metadata

base_model: elnasharomar2/AraBert_arabic_keyword_extraction
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: AraBert_arabic_keyword_extraction
    results: []

AraBert_arabic_keyword_extraction

This model is a fine-tuned version of aubmindlab/bert-base-arabertv2n) on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4622
Precision: 0.5583
Recall: 0.6294
F1: 0.5917
Accuracy: 0.9297

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2524	1.0	750	0.1969	0.3638	0.4022	0.3820	0.9161
0.1705	2.0	1500	0.1793	0.4424	0.4749	0.4581	0.9240
0.1386	3.0	2250	0.1834	0.4547	0.5438	0.4953	0.9240
0.1091	4.0	3000	0.1987	0.4805	0.5650	0.5193	0.9243
0.0892	5.0	3750	0.2164	0.4951	0.5661	0.5282	0.9259
0.0737	6.0	4500	0.2130	0.5101	0.5635	0.5355	0.9282
0.0579	7.0	5250	0.2301	0.4890	0.5810	0.5311	0.9266
0.0481	8.0	6000	0.2479	0.5025	0.6041	0.5486	0.9269
0.0411	9.0	6750	0.2496	0.5353	0.5739	0.5539	0.9298
0.0348	10.0	7500	0.2719	0.5150	0.6063	0.5570	0.9286
0.0304	11.0	8250	0.2881	0.5252	0.6015	0.5608	0.9283
0.0258	12.0	9000	0.3088	0.5129	0.6093	0.5569	0.9266
0.0231	13.0	9750	0.3110	0.5230	0.5922	0.5555	0.9284
0.0199	14.0	10500	0.3196	0.5243	0.6030	0.5609	0.9282
0.0188	15.0	11250	0.3194	0.5169	0.6041	0.5571	0.9279
0.0146	16.0	750	0.3524	0.5119	0.5993	0.5522	0.9237
0.011	17.0	1500	0.3849	0.4895	0.6410	0.5551	0.9214
0.0087	18.0	2250	0.3469	0.5353	0.6153	0.5725	0.9311
0.0113	19.0	3000	0.3471	0.5150	0.6212	0.5631	0.9268
0.0088	20.0	3750	0.3677	0.5493	0.5929	0.5703	0.9302
0.0068	21.0	4500	0.3867	0.5313	0.6071	0.5667	0.9270
0.0056	22.0	5250	0.3843	0.5435	0.6186	0.5786	0.9293
0.0049	23.0	6000	0.4145	0.5491	0.6272	0.5855	0.9295
0.0043	24.0	6750	0.4290	0.5396	0.6339	0.5830	0.9280
0.0035	25.0	7500	0.4532	0.5322	0.6369	0.5799	0.9274
0.0033	26.0	8250	0.4273	0.5570	0.6227	0.5880	0.9309
0.0032	27.0	9000	0.4415	0.5541	0.6317	0.5903	0.9297
0.0025	28.0	9750	0.4509	0.5518	0.6272	0.5871	0.9291
0.0021	29.0	10500	0.4652	0.5668	0.6179	0.5912	0.9308
0.0026	30.0	11250	0.4622	0.5583	0.6294	0.5917	0.9297

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0