ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task1_organization_fold0

MayBashendy/ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task1_organization_fold0

ArabicNewSplits_FineTuningAraBERT_AugV5_k5_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 26, 2024
Updated: Nov 26, 2024
SHA: b3e89b18

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits_FineTuningAraBERT_AugV5_k5_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: 1.4588
Qwk: 0.3967
Mse: 1.4588
Rmse: 1.2078

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	0.0769	2	4.3431	-0.0044	4.3431	2.0840
No log	0.1538	4	2.4609	0.1622	2.4609	1.5687
No log	0.2308	6	1.4543	0.2085	1.4543	1.2060
No log	0.3077	8	1.4677	0.0200	1.4677	1.2115
No log	0.3846	10	1.3222	0.0227	1.3222	1.1499
No log	0.4615	12	1.3786	0.0227	1.3786	1.1741
No log	0.5385	14	1.1858	0.0593	1.1858	1.0890
No log	0.6154	16	1.1640	0.0337	1.1640	1.0789
No log	0.6923	18	1.2038	0.0	1.2038	1.0972
No log	0.7692	20	1.1384	0.0	1.1384	1.0670
No log	0.8462	22	1.0994	0.0500	1.0994	1.0485
No log	0.9231	24	1.0641	0.1363	1.0641	1.0316
No log	1.0	26	1.0194	0.2818	1.0194	1.0096
No log	1.0769	28	0.9996	0.2818	0.9996	0.9998
No log	1.1538	30	0.9137	0.3446	0.9137	0.9559
No log	1.2308	32	0.8389	0.3850	0.8389	0.9159
No log	1.3077	34	0.8528	0.3665	0.8528	0.9235
No log	1.3846	36	0.8261	0.3790	0.8261	0.9089
No log	1.4615	38	0.8666	0.3414	0.8666	0.9309
No log	1.5385	40	1.1503	0.1212	1.1503	1.0725
No log	1.6154	42	1.3120	0.0660	1.3120	1.1454
No log	1.6923	44	1.2948	0.0	1.2948	1.1379
No log	1.7692	46	1.2166	0.0	1.2166	1.1030
No log	1.8462	48	1.0612	0.2073	1.0612	1.0302
No log	1.9231	50	0.8878	0.3430	0.8878	0.9422
No log	2.0	52	0.7592	0.4110	0.7592	0.8713
No log	2.0769	54	0.8418	0.4394	0.8418	0.9175
No log	2.1538	56	0.9741	0.3994	0.9741	0.9869
No log	2.2308	58	0.9352	0.4244	0.9352	0.9671
No log	2.3077	60	0.8025	0.5397	0.8025	0.8958
No log	2.3846	62	0.6702	0.6095	0.6702	0.8187
No log	2.4615	64	0.6352	0.5242	0.6352	0.7970
No log	2.5385	66	0.6685	0.4992	0.6685	0.8176
No log	2.6154	68	0.7147	0.5092	0.7147	0.8454
No log	2.6923	70	0.6667	0.5092	0.6667	0.8165
No log	2.7692	72	0.5946	0.5016	0.5946	0.7711
No log	2.8462	74	0.6019	0.6105	0.6019	0.7758
No log	2.9231	76	0.6823	0.5989	0.6823	0.8260
No log	3.0	78	0.8454	0.5319	0.8454	0.9195
No log	3.0769	80	1.0975	0.4479	1.0975	1.0476
No log	3.1538	82	1.2929	0.3735	1.2929	1.1371
No log	3.2308	84	1.3572	0.3787	1.3572	1.1650
No log	3.3077	86	1.3102	0.4119	1.3102	1.1447
No log	3.3846	88	1.1159	0.4607	1.1159	1.0564
No log	3.4615	90	1.0535	0.4930	1.0535	1.0264
No log	3.5385	92	1.0559	0.4685	1.0559	1.0276
No log	3.6154	94	1.0935	0.4781	1.0935	1.0457
No log	3.6923	96	0.9823	0.4644	0.9823	0.9911
No log	3.7692	98	0.8908	0.4173	0.8908	0.9438
No log	3.8462	100	0.9290	0.4184	0.9290	0.9639
No log	3.9231	102	0.9968	0.4211	0.9968	0.9984
No log	4.0	104	1.2528	0.4446	1.2528	1.1193
No log	4.0769	106	1.3405	0.4482	1.3405	1.1578
No log	4.1538	108	1.4517	0.4482	1.4517	1.2049
No log	4.2308	110	1.4587	0.4352	1.4587	1.2078
No log	4.3077	112	1.2445	0.4608	1.2445	1.1156
No log	4.3846	114	1.1834	0.4677	1.1834	1.0879
No log	4.4615	116	1.2592	0.4331	1.2592	1.1221
No log	4.5385	118	1.2324	0.4411	1.2324	1.1101
No log	4.6154	120	1.1940	0.4411	1.1940	1.0927
No log	4.6923	122	1.1901	0.4357	1.1901	1.0909
No log	4.7692	124	1.1844	0.4344	1.1844	1.0883
No log	4.8462	126	1.1749	0.4120	1.1749	1.0839
No log	4.9231	128	1.1557	0.4331	1.1557	1.0750
No log	5.0	130	1.0887	0.4610	1.0887	1.0434
No log	5.0769	132	1.0170	0.4592	1.0170	1.0084
No log	5.1538	134	0.9085	0.5163	0.9085	0.9531
No log	5.2308	136	0.8477	0.5316	0.8477	0.9207
No log	5.3077	138	0.8795	0.5	0.8795	0.9378
No log	5.3846	140	0.9697	0.4924	0.9697	0.9847
No log	5.4615	142	1.1080	0.3945	1.1080	1.0526
No log	5.5385	144	1.0219	0.4509	1.0219	1.0109
No log	5.6154	146	1.0212	0.4431	1.0212	1.0105
No log	5.6923	148	1.1804	0.4028	1.1804	1.0865
No log	5.7692	150	1.5146	0.3757	1.5146	1.2307
No log	5.8462	152	1.5224	0.3757	1.5224	1.2338
No log	5.9231	154	1.3467	0.3880	1.3467	1.1605
No log	6.0	156	1.1132	0.4111	1.1132	1.0551
No log	6.0769	158	1.0173	0.5080	1.0173	1.0086
No log	6.1538	160	1.0087	0.4932	1.0087	1.0043
No log	6.2308	162	1.1296	0.4419	1.1296	1.0628
No log	6.3077	164	1.3873	0.3861	1.3873	1.1779
No log	6.3846	166	1.7733	0.3628	1.7733	1.3317
No log	6.4615	168	2.1395	0.3041	2.1395	1.4627
No log	6.5385	170	2.2303	0.2824	2.2303	1.4934
No log	6.6154	172	2.0644	0.3204	2.0644	1.4368
No log	6.6923	174	1.7200	0.3505	1.7200	1.3115
No log	6.7692	176	1.3530	0.3894	1.3530	1.1632
No log	6.8462	178	1.1862	0.3995	1.1862	1.0891
No log	6.9231	180	1.1422	0.4061	1.1422	1.0687
No log	7.0	182	1.1721	0.4061	1.1721	1.0826
No log	7.0769	184	1.1608	0.4269	1.1608	1.0774
No log	7.1538	186	1.2451	0.3670	1.2451	1.1159
No log	7.2308	188	1.2542	0.3693	1.2542	1.1199
No log	7.3077	190	1.2561	0.3693	1.2561	1.1208
No log	7.3846	192	1.3482	0.3693	1.3482	1.1611
No log	7.4615	194	1.3964	0.3693	1.3964	1.1817
No log	7.5385	196	1.3850	0.3693	1.3850	1.1769
No log	7.6154	198	1.3741	0.3693	1.3741	1.1722
No log	7.6923	200	1.2965	0.3693	1.2965	1.1386
No log	7.7692	202	1.2335	0.4153	1.2335	1.1106
No log	7.8462	204	1.1811	0.4552	1.1811	1.0868
No log	7.9231	206	1.1603	0.4619	1.1603	1.0772
No log	8.0	208	1.2103	0.4153	1.2103	1.1001
No log	8.0769	210	1.3009	0.4153	1.3009	1.1406
No log	8.1538	212	1.3681	0.3948	1.3681	1.1696
No log	8.2308	214	1.4368	0.3693	1.4368	1.1987
No log	8.3077	216	1.5442	0.3541	1.5442	1.2426
No log	8.3846	218	1.5799	0.3541	1.5799	1.2569
No log	8.4615	220	1.6007	0.3458	1.6007	1.2652
No log	8.5385	222	1.5676	0.3541	1.5676	1.2520
No log	8.6154	224	1.4945	0.3715	1.4945	1.2225
No log	8.6923	226	1.4510	0.3715	1.4510	1.2046
No log	8.7692	228	1.3994	0.3967	1.3994	1.1829
No log	8.8462	230	1.3958	0.3967	1.3958	1.1814
No log	8.9231	232	1.3922	0.3967	1.3922	1.1799
No log	9.0	234	1.3582	0.4153	1.3582	1.1654
No log	9.0769	236	1.3700	0.3948	1.3700	1.1705
No log	9.1538	238	1.3700	0.3948	1.3700	1.1705
No log	9.2308	240	1.3678	0.3948	1.3678	1.1695
No log	9.3077	242	1.3568	0.3948	1.3568	1.1648
No log	9.3846	244	1.3700	0.3948	1.3700	1.1705
No log	9.4615	246	1.3886	0.3948	1.3886	1.1784
No log	9.5385	248	1.4175	0.3948	1.4175	1.1906
No log	9.6154	250	1.4386	0.3948	1.4386	1.1994
No log	9.6923	252	1.4510	0.3948	1.4510	1.2046
No log	9.7692	254	1.4549	0.3948	1.4549	1.2062
No log	9.8462	256	1.4534	0.3948	1.4534	1.2056
No log	9.9231	258	1.4562	0.3967	1.4562	1.2067
No log	10.0	260	1.4588	0.3967	1.4588	1.2078

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 26, 2024
Updated: Nov 26, 2024