ArabicNewSplits5_FineTuningAraBERT_run2_AugV5_k4_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: 0.7367
• Qwk: 0.6837
• Mse: 0.7367
• Rmse: 0.8583

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	5.3037	-0.0179	5.3037	2.3030
No log	0.1429	4	4.0836	0.0284	4.0836	2.0208
No log	0.2143	6	2.7112	0.1333	2.7112	1.6466
No log	0.2857	8	1.8214	0.0345	1.8214	1.3496
No log	0.3571	10	1.7552	0.0563	1.7552	1.3248
No log	0.4286	12	1.4629	0.0594	1.4629	1.2095
No log	0.5	14	1.3154	0.1535	1.3154	1.1469
No log	0.5714	16	1.2770	0.3039	1.2770	1.1301
No log	0.6429	18	1.3457	0.1449	1.3457	1.1600
No log	0.7143	20	1.4682	0.0011	1.4682	1.2117
No log	0.7857	22	1.6748	0.0158	1.6748	1.2941
No log	0.8571	24	1.6452	0.0158	1.6452	1.2827
No log	0.9286	26	1.3951	0.1044	1.3951	1.1811
No log	1.0	28	1.3589	0.1485	1.3589	1.1657
No log	1.0714	30	1.4326	0.1233	1.4326	1.1969
No log	1.1429	32	1.3551	0.1766	1.3551	1.1641
No log	1.2143	34	1.3305	0.2369	1.3305	1.1535
No log	1.2857	36	1.1894	0.2512	1.1894	1.0906
No log	1.3571	38	1.1689	0.2912	1.1689	1.0812
No log	1.4286	40	1.2494	0.2796	1.2494	1.1178
No log	1.5	42	1.2125	0.2728	1.2125	1.1011
No log	1.5714	44	1.1800	0.2437	1.1800	1.0863
No log	1.6429	46	1.2837	0.1913	1.2837	1.1330
No log	1.7143	48	1.2301	0.2585	1.2301	1.1091
No log	1.7857	50	1.1807	0.3683	1.1807	1.0866
No log	1.8571	52	1.2248	0.3737	1.2248	1.1067
No log	1.9286	54	1.0235	0.4138	1.0235	1.0117
No log	2.0	56	0.9294	0.4427	0.9294	0.9641
No log	2.0714	58	1.0142	0.3966	1.0142	1.0071
No log	2.1429	60	0.8500	0.4770	0.8500	0.9220
No log	2.2143	62	0.7875	0.5799	0.7875	0.8874
No log	2.2857	64	0.8489	0.5536	0.8489	0.9213
No log	2.3571	66	1.1179	0.4587	1.1179	1.0573
No log	2.4286	68	1.1558	0.4641	1.1558	1.0751
No log	2.5	70	1.0590	0.5088	1.0590	1.0291
No log	2.5714	72	0.9130	0.5227	0.9130	0.9555
No log	2.6429	74	0.8768	0.5575	0.8768	0.9364
No log	2.7143	76	0.8816	0.5627	0.8816	0.9390
No log	2.7857	78	0.8692	0.5705	0.8692	0.9323
No log	2.8571	80	0.9357	0.5851	0.9357	0.9673
No log	2.9286	82	0.9644	0.5965	0.9644	0.9821
No log	3.0	84	1.1043	0.5399	1.1043	1.0509
No log	3.0714	86	1.1054	0.5366	1.1054	1.0514
No log	3.1429	88	1.0816	0.5434	1.0816	1.0400
No log	3.2143	90	1.2089	0.5423	1.2089	1.0995
No log	3.2857	92	1.1578	0.5626	1.1578	1.0760
No log	3.3571	94	0.9356	0.6363	0.9356	0.9673
No log	3.4286	96	0.7304	0.6635	0.7304	0.8546
No log	3.5	98	0.6563	0.6678	0.6563	0.8101
No log	3.5714	100	0.6507	0.6792	0.6507	0.8066
No log	3.6429	102	0.6758	0.6805	0.6758	0.8221
No log	3.7143	104	0.6923	0.6673	0.6923	0.8320
No log	3.7857	106	0.6942	0.6510	0.6942	0.8332
No log	3.8571	108	0.7193	0.6557	0.7193	0.8481
No log	3.9286	110	0.6974	0.6385	0.6974	0.8351
No log	4.0	112	0.6522	0.7079	0.6522	0.8076
No log	4.0714	114	0.6769	0.7005	0.6769	0.8227
No log	4.1429	116	0.6842	0.6851	0.6842	0.8272
No log	4.2143	118	0.7129	0.6787	0.7129	0.8444
No log	4.2857	120	0.8131	0.6403	0.8131	0.9017
No log	4.3571	122	0.8159	0.6724	0.8159	0.9033
No log	4.4286	124	0.7905	0.6776	0.7905	0.8891
No log	4.5	126	0.8259	0.6613	0.8259	0.9088
No log	4.5714	128	0.8304	0.6449	0.8304	0.9113
No log	4.6429	130	0.8075	0.6835	0.8075	0.8986
No log	4.7143	132	0.7921	0.6884	0.7921	0.8900
No log	4.7857	134	0.7765	0.6730	0.7765	0.8812
No log	4.8571	136	0.7834	0.6808	0.7834	0.8851
No log	4.9286	138	0.8157	0.6650	0.8157	0.9032
No log	5.0	140	0.9644	0.5601	0.9644	0.9821
No log	5.0714	142	1.0152	0.5370	1.0152	1.0076
No log	5.1429	144	0.8691	0.6161	0.8691	0.9323
No log	5.2143	146	0.7183	0.7070	0.7183	0.8475
No log	5.2857	148	0.7114	0.6683	0.7114	0.8434
No log	5.3571	150	0.7819	0.6765	0.7819	0.8843
No log	5.4286	152	0.8433	0.6539	0.8433	0.9183
No log	5.5	154	0.8221	0.6609	0.8221	0.9067
No log	5.5714	156	0.7489	0.6615	0.7489	0.8654
No log	5.6429	158	0.7038	0.6776	0.7038	0.8389
No log	5.7143	160	0.7219	0.7087	0.7219	0.8497
No log	5.7857	162	0.7546	0.6823	0.7546	0.8687
No log	5.8571	164	0.7822	0.6841	0.7822	0.8844
No log	5.9286	166	0.8506	0.6688	0.8506	0.9223
No log	6.0	168	0.9285	0.6463	0.9285	0.9636
No log	6.0714	170	0.9153	0.6406	0.9153	0.9567
No log	6.1429	172	0.8233	0.6817	0.8233	0.9074
No log	6.2143	174	0.7392	0.6824	0.7392	0.8597
No log	6.2857	176	0.7423	0.7022	0.7423	0.8616
No log	6.3571	178	0.7571	0.6849	0.7571	0.8701
No log	6.4286	180	0.7370	0.7024	0.7370	0.8585
No log	6.5	182	0.7281	0.6786	0.7281	0.8533
No log	6.5714	184	0.7725	0.6931	0.7725	0.8789
No log	6.6429	186	0.7931	0.6861	0.7931	0.8905
No log	6.7143	188	0.8233	0.6882	0.8233	0.9074
No log	6.7857	190	0.7937	0.6797	0.7937	0.8909
No log	6.8571	192	0.7406	0.6852	0.7406	0.8606
No log	6.9286	194	0.7010	0.7051	0.7010	0.8372
No log	7.0	196	0.6964	0.7100	0.6964	0.8345
No log	7.0714	198	0.7050	0.6911	0.7050	0.8396
No log	7.1429	200	0.7308	0.7002	0.7308	0.8549
No log	7.2143	202	0.7778	0.6815	0.7778	0.8819
No log	7.2857	204	0.8368	0.6683	0.8368	0.9148
No log	7.3571	206	0.8481	0.6544	0.8481	0.9209
No log	7.4286	208	0.8338	0.6785	0.8338	0.9131
No log	7.5	210	0.8121	0.6833	0.8121	0.9012
No log	7.5714	212	0.7645	0.6808	0.7645	0.8744
No log	7.6429	214	0.7354	0.6949	0.7354	0.8576
No log	7.7143	216	0.7131	0.6820	0.7131	0.8444
No log	7.7857	218	0.7134	0.7001	0.7134	0.8446
No log	7.8571	220	0.7138	0.7153	0.7138	0.8449
No log	7.9286	222	0.7132	0.6907	0.7132	0.8445
No log	8.0	224	0.7144	0.6957	0.7144	0.8452
No log	8.0714	226	0.7230	0.7080	0.7230	0.8503
No log	8.1429	228	0.7315	0.6989	0.7315	0.8553
No log	8.2143	230	0.7347	0.6989	0.7347	0.8572
No log	8.2857	232	0.7489	0.6793	0.7489	0.8654
No log	8.3571	234	0.7617	0.6780	0.7617	0.8728
No log	8.4286	236	0.7576	0.6780	0.7576	0.8704
No log	8.5	238	0.7408	0.6751	0.7408	0.8607
No log	8.5714	240	0.7195	0.6797	0.7195	0.8483
No log	8.6429	242	0.7080	0.6814	0.7080	0.8414
No log	8.7143	244	0.7093	0.6825	0.7093	0.8422
No log	8.7857	246	0.7095	0.6836	0.7095	0.8423
No log	8.8571	248	0.7034	0.6921	0.7034	0.8387
No log	8.9286	250	0.6972	0.6891	0.6972	0.8350
No log	9.0	252	0.6965	0.6879	0.6965	0.8346
No log	9.0714	254	0.6954	0.6879	0.6954	0.8339
No log	9.1429	256	0.6977	0.6879	0.6977	0.8353
No log	9.2143	258	0.7024	0.6909	0.7024	0.8381
No log	9.2857	260	0.7078	0.6714	0.7078	0.8413
No log	9.3571	262	0.7128	0.6724	0.7128	0.8443
No log	9.4286	264	0.7182	0.6848	0.7182	0.8475
No log	9.5	266	0.7274	0.6787	0.7274	0.8529
No log	9.5714	268	0.7348	0.6937	0.7348	0.8572
No log	9.6429	270	0.7387	0.6918	0.7387	0.8595
No log	9.7143	272	0.7398	0.6918	0.7398	0.8601
No log	9.7857	274	0.7381	0.6736	0.7381	0.8592
No log	9.8571	276	0.7375	0.6736	0.7375	0.8588
No log	9.9286	278	0.7372	0.6837	0.7372	0.8586
No log	10.0	280	0.7367	0.6837	0.7367	0.8583

Framework versions

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