ArabicNewSplits_FineTuningAraBERT_AugV5_k4_task2_organization_fold0

MayBashendy/ArabicNewSplits_FineTuningAraBERT_AugV5_k4_task2_organization_fold0

ArabicNewSplits_FineTuningAraBERT_AugV5_k4_task2_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: d1f6adb7

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits_FineTuningAraBERT_AugV5_k4_task2_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: 0.8829
Qwk: 0.4460
Mse: 0.8829
Rmse: 0.9396

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	0.1429	2	3.5901	0.0019	3.5901	1.8948
No log	0.2857	4	1.8554	0.0195	1.8554	1.3621
No log	0.4286	6	1.1036	0.1525	1.1036	1.0505
No log	0.5714	8	0.8985	0.0290	0.8985	0.9479
No log	0.7143	10	0.7913	0.0983	0.7913	0.8895
No log	0.8571	12	0.8742	-0.0047	0.8742	0.9350
No log	1.0	14	1.2218	0.0	1.2218	1.1053
No log	1.1429	16	1.6738	-0.0305	1.6738	1.2937
No log	1.2857	18	1.6681	-0.0305	1.6681	1.2916
No log	1.4286	20	1.3812	0.0	1.3812	1.1753
No log	1.5714	22	1.0854	0.0	1.0854	1.0418
No log	1.7143	24	0.9134	-0.0486	0.9134	0.9557
No log	1.8571	26	0.7955	0.1418	0.7955	0.8919
No log	2.0	28	0.8573	0.0600	0.8573	0.9259
No log	2.1429	30	1.0461	-0.0880	1.0461	1.0228
No log	2.2857	32	1.3789	-0.1111	1.3789	1.1743
No log	2.4286	34	1.4194	-0.1650	1.4194	1.1914
No log	2.5714	36	1.2233	0.0127	1.2233	1.1060
No log	2.7143	38	0.9529	0.0630	0.9529	0.9762
No log	2.8571	40	0.8676	0.1414	0.8676	0.9314
No log	3.0	42	0.8769	0.2528	0.8769	0.9365
No log	3.1429	44	0.8073	0.2528	0.8073	0.8985
No log	3.2857	46	0.7249	0.2720	0.7249	0.8514
No log	3.4286	48	0.6900	0.1978	0.6900	0.8307
No log	3.5714	50	0.8178	0.2597	0.8178	0.9043
No log	3.7143	52	0.8772	0.2250	0.8772	0.9366
No log	3.8571	54	0.8604	0.2239	0.8604	0.9276
No log	4.0	56	0.7539	0.2492	0.7539	0.8683
No log	4.1429	58	0.7152	0.2888	0.7152	0.8457
No log	4.2857	60	0.7440	0.2914	0.7440	0.8626
No log	4.4286	62	0.7544	0.2754	0.7544	0.8686
No log	4.5714	64	0.7519	0.3499	0.7519	0.8671
No log	4.7143	66	0.7600	0.3499	0.7600	0.8718
No log	4.8571	68	0.7671	0.1893	0.7671	0.8758
No log	5.0	70	0.8229	0.1969	0.8229	0.9071
No log	5.1429	72	0.8291	0.1969	0.8291	0.9106
No log	5.2857	74	0.7957	0.2210	0.7957	0.8920
No log	5.4286	76	0.8152	0.3854	0.8152	0.9029
No log	5.5714	78	0.8653	0.4221	0.8653	0.9302
No log	5.7143	80	0.9118	0.4337	0.9118	0.9549
No log	5.8571	82	0.9070	0.4337	0.9070	0.9524
No log	6.0	84	0.8759	0.3792	0.8759	0.9359
No log	6.1429	86	0.8460	0.3571	0.8460	0.9198
No log	6.2857	88	0.8496	0.3542	0.8496	0.9217
No log	6.4286	90	0.8534	0.3661	0.8534	0.9238
No log	6.5714	92	0.8638	0.3485	0.8638	0.9294
No log	6.7143	94	0.8910	0.4349	0.8910	0.9439
No log	6.8571	96	0.9351	0.3952	0.9351	0.9670
No log	7.0	98	0.9467	0.3847	0.9467	0.9730
No log	7.1429	100	0.9273	0.4175	0.9273	0.9630
No log	7.2857	102	0.8638	0.4343	0.8638	0.9294
No log	7.4286	104	0.8185	0.4235	0.8185	0.9047
No log	7.5714	106	0.8020	0.4461	0.8020	0.8955
No log	7.7143	108	0.8008	0.4351	0.8008	0.8949
No log	7.8571	110	0.8086	0.4454	0.8086	0.8992
No log	8.0	112	0.8046	0.4461	0.8046	0.8970
No log	8.1429	114	0.8077	0.4454	0.8077	0.8987
No log	8.2857	116	0.8085	0.4461	0.8085	0.8991
No log	8.4286	118	0.8184	0.4020	0.8184	0.9047
No log	8.5714	120	0.8408	0.4235	0.8408	0.9170
No log	8.7143	122	0.8588	0.4571	0.8588	0.9267
No log	8.8571	124	0.8713	0.4460	0.8713	0.9334
No log	9.0	126	0.8858	0.4348	0.8858	0.9412
No log	9.1429	128	0.8891	0.4348	0.8891	0.9429
No log	9.2857	130	0.8929	0.4359	0.8929	0.9449
No log	9.4286	132	0.8886	0.4359	0.8886	0.9426
No log	9.5714	134	0.8848	0.4348	0.8848	0.9407
No log	9.7143	136	0.8844	0.4348	0.8844	0.9404
No log	9.8571	138	0.8838	0.4348	0.8838	0.9401
No log	10.0	140	0.8829	0.4460	0.8829	0.9396

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