Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold1

Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold1 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: c5c3da55

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV5_k4_task5_organization_fold1

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: 0.8682
Qwk: 0.6767
Mse: 0.8682
Rmse: 0.9318

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	0.0714	2	3.7423	0.0897	3.7423	1.9345
No log	0.1429	4	2.4307	0.0	2.4307	1.5591
No log	0.2143	6	1.6103	-0.1461	1.6103	1.2690
No log	0.2857	8	1.2813	0.1571	1.2813	1.1320
No log	0.3571	10	1.2019	0.0741	1.2019	1.0963
No log	0.4286	12	1.3591	0.0816	1.3591	1.1658
No log	0.5	14	1.3007	0.0820	1.3007	1.1405
No log	0.5714	16	1.1178	0.1571	1.1178	1.0572
No log	0.6429	18	1.0792	0.1481	1.0792	1.0388
No log	0.7143	20	1.0658	0.1481	1.0658	1.0324
No log	0.7857	22	1.0799	0.1571	1.0799	1.0392
No log	0.8571	24	1.0871	0.2279	1.0871	1.0427
No log	0.9286	26	1.0585	0.2226	1.0585	1.0288
No log	1.0	28	1.1341	0.4345	1.1341	1.0649
No log	1.0714	30	1.1730	0.4236	1.1730	1.0831
No log	1.1429	32	1.1023	0.4236	1.1023	1.0499
No log	1.2143	34	1.0145	0.4236	1.0145	1.0072
No log	1.2857	36	0.9817	0.4236	0.9817	0.9908
No log	1.3571	38	0.9747	0.4236	0.9747	0.9873
No log	1.4286	40	0.8506	0.4345	0.8506	0.9223
No log	1.5	42	0.8332	0.4345	0.8332	0.9128
No log	1.5714	44	0.8349	0.4345	0.8349	0.9137
No log	1.6429	46	0.8454	0.4345	0.8454	0.9195
No log	1.7143	48	0.8511	0.4345	0.8511	0.9226
No log	1.7857	50	0.9609	0.4236	0.9609	0.9802
No log	1.8571	52	0.9826	0.3354	0.9826	0.9913
No log	1.9286	54	0.9607	0.3443	0.9607	0.9802
No log	2.0	56	0.8781	0.2857	0.8781	0.9371
No log	2.0714	58	0.8288	0.2933	0.8288	0.9104
No log	2.1429	60	0.8241	0.2759	0.8241	0.9078
No log	2.2143	62	0.8637	0.4627	0.8637	0.9293
No log	2.2857	64	0.8802	0.3895	0.8802	0.9382
No log	2.3571	66	0.8329	0.4627	0.8329	0.9126
No log	2.4286	68	0.7811	0.4752	0.7811	0.8838
No log	2.5	70	0.8143	0.375	0.8143	0.9024
No log	2.5714	72	0.8830	0.375	0.8830	0.9397
No log	2.6429	74	0.9510	0.4134	0.9510	0.9752
No log	2.7143	76	0.8576	0.3443	0.8576	0.9261
No log	2.7857	78	0.7622	0.4848	0.7622	0.8730
No log	2.8571	80	0.7779	0.4207	0.7779	0.8820
No log	2.9286	82	0.8064	0.4627	0.8064	0.8980
No log	3.0	84	0.7896	0.5058	0.7896	0.8886
No log	3.0714	86	0.7462	0.4540	0.7462	0.8639
No log	3.1429	88	0.7343	0.4540	0.7343	0.8569
No log	3.2143	90	0.7514	0.5355	0.7514	0.8668
No log	3.2857	92	0.7487	0.5355	0.7487	0.8653
No log	3.3571	94	0.7591	0.6094	0.7591	0.8713
No log	3.4286	96	0.7671	0.6094	0.7671	0.8759
No log	3.5	98	0.8507	0.5420	0.8507	0.9224
No log	3.5714	100	0.9618	0.5134	0.9618	0.9807
No log	3.6429	102	0.8368	0.5420	0.8368	0.9148
No log	3.7143	104	0.6595	0.6814	0.6595	0.8121
No log	3.7857	106	0.7809	0.6253	0.7809	0.8837
No log	3.8571	108	0.8132	0.5933	0.8132	0.9018
No log	3.9286	110	0.6963	0.5272	0.6963	0.8345
No log	4.0	112	0.7022	0.6267	0.7022	0.8380
No log	4.0714	114	0.8397	0.6094	0.8397	0.9163
No log	4.1429	116	0.8809	0.5420	0.8809	0.9386
No log	4.2143	118	0.8567	0.6094	0.8567	0.9256
No log	4.2857	120	0.8574	0.6094	0.8574	0.9260
No log	4.3571	122	0.8048	0.6094	0.8048	0.8971
No log	4.4286	124	0.7786	0.6094	0.7786	0.8824
No log	4.5	126	0.7463	0.6094	0.7463	0.8639
No log	4.5714	128	0.6991	0.6094	0.6991	0.8361
No log	4.6429	130	0.6714	0.6267	0.6714	0.8194
No log	4.7143	132	0.7029	0.6267	0.7029	0.8384
No log	4.7857	134	0.8002	0.6094	0.8002	0.8946
No log	4.8571	136	0.8345	0.6094	0.8345	0.9135
No log	4.9286	138	0.7906	0.6145	0.7906	0.8892
No log	5.0	140	0.8144	0.6622	0.8144	0.9024
No log	5.0714	142	0.7536	0.6622	0.7536	0.8681
No log	5.1429	144	0.7721	0.6617	0.7721	0.8787
No log	5.2143	146	0.8918	0.6195	0.8918	0.9443
No log	5.2857	148	0.9131	0.6195	0.9131	0.9556
No log	5.3571	150	0.8022	0.6484	0.8022	0.8957
No log	5.4286	152	0.7731	0.6499	0.7731	0.8793
No log	5.5	154	0.8211	0.6195	0.8211	0.9062
No log	5.5714	156	0.9140	0.6058	0.9140	0.9560
No log	5.6429	158	0.8924	0.6195	0.8924	0.9447
No log	5.7143	160	0.8321	0.6336	0.8321	0.9122
No log	5.7857	162	0.8619	0.6305	0.8619	0.9284
No log	5.8571	164	0.9779	0.5625	0.9779	0.9889
No log	5.9286	166	1.0514	0.5092	1.0514	1.0254
No log	6.0	168	1.0961	0.5560	1.0961	1.0469
No log	6.0714	170	1.0115	0.5560	1.0115	1.0057
No log	6.1429	172	0.8471	0.6767	0.8471	0.9204
No log	6.2143	174	0.7653	0.6114	0.7653	0.8748
No log	6.2857	176	0.7592	0.5103	0.7592	0.8713
No log	6.3571	178	0.7688	0.6114	0.7688	0.8768
No log	6.4286	180	0.8343	0.6782	0.8343	0.9134
No log	6.5	182	0.8934	0.6606	0.8934	0.9452
No log	6.5714	184	0.8677	0.6606	0.8677	0.9315
No log	6.6429	186	0.7892	0.6948	0.7892	0.8884
No log	6.7143	188	0.7737	0.6163	0.7737	0.8796
No log	6.7857	190	0.7859	0.6163	0.7859	0.8865
No log	6.8571	192	0.8432	0.6767	0.8432	0.9182
No log	6.9286	194	0.9046	0.6614	0.9046	0.9511
No log	7.0	196	0.9381	0.6614	0.9381	0.9685
No log	7.0714	198	0.8982	0.6755	0.8982	0.9477
No log	7.1429	200	0.8641	0.5923	0.8641	0.9295
No log	7.2143	202	0.8702	0.6226	0.8702	0.9329
No log	7.2857	204	0.8845	0.6632	0.8845	0.9405
No log	7.3571	206	0.9586	0.5857	0.9586	0.9791
No log	7.4286	208	1.0817	0.4965	1.0817	1.0400
No log	7.5	210	1.1268	0.4965	1.1268	1.0615
No log	7.5714	212	1.0665	0.5062	1.0665	1.0327
No log	7.6429	214	0.9669	0.5800	0.9669	0.9833
No log	7.7143	216	0.8644	0.5923	0.8644	0.9298
No log	7.7857	218	0.8320	0.6368	0.8320	0.9122
No log	7.8571	220	0.8155	0.6242	0.8155	0.9031
No log	7.9286	222	0.8097	0.6242	0.8097	0.8998
No log	8.0	224	0.8024	0.6242	0.8024	0.8958
No log	8.0714	226	0.8108	0.6009	0.8108	0.9005
No log	8.1429	228	0.8207	0.6009	0.8207	0.9059
No log	8.2143	230	0.8415	0.5991	0.8415	0.9173
No log	8.2857	232	0.8686	0.6767	0.8686	0.9320
No log	8.3571	234	0.8860	0.6767	0.8860	0.9413
No log	8.4286	236	0.9276	0.6753	0.9276	0.9631
No log	8.5	238	0.9519	0.6603	0.9519	0.9756
No log	8.5714	240	0.9350	0.6753	0.9350	0.9670
No log	8.6429	242	0.9021	0.6767	0.9021	0.9498
No log	8.7143	244	0.8902	0.6767	0.8902	0.9435
No log	8.7857	246	0.8951	0.6767	0.8951	0.9461
No log	8.8571	248	0.8892	0.6767	0.8892	0.9430
No log	8.9286	250	0.8842	0.6767	0.8842	0.9403
No log	9.0	252	0.8951	0.6767	0.8951	0.9461
No log	9.0714	254	0.9013	0.6767	0.9013	0.9494
No log	9.1429	256	0.8995	0.6767	0.8995	0.9484
No log	9.2143	258	0.8960	0.6767	0.8960	0.9466
No log	9.2857	260	0.8879	0.6767	0.8879	0.9423
No log	9.3571	262	0.8745	0.6767	0.8745	0.9352
No log	9.4286	264	0.8727	0.6767	0.8727	0.9342
No log	9.5	266	0.8747	0.6767	0.8747	0.9353
No log	9.5714	268	0.8704	0.6767	0.8704	0.9330
No log	9.6429	270	0.8687	0.6767	0.8687	0.9321
No log	9.7143	272	0.8676	0.6767	0.8676	0.9315
No log	9.7857	274	0.8703	0.6767	0.8703	0.9329
No log	9.8571	276	0.8690	0.6767	0.8690	0.9322
No log	9.9286	278	0.8685	0.6767	0.8685	0.9319
No log	10.0	280	0.8682	0.6767	0.8682	0.9318

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