ArabicNewSplits2_FineTuningAraBERT_run1_AugV5_k3_task1_organization

MayBashendy/ArabicNewSplits2_FineTuningAraBERT_run1_AugV5_k3_task1_organization

ArabicNewSplits2_FineTuningAraBERT_run1_AugV5_k3_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. --

Downloads · 30 days

11% of all-time downloads

All-time downloads

Public

Parameters

135M

541 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors541 MB · 100%

At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 1, 2024
Updated: Dec 1, 2024
SHA: cbd2c33e

Try a prompt

Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits2_FineTuningAraBERT_run1_AugV5_k3_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: 0.5206
Qwk: 0.7462
Mse: 0.5206
Rmse: 0.7215

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	0.125	2	4.9509	-0.0722	4.9509	2.2251
No log	0.25	4	3.6278	0.0258	3.6278	1.9047
No log	0.375	6	2.3567	0.0774	2.3567	1.5351
No log	0.5	8	1.5159	0.0185	1.5159	1.2312
No log	0.625	10	1.4473	0.0118	1.4473	1.2031
No log	0.75	12	1.2341	0.2721	1.2341	1.1109
No log	0.875	14	1.1312	0.3333	1.1312	1.0636
No log	1.0	16	1.0618	0.3182	1.0618	1.0304
No log	1.125	18	1.0324	0.3717	1.0324	1.0161
No log	1.25	20	1.0363	0.4276	1.0363	1.0180
No log	1.375	22	1.4574	0.2795	1.4574	1.2072
No log	1.5	24	1.8624	0.2059	1.8624	1.3647
No log	1.625	26	2.0561	0.2133	2.0561	1.4339
No log	1.75	28	1.8370	0.2430	1.8370	1.3554
No log	1.875	30	1.1834	0.3309	1.1834	1.0879
No log	2.0	32	0.7784	0.4706	0.7784	0.8822
No log	2.125	34	0.6961	0.4667	0.6961	0.8343
No log	2.25	36	0.6602	0.4814	0.6602	0.8125
No log	2.375	38	0.6252	0.5453	0.6252	0.7907
No log	2.5	40	0.6750	0.6383	0.6750	0.8216
No log	2.625	42	0.6955	0.6636	0.6955	0.8340
No log	2.75	44	0.6651	0.6305	0.6651	0.8155
No log	2.875	46	0.5465	0.7455	0.5465	0.7393
No log	3.0	48	0.5578	0.7008	0.5578	0.7469
No log	3.125	50	0.7586	0.4393	0.7586	0.8710
No log	3.25	52	0.7009	0.5032	0.7009	0.8372
No log	3.375	54	0.6113	0.6923	0.6113	0.7819
No log	3.5	56	0.6435	0.7163	0.6435	0.8022
No log	3.625	58	0.6117	0.6849	0.6117	0.7821
No log	3.75	60	0.5999	0.7531	0.5999	0.7745
No log	3.875	62	0.6140	0.7649	0.6140	0.7836
No log	4.0	64	0.6070	0.7841	0.6070	0.7791
No log	4.125	66	0.6827	0.64	0.6827	0.8262
No log	4.25	68	0.6405	0.7188	0.6405	0.8003
No log	4.375	70	0.6139	0.7188	0.6139	0.7835
No log	4.5	72	0.6253	0.6488	0.6253	0.7907
No log	4.625	74	0.6548	0.5884	0.6548	0.8092
No log	4.75	76	0.5962	0.6701	0.5962	0.7721
No log	4.875	78	0.5506	0.7202	0.5506	0.7421
No log	5.0	80	0.5512	0.7373	0.5512	0.7424
No log	5.125	82	0.5593	0.7090	0.5593	0.7479
No log	5.25	84	0.5261	0.7667	0.5261	0.7253
No log	5.375	86	0.5146	0.7667	0.5146	0.7174
No log	5.5	88	0.5107	0.7678	0.5107	0.7147
No log	5.625	90	0.5169	0.7667	0.5169	0.7190
No log	5.75	92	0.5790	0.7269	0.5790	0.7609
No log	5.875	94	0.6319	0.7037	0.6319	0.7950
No log	6.0	96	0.5811	0.7432	0.5811	0.7623
No log	6.125	98	0.5573	0.7432	0.5573	0.7465
No log	6.25	100	0.5043	0.7997	0.5043	0.7102
No log	6.375	102	0.5305	0.7382	0.5305	0.7284
No log	6.5	104	0.5782	0.7233	0.5782	0.7604
No log	6.625	106	0.5668	0.7331	0.5668	0.7529
No log	6.75	108	0.5068	0.7737	0.5068	0.7119
No log	6.875	110	0.5243	0.7680	0.5243	0.7241
No log	7.0	112	0.5412	0.7550	0.5412	0.7357
No log	7.125	114	0.5189	0.7667	0.5189	0.7204
No log	7.25	116	0.4966	0.7571	0.4966	0.7047
No log	7.375	118	0.5036	0.7667	0.5036	0.7096
No log	7.5	120	0.5225	0.7550	0.5225	0.7228
No log	7.625	122	0.5592	0.7550	0.5592	0.7478
No log	7.75	124	0.5941	0.7432	0.5941	0.7708
No log	7.875	126	0.5950	0.7432	0.5950	0.7714
No log	8.0	128	0.5514	0.7550	0.5514	0.7425
No log	8.125	130	0.5178	0.7418	0.5178	0.7196
No log	8.25	132	0.5251	0.7486	0.5251	0.7246
No log	8.375	134	0.5399	0.725	0.5399	0.7348
No log	8.5	136	0.5354	0.7486	0.5354	0.7317
No log	8.625	138	0.5281	0.7486	0.5281	0.7267
No log	8.75	140	0.5288	0.7418	0.5288	0.7272
No log	8.875	142	0.5320	0.7418	0.5320	0.7294
No log	9.0	144	0.5282	0.7418	0.5282	0.7267
No log	9.125	146	0.5233	0.7418	0.5233	0.7234
No log	9.25	148	0.5202	0.7486	0.5202	0.7213
No log	9.375	150	0.5193	0.7664	0.5193	0.7206
No log	9.5	152	0.5195	0.7594	0.5195	0.7208
No log	9.625	154	0.5198	0.7462	0.5198	0.7210
No log	9.75	156	0.5201	0.7462	0.5201	0.7211
No log	9.875	158	0.5203	0.7462	0.5203	0.7213
No log	10.0	160	0.5206	0.7462	0.5206	0.7215

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 1, 2024
Updated: Dec 1, 2024