Arabic_FineTuningAraBERT_AugV0_k1_task1_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: 0.9449
• Qwk: 0.5896
• Mse: 0.9449
• Rmse: 0.9721

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	0.1176	2	5.1330	0.0904	5.1330	2.2656
No log	0.2353	4	3.4675	0.0	3.4675	1.8621
No log	0.3529	6	2.3648	0.0696	2.3648	1.5378
No log	0.4706	8	1.6993	0.0	1.6993	1.3036
No log	0.5882	10	1.4000	0.1973	1.4000	1.1832
No log	0.7059	12	1.3257	0.1933	1.3257	1.1514
No log	0.8235	14	1.2645	0.2939	1.2645	1.1245
No log	0.9412	16	1.2126	0.2668	1.2126	1.1012
No log	1.0588	18	0.9516	0.3259	0.9516	0.9755
No log	1.1765	20	0.8599	0.4590	0.8599	0.9273
No log	1.2941	22	0.8162	0.4085	0.8162	0.9035
No log	1.4118	24	0.9496	0.3478	0.9496	0.9745
No log	1.5294	26	1.1079	0.3209	1.1079	1.0526
No log	1.6471	28	1.1311	0.3209	1.1311	1.0635
No log	1.7647	30	1.0768	0.3494	1.0768	1.0377
No log	1.8824	32	1.0150	0.5698	1.0150	1.0075
No log	2.0	34	1.0474	0.5468	1.0474	1.0234
No log	2.1176	36	0.9894	0.5653	0.9894	0.9947
No log	2.2353	38	0.8745	0.5830	0.8745	0.9351
No log	2.3529	40	0.8798	0.6738	0.8798	0.9380
No log	2.4706	42	1.0482	0.5830	1.0482	1.0238
No log	2.5882	44	1.1713	0.5660	1.1713	1.0823
No log	2.7059	46	1.0797	0.5686	1.0797	1.0391
No log	2.8235	48	0.9651	0.5686	0.9651	0.9824
No log	2.9412	50	0.8528	0.6338	0.8528	0.9235
No log	3.0588	52	0.8694	0.5312	0.8694	0.9324
No log	3.1765	54	0.9500	0.5312	0.9500	0.9747
No log	3.2941	56	0.9084	0.5312	0.9084	0.9531
No log	3.4118	58	0.8059	0.5312	0.8059	0.8977
No log	3.5294	60	0.8138	0.6338	0.8138	0.9021
No log	3.6471	62	0.9146	0.5422	0.9146	0.9564
No log	3.7647	64	0.9550	0.5365	0.9550	0.9773
No log	3.8824	66	0.9336	0.5365	0.9336	0.9662
No log	4.0	68	0.9319	0.5625	0.9319	0.9654
No log	4.1176	70	0.8851	0.6260	0.8851	0.9408
No log	4.2353	72	0.8516	0.6182	0.8516	0.9228
No log	4.3529	74	0.8250	0.6188	0.8250	0.9083
No log	4.4706	76	0.8158	0.6690	0.8158	0.9032
No log	4.5882	78	0.8102	0.5882	0.8102	0.9001
No log	4.7059	80	0.8191	0.5882	0.8191	0.9051
No log	4.8235	82	0.8353	0.6441	0.8353	0.9139
No log	4.9412	84	0.8613	0.6213	0.8613	0.9281
No log	5.0588	86	0.9234	0.6038	0.9234	0.9610
No log	5.1765	88	0.9213	0.5714	0.9213	0.9598
No log	5.2941	90	0.8488	0.5563	0.8488	0.9213
No log	5.4118	92	0.8263	0.5563	0.8263	0.9090
No log	5.5294	94	0.8231	0.5896	0.8231	0.9073
No log	5.6471	96	0.8229	0.6213	0.8229	0.9071
No log	5.7647	98	0.8101	0.6213	0.8101	0.9000
No log	5.8824	100	0.7902	0.6732	0.7902	0.8889
No log	6.0	102	0.7975	0.6732	0.7975	0.8930
No log	6.1176	104	0.7976	0.6732	0.7976	0.8931
No log	6.2353	106	0.8341	0.6732	0.8341	0.9133
No log	6.3529	108	0.8567	0.6677	0.8567	0.9256
No log	6.4706	110	0.9204	0.6213	0.9204	0.9594
No log	6.5882	112	0.9584	0.6038	0.9584	0.9790
No log	6.7059	114	0.9535	0.6213	0.9535	0.9765
No log	6.8235	116	0.9361	0.6213	0.9361	0.9675
No log	6.9412	118	0.8850	0.6213	0.8850	0.9407
No log	7.0588	120	0.8731	0.6213	0.8731	0.9344
No log	7.1765	122	0.8714	0.6213	0.8714	0.9335
No log	7.2941	124	0.8738	0.6213	0.8738	0.9348
No log	7.4118	126	0.8868	0.6213	0.8868	0.9417
No log	7.5294	128	0.9237	0.6213	0.9237	0.9611
No log	7.6471	130	0.9781	0.5714	0.9781	0.9890
No log	7.7647	132	0.9926	0.5686	0.9926	0.9963
No log	7.8824	134	0.9841	0.5686	0.9841	0.9920
No log	8.0	136	0.9469	0.5896	0.9469	0.9731
No log	8.1176	138	0.9149	0.5896	0.9149	0.9565
No log	8.2353	140	0.8707	0.6677	0.8707	0.9331
No log	8.3529	142	0.8480	0.7178	0.8480	0.9209
No log	8.4706	144	0.8500	0.6677	0.8500	0.9219
No log	8.5882	146	0.8616	0.6677	0.8616	0.9282
No log	8.7059	148	0.8742	0.6677	0.8742	0.9350
No log	8.8235	150	0.8966	0.5896	0.8966	0.9469
No log	8.9412	152	0.9197	0.5896	0.9197	0.9590
No log	9.0588	154	0.9430	0.5896	0.9430	0.9711
No log	9.1765	156	0.9552	0.5714	0.9552	0.9774
No log	9.2941	158	0.9605	0.5714	0.9605	0.9800
No log	9.4118	160	0.9609	0.5714	0.9609	0.9803
No log	9.5294	162	0.9624	0.5714	0.9624	0.9810
No log	9.6471	164	0.9603	0.5896	0.9603	0.9799
No log	9.7647	166	0.9534	0.5896	0.9534	0.9764
No log	9.8824	168	0.9476	0.5896	0.9476	0.9735
No log	10.0	170	0.9449	0.5896	0.9449	0.9721

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu118
• Datasets 2.21.0
• Tokenizers 0.19.1