Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold0

Arabic_FineTuningAraBERT_AugV5_k4_task5_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: dec2dbbf

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV5_k4_task5_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: 0.6570
Qwk: 0.6617
Mse: 0.6570
Rmse: 0.8105

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	0.0769	2	2.0705	0.0402	2.0705	1.4389
No log	0.1538	4	1.1508	0.0461	1.1508	1.0727
No log	0.2308	6	1.4223	0.0753	1.4223	1.1926
No log	0.3077	8	1.4063	-0.0616	1.4063	1.1859
No log	0.3846	10	1.2171	0.0311	1.2171	1.1032
No log	0.4615	12	1.0108	0.3311	1.0108	1.0054
No log	0.5385	14	0.9249	0.3272	0.9249	0.9617
No log	0.6154	16	0.8946	0.5107	0.8946	0.9458
No log	0.6923	18	0.8524	0.3841	0.8524	0.9233
No log	0.7692	20	0.8205	0.3050	0.8205	0.9058
No log	0.8462	22	0.7849	0.3050	0.7849	0.8859
No log	0.9231	24	0.7277	0.3841	0.7277	0.8531
No log	1.0	26	0.6799	0.5195	0.6799	0.8245
No log	1.0769	28	0.6240	0.4953	0.6240	0.7899
No log	1.1538	30	0.6252	0.5312	0.6252	0.7907
No log	1.2308	32	0.6362	0.5312	0.6362	0.7976
No log	1.3077	34	0.6171	0.4586	0.6171	0.7856
No log	1.3846	36	0.6366	0.5046	0.6366	0.7979
No log	1.4615	38	0.6634	0.5046	0.6634	0.8145
No log	1.5385	40	0.6512	0.4688	0.6512	0.8070
No log	1.6154	42	0.6416	0.4097	0.6416	0.8010
No log	1.6923	44	0.7047	0.3884	0.7047	0.8395
No log	1.7692	46	0.7715	0.5503	0.7715	0.8783
No log	1.8462	48	0.7998	0.5503	0.7998	0.8943
No log	1.9231	50	0.8379	0.5503	0.8379	0.9154
No log	2.0	52	0.7863	0.6122	0.7863	0.8867
No log	2.0769	54	0.7059	0.6122	0.7059	0.8402
No log	2.1538	56	0.6167	0.5719	0.6167	0.7853
No log	2.2308	58	0.6061	0.6122	0.6061	0.7785
No log	2.3077	60	0.6415	0.5879	0.6415	0.8009
No log	2.3846	62	0.7118	0.5349	0.7118	0.8437
No log	2.4615	64	0.7414	0.3872	0.7414	0.8610
No log	2.5385	66	0.7535	0.4203	0.7535	0.8681
No log	2.6154	68	0.7118	0.4521	0.7118	0.8437
No log	2.6923	70	0.6677	0.5128	0.6677	0.8171
No log	2.7692	72	0.6070	0.6069	0.6070	0.7791
No log	2.8462	74	0.6175	0.5792	0.6175	0.7858
No log	2.9231	76	0.6055	0.5792	0.6055	0.7782
No log	3.0	78	0.6576	0.5792	0.6576	0.8109
No log	3.0769	80	0.6292	0.6341	0.6292	0.7932
No log	3.1538	82	0.6229	0.6438	0.6229	0.7893
No log	3.2308	84	0.6116	0.6834	0.6116	0.7820
No log	3.3077	86	0.6162	0.4872	0.6162	0.7850
No log	3.3846	88	0.6053	0.4920	0.6053	0.7780
No log	3.4615	90	0.5724	0.7120	0.5724	0.7566
No log	3.5385	92	0.5390	0.6717	0.5390	0.7342
No log	3.6154	94	0.5348	0.6992	0.5348	0.7313
No log	3.6923	96	0.5298	0.6992	0.5298	0.7279
No log	3.7692	98	0.5820	0.6717	0.5820	0.7629
No log	3.8462	100	0.6435	0.6717	0.6435	0.8022
No log	3.9231	102	0.7564	0.6444	0.7564	0.8697
No log	4.0	104	0.9983	0.5734	0.9983	0.9992
No log	4.0769	106	1.1154	0.5434	1.1154	1.0561
No log	4.1538	108	1.0462	0.5875	1.0462	1.0228
No log	4.2308	110	0.8733	0.6757	0.8733	0.9345
No log	4.3077	112	0.7580	0.6929	0.7580	0.8706
No log	4.3846	114	0.6602	0.6929	0.6602	0.8125
No log	4.4615	116	0.5573	0.6992	0.5573	0.7466
No log	4.5385	118	0.5143	0.6269	0.5143	0.7171
No log	4.6154	120	0.5052	0.6269	0.5052	0.7108
No log	4.6923	122	0.5120	0.6992	0.5120	0.7155
No log	4.7692	124	0.5532	0.6438	0.5532	0.7438
No log	4.8462	126	0.6268	0.6069	0.6268	0.7917
No log	4.9231	128	0.7298	0.6444	0.7298	0.8543
No log	5.0	130	0.8715	0.6444	0.8715	0.9335
No log	5.0769	132	0.9723	0.6505	0.9723	0.9860
No log	5.1538	134	0.9957	0.6505	0.9957	0.9978
No log	5.2308	136	0.9482	0.6805	0.9482	0.9737
No log	5.3077	138	0.8800	0.6505	0.8800	0.9381
No log	5.3846	140	0.8580	0.6444	0.8580	0.9263
No log	5.4615	142	0.8225	0.6674	0.8225	0.9069
No log	5.5385	144	0.7904	0.6868	0.7904	0.8891
No log	5.6154	146	0.7569	0.6610	0.7569	0.8700
No log	5.6923	148	0.7324	0.7111	0.7324	0.8558
No log	5.7692	150	0.7219	0.7111	0.7219	0.8497
No log	5.8462	152	0.7099	0.6560	0.7099	0.8425
No log	5.9231	154	0.6984	0.6560	0.6984	0.8357
No log	6.0	156	0.6891	0.6560	0.6891	0.8301
No log	6.0769	158	0.6688	0.6560	0.6688	0.8178
No log	6.1538	160	0.6518	0.6560	0.6518	0.8073
No log	6.2308	162	0.6311	0.6560	0.6311	0.7944
No log	6.3077	164	0.6333	0.6560	0.6333	0.7958
No log	6.3846	166	0.6513	0.7229	0.6513	0.8070
No log	6.4615	168	0.6258	0.7229	0.6258	0.7910
No log	6.5385	170	0.6331	0.7229	0.6331	0.7957
No log	6.6154	172	0.6394	0.7229	0.6394	0.7997
No log	6.6923	174	0.6261	0.7229	0.6261	0.7913
No log	6.7692	176	0.6171	0.7125	0.6171	0.7855
No log	6.8462	178	0.6264	0.7125	0.6264	0.7914
No log	6.9231	180	0.6532	0.7125	0.6532	0.8082
No log	7.0	182	0.6924	0.7125	0.6924	0.8321
No log	7.0769	184	0.7172	0.6901	0.7172	0.8469
No log	7.1538	186	0.7043	0.6901	0.7043	0.8392
No log	7.2308	188	0.6624	0.7229	0.6624	0.8139
No log	7.3077	190	0.6293	0.6560	0.6293	0.7933
No log	7.3846	192	0.6217	0.6560	0.6217	0.7885
No log	7.4615	194	0.6059	0.6560	0.6059	0.7784
No log	7.5385	196	0.6226	0.6617	0.6226	0.7890
No log	7.6154	198	0.6272	0.6617	0.6272	0.7920
No log	7.6923	200	0.6301	0.6617	0.6301	0.7938
No log	7.7692	202	0.6398	0.7234	0.6398	0.7999
No log	7.8462	204	0.6649	0.7234	0.6649	0.8154
No log	7.9231	206	0.7128	0.7125	0.7128	0.8442
No log	8.0	208	0.7526	0.7125	0.7526	0.8675
No log	8.0769	210	0.7657	0.7125	0.7657	0.8750
No log	8.1538	212	0.7593	0.7125	0.7593	0.8714
No log	8.2308	214	0.7534	0.7125	0.7534	0.8680
No log	8.3077	216	0.7346	0.7125	0.7346	0.8571
No log	8.3846	218	0.7144	0.7125	0.7144	0.8452
No log	8.4615	220	0.6943	0.7234	0.6943	0.8333
No log	8.5385	222	0.6702	0.7234	0.6702	0.8186
No log	8.6154	224	0.6557	0.6617	0.6557	0.8097
No log	8.6923	226	0.6551	0.7234	0.6551	0.8094
No log	8.7692	228	0.6542	0.7234	0.6542	0.8088
No log	8.8462	230	0.6436	0.6617	0.6436	0.8022
No log	8.9231	232	0.6421	0.6617	0.6421	0.8013
No log	9.0	234	0.6443	0.6617	0.6443	0.8027
No log	9.0769	236	0.6474	0.6617	0.6474	0.8046
No log	9.1538	238	0.6556	0.6617	0.6556	0.8097
No log	9.2308	240	0.6574	0.6617	0.6574	0.8108
No log	9.3077	242	0.6520	0.6617	0.6520	0.8075
No log	9.3846	244	0.6468	0.6617	0.6468	0.8042
No log	9.4615	246	0.6471	0.6617	0.6471	0.8044
No log	9.5385	248	0.6446	0.6617	0.6446	0.8028
No log	9.6154	250	0.6402	0.6617	0.6402	0.8001
No log	9.6923	252	0.6422	0.6617	0.6422	0.8014
No log	9.7692	254	0.6475	0.6617	0.6475	0.8047
No log	9.8462	256	0.6517	0.6617	0.6517	0.8073
No log	9.9231	258	0.6552	0.6617	0.6552	0.8095
No log	10.0	260	0.6570	0.6617	0.6570	0.8105

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