ArabicNewSplits2_FineTuningAraBERT_run4_AugV5_k2_task1_organization

MayBashendy/ArabicNewSplits2_FineTuningAraBERT_run4_AugV5_k2_task1_organization

ArabicNewSplits2_FineTuningAraBERT_run4_AugV5_k2_task1_organization is a text classification model from MayBashendy. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 3, 2024
Updated: Dec 3, 2024
SHA: 5da84042

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits2_FineTuningAraBERT_run4_AugV5_k2_task1_organization

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7572
Qwk: 0.5855
Mse: 0.7572
Rmse: 0.8702

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	Qwk	Mse	Rmse
No log	0.1667	2	4.6909	-0.0322	4.6909	2.1659
No log	0.3333	4	2.7149	0.0080	2.7149	1.6477
No log	0.5	6	1.8117	0.1619	1.8117	1.3460
No log	0.6667	8	1.4092	0.2691	1.4092	1.1871
No log	0.8333	10	1.1492	0.2344	1.1492	1.0720
No log	1.0	12	0.9635	0.3910	0.9635	0.9816
No log	1.1667	14	0.9354	0.4145	0.9354	0.9672
No log	1.3333	16	0.9190	0.4044	0.9190	0.9586
No log	1.5	18	0.9040	0.4560	0.9040	0.9508
No log	1.6667	20	1.1103	0.3065	1.1103	1.0537
No log	1.8333	22	1.2404	0.2303	1.2404	1.1137
No log	2.0	24	1.5408	0.2869	1.5408	1.2413
No log	2.1667	26	1.3177	0.2936	1.3177	1.1479
No log	2.3333	28	0.9035	0.3945	0.9035	0.9505
No log	2.5	30	0.8375	0.4667	0.8375	0.9151
No log	2.6667	32	0.8624	0.4480	0.8624	0.9287
No log	2.8333	34	0.8165	0.4667	0.8165	0.9036
No log	3.0	36	0.8406	0.3431	0.8406	0.9168
No log	3.1667	38	0.8689	0.3703	0.8689	0.9321
No log	3.3333	40	0.7971	0.4588	0.7971	0.8928
No log	3.5	42	0.7620	0.5017	0.7620	0.8729
No log	3.6667	44	0.7414	0.5017	0.7414	0.8611
No log	3.8333	46	0.7366	0.5294	0.7366	0.8583
No log	4.0	48	0.7263	0.6238	0.7263	0.8522
No log	4.1667	50	0.7326	0.5471	0.7326	0.8559
No log	4.3333	52	0.7154	0.5807	0.7154	0.8458
No log	4.5	54	0.8025	0.6379	0.8025	0.8958
No log	4.6667	56	0.8374	0.6355	0.8374	0.9151
No log	4.8333	58	0.8099	0.6379	0.8099	0.9000
No log	5.0	60	0.7873	0.6587	0.7873	0.8873
No log	5.1667	62	0.7432	0.6416	0.7432	0.8621
No log	5.3333	64	0.7539	0.6261	0.7539	0.8683
No log	5.5	66	0.8086	0.5766	0.8086	0.8992
No log	5.6667	68	0.8203	0.5932	0.8203	0.9057
No log	5.8333	70	0.8287	0.5028	0.8287	0.9103
No log	6.0	72	0.8516	0.6230	0.8516	0.9228
No log	6.1667	74	0.9310	0.5463	0.9310	0.9649
No log	6.3333	76	0.8767	0.5630	0.8767	0.9363
No log	6.5	78	0.8639	0.5630	0.8639	0.9295
No log	6.6667	80	0.8063	0.6168	0.8063	0.8980
No log	6.8333	82	0.8378	0.5932	0.8378	0.9153
No log	7.0	84	0.9299	0.5558	0.9299	0.9643
No log	7.1667	86	0.9697	0.5406	0.9697	0.9847
No log	7.3333	88	0.9256	0.5452	0.9256	0.9621
No log	7.5	90	0.8569	0.5766	0.8569	0.9257
No log	7.6667	92	0.8180	0.5463	0.8180	0.9044
No log	7.8333	94	0.8088	0.6371	0.8088	0.8994
No log	8.0	96	0.8058	0.6371	0.8058	0.8976
No log	8.1667	98	0.8018	0.5855	0.8018	0.8954
No log	8.3333	100	0.7941	0.5855	0.7941	0.8911
No log	8.5	102	0.7897	0.5855	0.7897	0.8886
No log	8.6667	104	0.7832	0.5855	0.7832	0.8850
No log	8.8333	106	0.7760	0.5855	0.7760	0.8809
No log	9.0	108	0.7731	0.5855	0.7731	0.8792
No log	9.1667	110	0.7704	0.6584	0.7704	0.8777
No log	9.3333	112	0.7658	0.6371	0.7658	0.8751
No log	9.5	114	0.7625	0.6371	0.7625	0.8732
No log	9.6667	116	0.7588	0.5855	0.7588	0.8711
No log	9.8333	118	0.7573	0.5855	0.7573	0.8702
No log	10.0	120	0.7572	0.5855	0.7572	0.8702

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
Created: Dec 3, 2024
Updated: Dec 3, 2024