Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold0

Arabic_FineTuningAraBERT_AugV5_k2_task3_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 25, 2024
Updated: Nov 25, 2024
SHA: 584656f0

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV5_k2_task3_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: 1.2888
Qwk: -0.1333
Mse: 1.2888
Rmse: 1.1352

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	0.1333	2	4.6034	-0.0034	4.6034	2.1456
No log	0.2667	4	3.1650	-0.0288	3.1650	1.7790
No log	0.4	6	1.5900	0.0	1.5900	1.2609
No log	0.5333	8	1.1293	0.0530	1.1293	1.0627
No log	0.6667	10	0.9623	0.0	0.9623	0.9809
No log	0.8	12	0.9528	0.0	0.9528	0.9761
No log	0.9333	14	0.9400	0.0	0.9400	0.9695
No log	1.0667	16	0.9775	0.0	0.9775	0.9887
No log	1.2	18	1.0455	-0.2754	1.0455	1.0225
No log	1.3333	20	0.9792	0.2143	0.9792	0.9896
No log	1.4667	22	1.0738	-0.0927	1.0738	1.0363
No log	1.6	24	1.2772	0.0833	1.2772	1.1301
No log	1.7333	26	1.1939	-0.0565	1.1939	1.0927
No log	1.8667	28	0.9685	-0.1440	0.9685	0.9841
No log	2.0	30	0.9344	0.0	0.9344	0.9666
No log	2.1333	32	0.9963	-0.1440	0.9963	0.9982
No log	2.2667	34	1.0763	-0.0732	1.0763	1.0374
No log	2.4	36	1.0958	-0.0732	1.0958	1.0468
No log	2.5333	38	0.9979	0.0530	0.9979	0.9989
No log	2.6667	40	0.8649	0.0	0.8649	0.9300
No log	2.8	42	0.8263	0.0	0.8263	0.9090
No log	2.9333	44	0.8095	0.0	0.8095	0.8997
No log	3.0667	46	0.8429	0.2143	0.8429	0.9181
No log	3.2	48	0.9163	0.0435	0.9163	0.9572
No log	3.3333	50	0.9014	0.384	0.9014	0.9494
No log	3.4667	52	0.8661	0.2143	0.8661	0.9306
No log	3.6	54	0.8938	0.2143	0.8938	0.9454
No log	3.7333	56	0.9186	0.2143	0.9186	0.9585
No log	3.8667	58	0.9358	0.2143	0.9358	0.9674
No log	4.0	60	0.9884	0.0435	0.9884	0.9942
No log	4.1333	62	0.9890	0.0435	0.9890	0.9945
No log	4.2667	64	0.9106	0.3231	0.9106	0.9543
No log	4.4	66	0.8998	0.2878	0.8998	0.9486
No log	4.5333	68	0.9050	0.2878	0.9050	0.9513
No log	4.6667	70	0.9034	0.2878	0.9034	0.9505
No log	4.8	72	0.9328	0.2878	0.9328	0.9658
No log	4.9333	74	1.0668	-0.1493	1.0668	1.0329
No log	5.0667	76	1.2228	-0.0809	1.2228	1.1058
No log	5.2	78	1.3336	-0.0565	1.3336	1.1548
No log	5.3333	80	1.2258	-0.0565	1.2258	1.1072
No log	5.4667	82	1.0475	0.0320	1.0475	1.0235
No log	5.6	84	1.0220	-0.0185	1.0220	1.0109
No log	5.7333	86	1.0330	-0.1846	1.0330	1.0163
No log	5.8667	88	1.1315	0.0149	1.1315	1.0637
No log	6.0	90	1.3322	-0.0565	1.3322	1.1542
No log	6.1333	92	1.4517	-0.0565	1.4517	1.2049
No log	6.2667	94	1.3978	-0.0565	1.3978	1.1823
No log	6.4	96	1.2382	-0.0565	1.2382	1.1127
No log	6.5333	98	1.1394	0.1316	1.1394	1.0674
No log	6.6667	100	1.1721	-0.1911	1.1721	1.0826
No log	6.8	102	1.2001	-0.0694	1.2001	1.0955
No log	6.9333	104	1.1749	-0.1911	1.1749	1.0839
No log	7.0667	106	1.1435	-0.0405	1.1435	1.0694
No log	7.2	108	1.1879	-0.1282	1.1879	1.0899
No log	7.3333	110	1.3246	-0.0565	1.3246	1.1509
No log	7.4667	112	1.4171	-0.0565	1.4171	1.1904
No log	7.6	114	1.4157	-0.0565	1.4157	1.1898
No log	7.7333	116	1.3329	-0.0565	1.3329	1.1545
No log	7.8667	118	1.2313	-0.1282	1.2313	1.1096
No log	8.0	120	1.1864	0.1316	1.1864	1.0892
No log	8.1333	122	1.1853	-0.0353	1.1853	1.0887
No log	8.2667	124	1.1954	-0.0353	1.1954	1.0933
No log	8.4	126	1.2121	-0.0353	1.2121	1.1010
No log	8.5333	128	1.2386	-0.1333	1.2386	1.1129
No log	8.6667	130	1.2691	-0.1333	1.2691	1.1266
No log	8.8	132	1.3016	-0.1282	1.3016	1.1409
No log	8.9333	134	1.3149	-0.1282	1.3149	1.1467
No log	9.0667	136	1.3089	-0.1282	1.3089	1.1441
No log	9.2	138	1.3043	-0.1282	1.3043	1.1421
No log	9.3333	140	1.2942	-0.1282	1.2942	1.1376
No log	9.4667	142	1.2883	-0.1333	1.2883	1.1351
No log	9.6	144	1.2852	-0.1333	1.2852	1.1337
No log	9.7333	146	1.2854	-0.1333	1.2854	1.1338
No log	9.8667	148	1.2877	-0.1333	1.2877	1.1348
No log	10.0	150	1.2888	-0.1333	1.2888	1.1352

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 25, 2024
Updated: Nov 25, 2024