Arabic_FineTuningAraBERT_run3_AugV5_k2_task1_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_run3_AugV5_k2_task1_organization_fold1

Arabic_FineTuningAraBERT_run3_AugV5_k2_task1_organization_fold1 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 28, 2024
Updated: Nov 28, 2024
SHA: 011c5077

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_run3_AugV5_k2_task1_organization_fold1

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: 0.5892
Qwk: 0.5825
Mse: 0.5892
Rmse: 0.7676

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	0.0645	2	3.2655	0.0755	3.2655	1.8071
No log	0.1290	4	2.1611	0.1881	2.1611	1.4701
No log	0.1935	6	1.7116	0.0	1.7116	1.3083
No log	0.2581	8	1.4178	0.0	1.4178	1.1907
No log	0.3226	10	1.2530	0.0	1.2530	1.1194
No log	0.3871	12	1.1439	0.0	1.1439	1.0695
No log	0.4516	14	1.0401	0.0	1.0401	1.0198
No log	0.5161	16	0.9646	0.0187	0.9646	0.9821
No log	0.5806	18	0.9897	0.0	0.9897	0.9949
No log	0.6452	20	0.9909	0.0	0.9909	0.9955
No log	0.7097	22	0.9952	0.0	0.9952	0.9976
No log	0.7742	24	1.0816	0.0	1.0816	1.0400
No log	0.8387	26	1.1260	0.0	1.1260	1.0611
No log	0.9032	28	1.0332	0.0	1.0332	1.0164
No log	0.9677	30	0.9485	0.0	0.9485	0.9739
No log	1.0323	32	0.8401	0.3834	0.8401	0.9166
No log	1.0968	34	0.7508	0.5435	0.7508	0.8665
No log	1.1613	36	0.7314	0.5435	0.7314	0.8552
No log	1.2258	38	0.7312	0.5435	0.7312	0.8551
No log	1.2903	40	0.7548	0.5882	0.7548	0.8688
No log	1.3548	42	0.7717	0.5604	0.7717	0.8785
No log	1.4194	44	0.8060	0.4815	0.8060	0.8978
No log	1.4839	46	0.8387	0.4815	0.8387	0.9158
No log	1.5484	48	0.8439	0.4815	0.8439	0.9186
No log	1.6129	50	0.7619	0.4928	0.7619	0.8729
No log	1.6774	52	0.7178	0.6169	0.7178	0.8472
No log	1.7419	54	0.7046	0.4896	0.7046	0.8394
No log	1.8065	56	0.6829	0.4896	0.6829	0.8263
No log	1.8710	58	0.6607	0.4444	0.6607	0.8128
No log	1.9355	60	0.6360	0.4444	0.6360	0.7975
No log	2.0	62	0.6154	0.5505	0.6154	0.7845
No log	2.0645	64	0.6141	0.6128	0.6141	0.7836
No log	2.1290	66	0.5898	0.6128	0.5898	0.7680
No log	2.1935	68	0.5816	0.4896	0.5816	0.7626
No log	2.2581	70	0.5774	0.4803	0.5774	0.7598
No log	2.3226	72	0.5739	0.6026	0.5739	0.7576
No log	2.3871	74	0.5637	0.6379	0.5637	0.7508
No log	2.4516	76	0.5694	0.4664	0.5694	0.7546
No log	2.5161	78	0.5619	0.5044	0.5619	0.7496
No log	2.5806	80	0.5604	0.6128	0.5604	0.7486
No log	2.6452	82	0.5618	0.5044	0.5618	0.7496
No log	2.7097	84	0.5860	0.3883	0.5860	0.7655
No log	2.7742	86	0.5923	0.4138	0.5923	0.7696
No log	2.8387	88	0.5730	0.4306	0.5730	0.7570
No log	2.9032	90	0.5809	0.4306	0.5809	0.7622
No log	2.9677	92	0.6081	0.4896	0.6081	0.7798
No log	3.0323	94	0.6192	0.4057	0.6192	0.7869
No log	3.0968	96	0.6303	0.5377	0.6303	0.7939
No log	3.1613	98	0.6551	0.4731	0.6551	0.8094
No log	3.2258	100	0.6912	0.4262	0.6912	0.8314
No log	3.2903	102	0.6856	0.4731	0.6856	0.8280
No log	3.3548	104	0.6641	0.4909	0.6641	0.8149
No log	3.4194	106	0.6393	0.4803	0.6393	0.7996
No log	3.4839	108	0.6255	0.4803	0.6255	0.7909
No log	3.5484	110	0.5988	0.4803	0.5988	0.7738
No log	3.6129	112	0.5815	0.4803	0.5815	0.7626
No log	3.6774	114	0.5680	0.4803	0.5680	0.7537
No log	3.7419	116	0.5825	0.4306	0.5825	0.7632
No log	3.8065	118	0.6192	0.4138	0.6192	0.7869
No log	3.8710	120	0.5953	0.4563	0.5953	0.7715
No log	3.9355	122	0.5532	0.5088	0.5532	0.7438
No log	4.0	124	0.5119	0.6375	0.5119	0.7155
No log	4.0645	126	0.5043	0.6375	0.5043	0.7101
No log	4.1290	128	0.5220	0.6889	0.5220	0.7225
No log	4.1935	130	0.5230	0.6980	0.5230	0.7232
No log	4.2581	132	0.5071	0.7407	0.5071	0.7121
No log	4.3226	134	0.4714	0.7255	0.4714	0.6866
No log	4.3871	136	0.4805	0.6688	0.4805	0.6932
No log	4.4516	138	0.4833	0.6688	0.4833	0.6952
No log	4.5161	140	0.5160	0.6688	0.5160	0.7183
No log	4.5806	142	0.5329	0.6688	0.5329	0.7300
No log	4.6452	144	0.5479	0.5987	0.5479	0.7402
No log	4.7097	146	0.5537	0.6174	0.5537	0.7441
No log	4.7742	148	0.5479	0.6174	0.5479	0.7402
No log	4.8387	150	0.5080	0.7279	0.5080	0.7127
No log	4.9032	152	0.4906	0.6978	0.4906	0.7004
No log	4.9677	154	0.5221	0.7279	0.5221	0.7226
No log	5.0323	156	0.5517	0.7279	0.5517	0.7428
No log	5.0968	158	0.5902	0.6667	0.5902	0.7682
No log	5.1613	160	0.5994	0.6209	0.5994	0.7742
No log	5.2258	162	0.5982	0.6209	0.5982	0.7734
No log	5.2903	164	0.5891	0.6667	0.5891	0.7675
No log	5.3548	166	0.5834	0.6174	0.5834	0.7638
No log	5.4194	168	0.5820	0.6174	0.5820	0.7629
No log	5.4839	170	0.6087	0.6174	0.6087	0.7802
No log	5.5484	172	0.6257	0.6667	0.6257	0.7910
No log	5.6129	174	0.6434	0.6392	0.6434	0.8021
No log	5.6774	176	0.6063	0.6667	0.6063	0.7787
No log	5.7419	178	0.5442	0.7055	0.5442	0.7377
No log	5.8065	180	0.5311	0.7181	0.5311	0.7288
No log	5.8710	182	0.5177	0.7181	0.5177	0.7195
No log	5.9355	184	0.5051	0.7181	0.5051	0.7107
No log	6.0	186	0.5218	0.6879	0.5218	0.7224
No log	6.0645	188	0.5303	0.7143	0.5303	0.7282
No log	6.1290	190	0.5145	0.6879	0.5145	0.7173
No log	6.1935	192	0.4886	0.6879	0.4886	0.6990
No log	6.2581	194	0.4884	0.6879	0.4884	0.6989
No log	6.3226	196	0.5236	0.7	0.5236	0.7236
No log	6.3871	198	0.5826	0.5912	0.5826	0.7633
No log	6.4516	200	0.6038	0.5912	0.6038	0.7770
No log	6.5161	202	0.5807	0.6209	0.5807	0.7621
No log	6.5806	204	0.5786	0.6209	0.5786	0.7606
No log	6.6452	206	0.5583	0.6174	0.5583	0.7472
No log	6.7097	208	0.5211	0.6688	0.5211	0.7219
No log	6.7742	210	0.4961	0.7508	0.4961	0.7043
No log	6.8387	212	0.4824	0.7651	0.4824	0.6946
No log	6.9032	214	0.4756	0.7508	0.4756	0.6896
No log	6.9677	216	0.5049	0.7	0.5049	0.7106
No log	7.0323	218	0.6035	0.5532	0.6035	0.7769
No log	7.0968	220	0.7030	0.5532	0.7030	0.8384
No log	7.1613	222	0.7300	0.4981	0.7300	0.8544
No log	7.2258	224	0.6852	0.5532	0.6852	0.8278
No log	7.2903	226	0.6102	0.5532	0.6102	0.7812
No log	7.3548	228	0.5243	0.7	0.5243	0.7241
No log	7.4194	230	0.4842	0.7658	0.4842	0.6959
No log	7.4839	232	0.4835	0.7426	0.4835	0.6954
No log	7.5484	234	0.5067	0.7	0.5067	0.7118
No log	7.6129	236	0.5425	0.7	0.5425	0.7365
No log	7.6774	238	0.5599	0.7	0.5599	0.7483
No log	7.7419	240	0.5716	0.5912	0.5716	0.7560
No log	7.8065	242	0.6017	0.5825	0.6017	0.7757
No log	7.8710	244	0.5998	0.5825	0.5998	0.7745
No log	7.9355	246	0.5631	0.6715	0.5631	0.7504
No log	8.0	248	0.5152	0.7	0.5152	0.7178
No log	8.0645	250	0.4904	0.6797	0.4904	0.7003
No log	8.1290	252	0.4877	0.7390	0.4877	0.6983
No log	8.1935	254	0.4941	0.7390	0.4941	0.7029
No log	8.2581	256	0.4994	0.7390	0.4994	0.7067
No log	8.3226	258	0.5147	0.6688	0.5147	0.7174
No log	8.3871	260	0.5394	0.7407	0.5394	0.7345
No log	8.4516	262	0.5513	0.7407	0.5513	0.7425
No log	8.5161	264	0.5611	0.7016	0.5611	0.7490
No log	8.5806	266	0.5731	0.6291	0.5731	0.7570
No log	8.6452	268	0.5808	0.5825	0.5808	0.7621
No log	8.7097	270	0.5759	0.5825	0.5759	0.7589
No log	8.7742	272	0.5832	0.5825	0.5832	0.7636
No log	8.8387	274	0.5779	0.5825	0.5779	0.7602
No log	8.9032	276	0.5535	0.5912	0.5535	0.7439
No log	8.9677	278	0.5295	0.7407	0.5295	0.7277
No log	9.0323	280	0.5114	0.7407	0.5114	0.7151
No log	9.0968	282	0.4992	0.7336	0.4992	0.7066
No log	9.1613	284	0.4920	0.7986	0.4920	0.7014
No log	9.2258	286	0.4894	0.7986	0.4894	0.6996
No log	9.2903	288	0.4935	0.7986	0.4935	0.7025
No log	9.3548	290	0.5020	0.7407	0.5020	0.7085
No log	9.4194	292	0.5103	0.7407	0.5103	0.7144
No log	9.4839	294	0.5211	0.7407	0.5211	0.7219
No log	9.5484	296	0.5379	0.6667	0.5379	0.7334
No log	9.6129	298	0.5538	0.6209	0.5538	0.7442
No log	9.6774	300	0.5695	0.5912	0.5695	0.7547
No log	9.7419	302	0.5811	0.5825	0.5811	0.7623
No log	9.8065	304	0.5879	0.5825	0.5879	0.7668
No log	9.8710	306	0.5893	0.5825	0.5893	0.7676
No log	9.9355	308	0.5895	0.5825	0.5895	0.7678
No log	10.0	310	0.5892	0.5825	0.5892	0.7676

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 28, 2024
Updated: Nov 28, 2024