.8632
0.5233	4.0	12420	0.7929	0.0896	0.0100	0.0180	0.8624
0.5096	5.0	15525	0.7911	0.0716	0.0108	0.0187	0.8617
0.4685	6.0	18630	0.8200	0.0906	0.0144	0.0248	0.8618
0.4393	7.0	21735	0.8399	0.0939	0.0160	0.0273	0.8618
0.4204	8.0	24840	0.8361	0.0862	0.0230	0.0363	0.8590
0.3872	9.0	27945	0.8706	0.0782	0.0251	0.0380	0.8567
0.3569	10.0	31050	0.8783	0.0736	0.0243	0.0365	0.8564

Framework versions

Transformers 4.30.2
Pytorch 1.12.1
Datasets 2.13.2
Tokenizers 0.13.3