ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k5_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k5_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k5_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: 86e25a27

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k5_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: 0.6794
Qwk: 0.6990
Mse: 0.6794
Rmse: 0.8243

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	5.4275	-0.0165	5.4275	2.3297
No log	0.1538	4	3.9220	0.0284	3.9220	1.9804
No log	0.2308	6	2.3034	0.1699	2.3034	1.5177
No log	0.3077	8	1.9941	0.0046	1.9941	1.4121
No log	0.3846	10	1.6000	0.0703	1.6000	1.2649
No log	0.4615	12	1.1909	0.2765	1.1909	1.0913
No log	0.5385	14	1.1975	0.3043	1.1975	1.0943
No log	0.6154	16	1.1927	0.2043	1.1927	1.0921
No log	0.6923	18	1.1842	0.1435	1.1842	1.0882
No log	0.7692	20	1.1840	0.1000	1.1840	1.0881
No log	0.8462	22	1.1826	0.1496	1.1826	1.0875
No log	0.9231	24	1.1524	0.2665	1.1524	1.0735
No log	1.0	26	1.1319	0.3408	1.1319	1.0639
No log	1.0769	28	1.1050	0.4116	1.1050	1.0512
No log	1.1538	30	1.1660	0.3559	1.1660	1.0798
No log	1.2308	32	1.1831	0.3412	1.1831	1.0877
No log	1.3077	34	1.1773	0.3364	1.1773	1.0850
No log	1.3846	36	1.0664	0.4079	1.0664	1.0327
No log	1.4615	38	1.0050	0.4213	1.0050	1.0025
No log	1.5385	40	1.1320	0.3678	1.1320	1.0640
No log	1.6154	42	1.1573	0.3698	1.1573	1.0758
No log	1.6923	44	0.9556	0.4586	0.9556	0.9775
No log	1.7692	46	0.8838	0.4450	0.8838	0.9401
No log	1.8462	48	0.8343	0.4787	0.8343	0.9134
No log	1.9231	50	0.8910	0.4619	0.8910	0.9439
No log	2.0	52	0.9931	0.4595	0.9931	0.9965
No log	2.0769	54	0.9518	0.4642	0.9518	0.9756
No log	2.1538	56	0.7498	0.5968	0.7498	0.8659
No log	2.2308	58	0.7449	0.5656	0.7449	0.8631
No log	2.3077	60	0.8393	0.5053	0.8393	0.9161
No log	2.3846	62	0.8024	0.4716	0.8024	0.8958
No log	2.4615	64	0.6918	0.6041	0.6918	0.8317
No log	2.5385	66	0.7300	0.5765	0.7300	0.8544
No log	2.6154	68	0.8532	0.5521	0.8532	0.9237
No log	2.6923	70	1.0396	0.6180	1.0396	1.0196
No log	2.7692	72	0.9305	0.6284	0.9305	0.9646
No log	2.8462	74	0.7451	0.6673	0.7451	0.8632
No log	2.9231	76	0.6922	0.6982	0.6922	0.8320
No log	3.0	78	0.6103	0.7192	0.6103	0.7812
No log	3.0769	80	0.6327	0.7049	0.6327	0.7954
No log	3.1538	82	0.6451	0.7035	0.6451	0.8032
No log	3.2308	84	0.6972	0.6074	0.6972	0.8350
No log	3.3077	86	0.7062	0.6115	0.7062	0.8404
No log	3.3846	88	0.6813	0.6859	0.6813	0.8254
No log	3.4615	90	0.6889	0.6911	0.6889	0.8300
No log	3.5385	92	0.7748	0.6900	0.7748	0.8802
No log	3.6154	94	0.8352	0.6403	0.8352	0.9139
No log	3.6923	96	0.7524	0.6877	0.7524	0.8674
No log	3.7692	98	0.7007	0.7015	0.7007	0.8371
No log	3.8462	100	0.7332	0.7002	0.7332	0.8562
No log	3.9231	102	0.7699	0.7085	0.7699	0.8774
No log	4.0	104	0.7681	0.6860	0.7681	0.8764
No log	4.0769	106	0.7048	0.6925	0.7048	0.8395
No log	4.1538	108	0.7769	0.6507	0.7769	0.8814
No log	4.2308	110	0.9657	0.5654	0.9657	0.9827
No log	4.3077	112	0.9888	0.5679	0.9888	0.9944
No log	4.3846	114	0.8467	0.6390	0.8467	0.9202
No log	4.4615	116	0.7051	0.6959	0.7051	0.8397
No log	4.5385	118	0.6925	0.7131	0.6925	0.8322
No log	4.6154	120	0.7239	0.6916	0.7239	0.8508
No log	4.6923	122	0.7021	0.6896	0.7021	0.8379
No log	4.7692	124	0.6569	0.7112	0.6569	0.8105
No log	4.8462	126	0.6460	0.7215	0.6460	0.8037
No log	4.9231	128	0.6636	0.6827	0.6636	0.8146
No log	5.0	130	0.6863	0.6912	0.6863	0.8285
No log	5.0769	132	0.6677	0.6823	0.6677	0.8171
No log	5.1538	134	0.6580	0.7194	0.6580	0.8112
No log	5.2308	136	0.7043	0.7011	0.7043	0.8392
No log	5.3077	138	0.7336	0.6870	0.7336	0.8565
No log	5.3846	140	0.6933	0.6906	0.6933	0.8326
No log	5.4615	142	0.6795	0.6897	0.6795	0.8243
No log	5.5385	144	0.7717	0.6751	0.7717	0.8785
No log	5.6154	146	0.8979	0.5923	0.8979	0.9476
No log	5.6923	148	0.9217	0.5954	0.9217	0.9600
No log	5.7692	150	0.8419	0.6527	0.8419	0.9176
No log	5.8462	152	0.7404	0.6915	0.7404	0.8604
No log	5.9231	154	0.7352	0.6974	0.7352	0.8574
No log	6.0	156	0.7731	0.6824	0.7731	0.8792
No log	6.0769	158	0.7696	0.6862	0.7696	0.8773
No log	6.1538	160	0.7222	0.6835	0.7222	0.8498
No log	6.2308	162	0.6812	0.6992	0.6812	0.8253
No log	6.3077	164	0.6773	0.7251	0.6773	0.8230
No log	6.3846	166	0.7240	0.6780	0.7240	0.8509
No log	6.4615	168	0.7204	0.6780	0.7204	0.8488
No log	6.5385	170	0.6867	0.7243	0.6867	0.8287
No log	6.6154	172	0.6575	0.7175	0.6575	0.8108
No log	6.6923	174	0.6777	0.7083	0.6777	0.8232
No log	6.7692	176	0.6927	0.6835	0.6927	0.8323
No log	6.8462	178	0.6749	0.7219	0.6749	0.8215
No log	6.9231	180	0.6636	0.7152	0.6636	0.8146
No log	7.0	182	0.6744	0.6852	0.6744	0.8212
No log	7.0769	184	0.7097	0.6799	0.7097	0.8424
No log	7.1538	186	0.7247	0.6792	0.7247	0.8513
No log	7.2308	188	0.7171	0.6774	0.7171	0.8468
No log	7.3077	190	0.7020	0.6816	0.7020	0.8378
No log	7.3846	192	0.6837	0.6745	0.6837	0.8269
No log	7.4615	194	0.6832	0.7118	0.6832	0.8265
No log	7.5385	196	0.6848	0.7118	0.6848	0.8276
No log	7.6154	198	0.6846	0.7118	0.6846	0.8274
No log	7.6923	200	0.6831	0.7211	0.6831	0.8265
No log	7.7692	202	0.6778	0.7265	0.6778	0.8233
No log	7.8462	204	0.6747	0.7259	0.6747	0.8214
No log	7.9231	206	0.6689	0.7253	0.6689	0.8179
No log	8.0	208	0.6644	0.7253	0.6644	0.8151
No log	8.0769	210	0.6665	0.7211	0.6665	0.8164
No log	8.1538	212	0.6643	0.7247	0.6643	0.8150
No log	8.2308	214	0.6495	0.7322	0.6495	0.8059
No log	8.3077	216	0.6427	0.7211	0.6427	0.8017
No log	8.3846	218	0.6440	0.7238	0.6440	0.8025
No log	8.4615	220	0.6478	0.7449	0.6478	0.8049
No log	8.5385	222	0.6506	0.7449	0.6506	0.8066
No log	8.6154	224	0.6472	0.7340	0.6472	0.8045
No log	8.6923	226	0.6469	0.7152	0.6469	0.8043
No log	8.7692	228	0.6486	0.7211	0.6486	0.8053
No log	8.8462	230	0.6540	0.7211	0.6540	0.8087
No log	8.9231	232	0.6613	0.7211	0.6613	0.8132
No log	9.0	234	0.6680	0.7211	0.6680	0.8173
No log	9.0769	236	0.6776	0.7007	0.6776	0.8231
No log	9.1538	238	0.6917	0.6741	0.6917	0.8317
No log	9.2308	240	0.7012	0.6762	0.7012	0.8374
No log	9.3077	242	0.7046	0.6823	0.7046	0.8394
No log	9.3846	244	0.7014	0.6762	0.7014	0.8375
No log	9.4615	246	0.6942	0.6762	0.6942	0.8332
No log	9.5385	248	0.6898	0.6783	0.6898	0.8306
No log	9.6154	250	0.6865	0.6773	0.6865	0.8285
No log	9.6923	252	0.6833	0.6970	0.6833	0.8266
No log	9.7692	254	0.6808	0.6971	0.6808	0.8251
No log	9.8462	256	0.6796	0.6990	0.6796	0.8244
No log	9.9231	258	0.6793	0.6990	0.6793	0.8242
No log	10.0	260	0.6794	0.6990	0.6794	0.8243

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