Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold0

Arabic_FineTuningAraBERT_AugV5_k3_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. --

Downloads · 30 days

19% 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: Nov 25, 2024
Updated: Nov 25, 2024
SHA: 074853c9

Try a prompt

Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV5_k3_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: 0.8382
Qwk: 0.0
Mse: 0.8382
Rmse: 0.9155

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	0.1	2	5.4054	0.0	5.4054	2.3250
No log	0.2	4	3.5002	0.0265	3.5002	1.8709
No log	0.3	6	1.5501	0.0873	1.5501	1.2450
No log	0.4	8	1.3024	0.0833	1.3024	1.1412
No log	0.5	10	1.3031	0.0833	1.3031	1.1415
No log	0.6	12	1.1850	0.0833	1.1850	1.0886
No log	0.7	14	1.0205	0.2080	1.0205	1.0102
No log	0.8	16	0.9363	0.0	0.9363	0.9676
No log	0.9	18	1.0339	0.2080	1.0339	1.0168
No log	1.0	20	0.9710	0.0	0.9710	0.9854
No log	1.1	22	0.9580	0.2143	0.9580	0.9788
No log	1.2	24	0.9752	0.3623	0.9752	0.9875
No log	1.3	26	0.8272	0.0	0.8272	0.9095
No log	1.4	28	0.7551	0.0	0.7551	0.8689
No log	1.5	30	0.7543	0.0	0.7543	0.8685
No log	1.6	32	0.7827	0.0	0.7827	0.8847
No log	1.7	34	0.9178	0.2143	0.9178	0.9580
No log	1.8	36	1.1111	0.0788	1.1111	1.0541
No log	1.9	38	1.6574	-0.0087	1.6574	1.2874
No log	2.0	40	1.9119	0.0	1.9119	1.3827
No log	2.1	42	1.8650	0.0	1.8650	1.3656
No log	2.2	44	1.6700	0.0	1.6700	1.2923
No log	2.3	46	1.4579	0.0	1.4579	1.2074
No log	2.4	48	1.3756	0.0	1.3756	1.1729
No log	2.5	50	1.2766	0.0	1.2766	1.1299
No log	2.6	52	1.1362	-0.1440	1.1362	1.0659
No log	2.7	54	1.1233	-0.1440	1.1233	1.0598
No log	2.8	56	1.0231	0.0	1.0231	1.0115
No log	2.9	58	0.9991	0.0	0.9991	0.9996
No log	3.0	60	0.9980	0.0	0.9980	0.9990
No log	3.1	62	0.9938	0.0	0.9938	0.9969
No log	3.2	64	1.0032	0.0	1.0032	1.0016
No log	3.3	66	1.0004	0.0	1.0004	1.0002
No log	3.4	68	1.0613	-0.1440	1.0613	1.0302
No log	3.5	70	1.3159	-0.0565	1.3159	1.1471
No log	3.6	72	1.4262	-0.1579	1.4262	1.1942
No log	3.7	74	1.5028	-0.1204	1.5028	1.2259
No log	3.8	76	1.3155	-0.0565	1.3155	1.1469
No log	3.9	78	1.0236	0.0	1.0236	1.0117
No log	4.0	80	0.9123	0.0	0.9123	0.9551
No log	4.1	82	0.8884	0.0	0.8884	0.9426
No log	4.2	84	0.8704	0.0	0.8704	0.9329
No log	4.3	86	0.9331	0.0	0.9331	0.9660
No log	4.4	88	1.0404	-0.375	1.0404	1.0200
No log	4.5	90	1.0975	-0.1159	1.0975	1.0476
No log	4.6	92	0.9822	0.0	0.9822	0.9911
No log	4.7	94	0.9009	0.0	0.9009	0.9492
No log	4.8	96	0.8757	0.0	0.8757	0.9358
No log	4.9	98	0.8578	0.0	0.8578	0.9262
No log	5.0	100	0.8370	0.0	0.8370	0.9149
No log	5.1	102	0.8429	0.0	0.8429	0.9181
No log	5.2	104	0.9026	0.0	0.9026	0.9501
No log	5.3	106	0.9556	0.0	0.9556	0.9776
No log	5.4	108	0.9494	0.0	0.9494	0.9744
No log	5.5	110	1.1853	-0.0927	1.1853	1.0887
No log	5.6	112	1.5673	-0.3051	1.5673	1.2519
No log	5.7	114	1.4221	-0.2073	1.4221	1.1925
No log	5.8	116	1.0250	0.2080	1.0250	1.0124
No log	5.9	118	0.8832	0.0	0.8832	0.9398
No log	6.0	120	0.8544	0.0	0.8544	0.9243
No log	6.1	122	0.8307	0.0	0.8307	0.9114
No log	6.2	124	0.8220	0.1852	0.8220	0.9067
No log	6.3	126	0.8128	0.1852	0.8128	0.9015
No log	6.4	128	0.8262	0.1852	0.8262	0.9089
No log	6.5	130	0.8261	0.1852	0.8261	0.9089
No log	6.6	132	0.8340	0.0	0.8340	0.9133
No log	6.7	134	0.8850	0.0	0.8850	0.9407
No log	6.8	136	0.9970	0.0	0.9970	0.9985
No log	6.9	138	1.1051	-0.1159	1.1051	1.0513
No log	7.0	140	1.2613	-0.0927	1.2613	1.1231
No log	7.1	142	1.2131	-0.0927	1.2131	1.1014
No log	7.2	144	1.1039	-0.1159	1.1039	1.0507
No log	7.3	146	0.9430	0.0	0.9430	0.9711
No log	7.4	148	0.8708	0.0	0.8708	0.9332
No log	7.5	150	0.8520	0.2143	0.8520	0.9230
No log	7.6	152	0.8353	0.2143	0.8353	0.9139
No log	7.7	154	0.8268	0.0	0.8268	0.9093
No log	7.8	156	0.8269	0.0	0.8269	0.9094
No log	7.9	158	0.8267	0.0	0.8267	0.9092
No log	8.0	160	0.8370	0.0	0.8370	0.9149
No log	8.1	162	0.8588	-0.0185	0.8588	0.9267
No log	8.2	164	0.9093	-0.0185	0.9093	0.9535
No log	8.3	166	0.9624	-0.0185	0.9624	0.9810
No log	8.4	168	0.9879	-0.0185	0.9879	0.9939
No log	8.5	170	0.9866	-0.0185	0.9866	0.9933
No log	8.6	172	0.9690	-0.0185	0.9690	0.9844
No log	8.7	174	0.9334	-0.0185	0.9334	0.9661
No log	8.8	176	0.8909	0.0	0.8909	0.9439
No log	8.9	178	0.8609	0.0	0.8609	0.9278
No log	9.0	180	0.8445	0.0	0.8445	0.9190
No log	9.1	182	0.8359	0.0	0.8359	0.9143
No log	9.2	184	0.8339	0.0	0.8339	0.9132
No log	9.3	186	0.8326	0.0	0.8326	0.9125
No log	9.4	188	0.8332	0.0	0.8332	0.9128
No log	9.5	190	0.8343	0.0	0.8343	0.9134
No log	9.6	192	0.8363	0.0	0.8363	0.9145
No log	9.7	194	0.8374	0.0	0.8374	0.9151
No log	9.8	196	0.8375	0.0	0.8375	0.9152
No log	9.9	198	0.8378	0.0	0.8378	0.9153
No log	10.0	200	0.8382	0.0	0.8382	0.9155

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