ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task3_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task3_organization

ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task3_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 9, 2024
Updated: Dec 9, 2024
SHA: 4e0390b4

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task3_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: 0.7366
Qwk: 0.2258
Mse: 0.7366
Rmse: 0.8583

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	3.3125	-0.0149	3.3125	1.8200
No log	0.2667	4	1.9351	0.0061	1.9351	1.3911
No log	0.4	6	1.7019	0.0264	1.7019	1.3046
No log	0.5333	8	0.7890	0.2919	0.7890	0.8883
No log	0.6667	10	0.6066	0.0569	0.6066	0.7788
No log	0.8	12	0.6314	0.0569	0.6314	0.7946
No log	0.9333	14	0.5628	0.1008	0.5628	0.7502
No log	1.0667	16	0.7065	0.2169	0.7065	0.8406
No log	1.2	18	0.7560	0.1174	0.7560	0.8695
No log	1.3333	20	0.6307	-0.0909	0.6307	0.7941
No log	1.4667	22	0.5846	0.0	0.5846	0.7646
No log	1.6	24	0.5843	0.0	0.5843	0.7644
No log	1.7333	26	0.6196	0.0	0.6196	0.7871
No log	1.8667	28	0.6751	0.0	0.6751	0.8216
No log	2.0	30	0.6603	-0.0732	0.6603	0.8126
No log	2.1333	32	0.7019	-0.0370	0.7019	0.8378
No log	2.2667	34	0.8546	0.0270	0.8546	0.9245
No log	2.4	36	0.7283	-0.0273	0.7283	0.8534
No log	2.5333	38	0.6438	-0.0732	0.6438	0.8023
No log	2.6667	40	0.6511	-0.0732	0.6511	0.8069
No log	2.8	42	0.6380	0.0	0.6380	0.7988
No log	2.9333	44	0.6208	0.0303	0.6208	0.7879
No log	3.0667	46	0.7743	0.1289	0.7743	0.8799
No log	3.2	48	0.7432	0.1481	0.7432	0.8621
No log	3.3333	50	0.6277	-0.0496	0.6277	0.7923
No log	3.4667	52	0.6050	0.0388	0.6050	0.7778
No log	3.6	54	0.6112	0.0303	0.6112	0.7818
No log	3.7333	56	0.6706	0.0256	0.6706	0.8189
No log	3.8667	58	0.6693	0.0327	0.6693	0.8181
No log	4.0	60	0.6735	0.0388	0.6735	0.8207
No log	4.1333	62	0.6964	0.0388	0.6964	0.8345
No log	4.2667	64	0.6809	0.0145	0.6809	0.8252
No log	4.4	66	0.7670	0.1414	0.7670	0.8758
No log	4.5333	68	0.7116	0.0061	0.7116	0.8436
No log	4.6667	70	0.6977	0.0725	0.6977	0.8353
No log	4.8	72	0.6994	0.0815	0.6994	0.8363
No log	4.9333	74	0.6914	0.0	0.6914	0.8315
No log	5.0667	76	0.7361	0.0	0.7361	0.8580
No log	5.2	78	0.7252	0.0	0.7252	0.8516
No log	5.3333	80	0.7122	0.1448	0.7122	0.8439
No log	5.4667	82	0.7748	0.2090	0.7748	0.8803
No log	5.6	84	0.7441	0.2096	0.7441	0.8626
No log	5.7333	86	0.7290	0.0698	0.7290	0.8538
No log	5.8667	88	0.7480	0.1020	0.7480	0.8649
No log	6.0	90	0.7280	0.1111	0.7280	0.8532
No log	6.1333	92	0.8219	0.2075	0.8219	0.9066
No log	6.2667	94	0.8608	0.1712	0.8608	0.9278
No log	6.4	96	0.7315	0.1475	0.7315	0.8553
No log	6.5333	98	0.7442	0.1456	0.7442	0.8627
No log	6.6667	100	0.8536	0.2489	0.8536	0.9239
No log	6.8	102	0.7666	0.1776	0.7666	0.8755
No log	6.9333	104	0.7007	0.125	0.7007	0.8371
No log	7.0667	106	0.8452	0.1776	0.8452	0.9194
No log	7.2	108	1.0990	-0.0107	1.0990	1.0483
No log	7.3333	110	1.1299	-0.0070	1.1299	1.0630
No log	7.4667	112	1.0174	0.0303	1.0174	1.0086
No log	7.6	114	0.8466	0.1287	0.8466	0.9201
No log	7.7333	116	0.7818	0.2340	0.7818	0.8842
No log	7.8667	118	0.7604	0.1675	0.7604	0.8720
No log	8.0	120	0.7759	0.2432	0.7759	0.8808
No log	8.1333	122	0.7747	0.2432	0.7747	0.8802
No log	8.2667	124	0.7522	0.1527	0.7522	0.8673
No log	8.4	126	0.7440	0.0995	0.7440	0.8625
No log	8.5333	128	0.7566	0.2432	0.7566	0.8698
No log	8.6667	130	0.7687	0.2432	0.7687	0.8768
No log	8.8	132	0.7636	0.2432	0.7636	0.8738
No log	8.9333	134	0.7861	0.1658	0.7861	0.8866
No log	9.0667	136	0.8225	0.1675	0.8225	0.9069
No log	9.2	138	0.8165	0.1675	0.8165	0.9036
No log	9.3333	140	0.8011	0.1675	0.8011	0.8950
No log	9.4667	142	0.7792	0.2088	0.7792	0.8827
No log	9.6	144	0.7573	0.2432	0.7573	0.8702
No log	9.7333	146	0.7423	0.2258	0.7423	0.8616
No log	9.8667	148	0.7372	0.2258	0.7372	0.8586
No log	10.0	150	0.7366	0.2258	0.7366	0.8583

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 9, 2024
Updated: Dec 9, 2024