Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold0

Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold0 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: Nov 18, 2024
Updated: Nov 24, 2024
SHA: eaa3f357

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold0

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: 0.8982
Qwk: 0.6326
Mse: 0.8982
Rmse: 0.9477

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	0.0952	2	4.7457	0.0011	4.7457	2.1785
No log	0.1905	4	2.3065	0.1874	2.3065	1.5187
No log	0.2857	6	1.4646	0.0742	1.4646	1.2102
No log	0.3810	8	1.1990	0.4854	1.1990	1.0950
No log	0.4762	10	1.0417	0.3845	1.0417	1.0206
No log	0.5714	12	0.9966	0.4085	0.9966	0.9983
No log	0.6667	14	0.9733	0.4603	0.9733	0.9865
No log	0.7619	16	1.0874	0.5523	1.0874	1.0428
No log	0.8571	18	1.1398	0.5490	1.1398	1.0676
No log	0.9524	20	1.3764	0.4731	1.3764	1.1732
No log	1.0476	22	2.0461	0.2541	2.0461	1.4304
No log	1.1429	24	1.9387	0.2759	1.9387	1.3924
No log	1.2381	26	1.2038	0.5299	1.2038	1.0972
No log	1.3333	28	1.1280	0.4085	1.1280	1.0621
No log	1.4286	30	1.2677	0.2827	1.2677	1.1259
No log	1.5238	32	1.1605	0.2827	1.1605	1.0773
No log	1.6190	34	0.9430	0.4085	0.9430	0.9711
No log	1.7143	36	0.9988	0.5305	0.9988	0.9994
No log	1.8095	38	1.1237	0.5660	1.1237	1.0600
No log	1.9048	40	0.9560	0.5660	0.9560	0.9778
No log	2.0	42	0.7901	0.6927	0.7901	0.8889
No log	2.0952	44	0.9326	0.5241	0.9326	0.9657
No log	2.1905	46	0.9808	0.5241	0.9808	0.9904
No log	2.2857	48	0.8310	0.6447	0.8310	0.9116
No log	2.3810	50	0.7405	0.7095	0.7405	0.8605
No log	2.4762	52	0.7184	0.7351	0.7184	0.8476
No log	2.5714	54	0.6892	0.7172	0.6892	0.8302
No log	2.6667	56	0.6880	0.7351	0.6880	0.8294
No log	2.7619	58	0.6646	0.7346	0.6646	0.8153
No log	2.8571	60	0.6976	0.6922	0.6976	0.8352
No log	2.9524	62	0.8389	0.6569	0.8389	0.9159
No log	3.0476	64	0.9367	0.6219	0.9367	0.9678
No log	3.1429	66	0.9178	0.6672	0.9178	0.9580
No log	3.2381	68	0.8897	0.6677	0.8897	0.9433
No log	3.3333	70	0.7605	0.6574	0.7605	0.8721
No log	3.4286	72	0.7235	0.6406	0.7235	0.8506
No log	3.5238	74	0.7031	0.6406	0.7031	0.8385
No log	3.6190	76	0.7071	0.6406	0.7071	0.8409
No log	3.7143	78	0.8664	0.6141	0.8664	0.9308
No log	3.8095	80	1.1160	0.5532	1.1160	1.0564
No log	3.9048	82	1.1228	0.5532	1.1228	1.0596
No log	4.0	84	0.9610	0.6176	0.9610	0.9803
No log	4.0952	86	0.7689	0.6922	0.7689	0.8769
No log	4.1905	88	0.7149	0.6922	0.7149	0.8455
No log	4.2857	90	0.7134	0.6922	0.7134	0.8446
No log	4.3810	92	0.7780	0.6922	0.7780	0.8820
No log	4.4762	94	0.8264	0.6356	0.8264	0.9091
No log	4.5714	96	0.8969	0.6108	0.8969	0.9471
No log	4.6667	98	0.9714	0.6220	0.9714	0.9856
No log	4.7619	100	0.9246	0.6187	0.9246	0.9616
No log	4.8571	102	0.8474	0.6341	0.8474	0.9205
No log	4.9524	104	0.7304	0.7123	0.7304	0.8546
No log	5.0476	106	0.6868	0.7603	0.6868	0.8287
No log	5.1429	108	0.6752	0.7689	0.6752	0.8217
No log	5.2381	110	0.6219	0.7921	0.6219	0.7886
No log	5.3333	112	0.6734	0.72	0.6734	0.8206
No log	5.4286	114	0.8522	0.7284	0.8522	0.9231
No log	5.5238	116	0.9695	0.6936	0.9695	0.9846
No log	5.6190	118	0.9671	0.6886	0.9671	0.9834
No log	5.7143	120	0.9941	0.7368	0.9941	0.9971
No log	5.8095	122	1.0151	0.7183	1.0151	1.0075
No log	5.9048	124	0.9881	0.7442	0.9881	0.9940
No log	6.0	126	1.0601	0.6675	1.0601	1.0296
No log	6.0952	128	1.1227	0.6051	1.1227	1.0596
No log	6.1905	130	1.1550	0.6419	1.1550	1.0747
No log	6.2857	132	1.1060	0.6419	1.1060	1.0517
No log	6.3810	134	1.0784	0.6632	1.0784	1.0385
No log	6.4762	136	1.0012	0.6632	1.0012	1.0006
No log	6.5714	138	0.9756	0.6632	0.9756	0.9877
No log	6.6667	140	0.9203	0.7368	0.9203	0.9593
No log	6.7619	142	0.8840	0.7368	0.8840	0.9402
No log	6.8571	144	0.8604	0.7368	0.8604	0.9276
No log	6.9524	146	0.8711	0.7368	0.8711	0.9333
No log	7.0476	148	0.8922	0.7368	0.8922	0.9446
No log	7.1429	150	0.9546	0.6436	0.9546	0.9770
No log	7.2381	152	0.9686	0.5912	0.9686	0.9842
No log	7.3333	154	0.9195	0.6286	0.9195	0.9589
No log	7.4286	156	0.8168	0.7509	0.8168	0.9038
No log	7.5238	158	0.7534	0.7650	0.7534	0.8680
No log	7.6190	160	0.7409	0.7727	0.7409	0.8607
No log	7.7143	162	0.7317	0.7727	0.7317	0.8554
No log	7.8095	164	0.7660	0.7650	0.7660	0.8752
No log	7.9048	166	0.8190	0.7724	0.8190	0.9050
No log	8.0	168	0.9095	0.6761	0.9095	0.9537
No log	8.0952	170	1.0266	0.6061	1.0266	1.0132
No log	8.1905	172	1.1532	0.6513	1.1532	1.0739
No log	8.2857	174	1.1839	0.6513	1.1839	1.0881
No log	8.3810	176	1.1761	0.6765	1.1761	1.0845
No log	8.4762	178	1.1642	0.6765	1.1642	1.0790
No log	8.5714	180	1.1318	0.6765	1.1318	1.0639
No log	8.6667	182	1.0638	0.6326	1.0638	1.0314
No log	8.7619	184	0.9857	0.6326	0.9857	0.9928
No log	8.8571	186	0.9270	0.6576	0.9270	0.9628
No log	8.9524	188	0.8669	0.6978	0.8669	0.9311
No log	9.0476	190	0.8177	0.6789	0.8177	0.9042
No log	9.1429	192	0.7994	0.6987	0.7994	0.8941
No log	9.2381	194	0.7945	0.6789	0.7945	0.8914
No log	9.3333	196	0.7980	0.6789	0.7980	0.8933
No log	9.4286	198	0.8127	0.6789	0.8127	0.9015
No log	9.5238	200	0.8343	0.6947	0.8343	0.9134
No log	9.6190	202	0.8586	0.6726	0.8586	0.9266
No log	9.7143	204	0.8822	0.6671	0.8822	0.9393
No log	9.8095	206	0.8937	0.6326	0.8937	0.9453
No log	9.9048	208	0.8965	0.6326	0.8965	0.9469
No log	10.0	210	0.8982	0.6326	0.8982	0.9477

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: Nov 18, 2024
Updated: Nov 24, 2024