ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k2_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k2_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run1_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 8, 2024
Updated: Dec 8, 2024
SHA: 20f7e69b

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_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: 0.6123
Qwk: 0.7419
Mse: 0.6123
Rmse: 0.7825

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	0.1538	2	5.1352	-0.0281	5.1352	2.2661
No log	0.3077	4	3.0564	0.0993	3.0564	1.7483
No log	0.4615	6	1.9386	0.0876	1.9386	1.3923
No log	0.6154	8	1.2101	0.2961	1.2101	1.1000
No log	0.7692	10	0.9284	0.4955	0.9284	0.9635
No log	0.9231	12	0.8188	0.4422	0.8188	0.9049
No log	1.0769	14	0.8569	0.5193	0.8569	0.9257
No log	1.2308	16	0.8192	0.5726	0.8192	0.9051
No log	1.3846	18	1.6458	0.2903	1.6458	1.2829
No log	1.5385	20	2.0889	0.2105	2.0889	1.4453
No log	1.6923	22	1.4795	0.3075	1.4795	1.2163
No log	1.8462	24	0.8994	0.4396	0.8994	0.9484
No log	2.0	26	0.9671	0.3778	0.9671	0.9834
No log	2.1538	28	0.9944	0.3694	0.9944	0.9972
No log	2.3077	30	0.9306	0.3905	0.9306	0.9647
No log	2.4615	32	0.7763	0.5677	0.7763	0.8811
No log	2.6154	34	0.7378	0.6158	0.7378	0.8589
No log	2.7692	36	0.7676	0.6391	0.7676	0.8762
No log	2.9231	38	0.7209	0.6789	0.7209	0.8491
No log	3.0769	40	0.6991	0.6801	0.6991	0.8361
No log	3.2308	42	0.6016	0.7200	0.6016	0.7757
No log	3.3846	44	0.5620	0.7581	0.5620	0.7497
No log	3.5385	46	0.5607	0.7514	0.5607	0.7488
No log	3.6923	48	0.5802	0.7297	0.5802	0.7617
No log	3.8462	50	0.5675	0.7584	0.5675	0.7534
No log	4.0	52	0.6000	0.7379	0.6000	0.7746
No log	4.1538	54	0.6283	0.7284	0.6283	0.7926
No log	4.3077	56	0.6251	0.7471	0.6251	0.7906
No log	4.4615	58	0.6431	0.7324	0.6431	0.8020
No log	4.6154	60	0.6568	0.7404	0.6568	0.8104
No log	4.7692	62	0.6757	0.7113	0.6757	0.8220
No log	4.9231	64	0.6715	0.7151	0.6715	0.8195
No log	5.0769	66	0.6634	0.7212	0.6634	0.8145
No log	5.2308	68	0.6652	0.7163	0.6652	0.8156
No log	5.3846	70	0.6809	0.7297	0.6809	0.8252
No log	5.5385	72	0.6857	0.7318	0.6857	0.8281
No log	5.6923	74	0.6608	0.7389	0.6608	0.8129
No log	5.8462	76	0.6315	0.7341	0.6315	0.7947
No log	6.0	78	0.6198	0.7398	0.6198	0.7873
No log	6.1538	80	0.6278	0.7237	0.6278	0.7923
No log	6.3077	82	0.6804	0.7239	0.6804	0.8249
No log	6.4615	84	0.6357	0.7230	0.6357	0.7973
No log	6.6154	86	0.5974	0.7272	0.5974	0.7729
No log	6.7692	88	0.5834	0.7475	0.5834	0.7638
No log	6.9231	90	0.5820	0.7475	0.5820	0.7629
No log	7.0769	92	0.5865	0.7448	0.5865	0.7658
No log	7.2308	94	0.5874	0.7485	0.5874	0.7664
No log	7.3846	96	0.5899	0.7503	0.5899	0.7680
No log	7.5385	98	0.5895	0.7506	0.5895	0.7678
No log	7.6923	100	0.5972	0.7349	0.5972	0.7728
No log	7.8462	102	0.6024	0.7405	0.6024	0.7761
No log	8.0	104	0.6158	0.7305	0.6158	0.7847
No log	8.1538	106	0.6346	0.7209	0.6346	0.7966
No log	8.3077	108	0.6488	0.7169	0.6488	0.8055
No log	8.4615	110	0.6429	0.7215	0.6429	0.8018
No log	8.6154	112	0.6250	0.7281	0.6250	0.7905
No log	8.7692	114	0.6176	0.7343	0.6176	0.7859
No log	8.9231	116	0.6173	0.7343	0.6173	0.7857
No log	9.0769	118	0.6147	0.7334	0.6147	0.7840
No log	9.2308	120	0.6133	0.7351	0.6133	0.7832
No log	9.3846	122	0.6125	0.7351	0.6125	0.7826
No log	9.5385	124	0.6118	0.7345	0.6118	0.7822
No log	9.6923	126	0.6119	0.7345	0.6119	0.7822
No log	9.8462	128	0.6121	0.7361	0.6121	0.7823
No log	10.0	130	0.6123	0.7419	0.6123	0.7825

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 8, 2024
Updated: Dec 8, 2024