ArabicNewSplits5_FineTuningAraBERT_run2_AugV5_k4_task5_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: 0.8226
• Qwk: 0.6881
• Mse: 0.8226
• Rmse: 0.9070

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	0.0952	2	2.3356	0.0562	2.3356	1.5283
No log	0.1905	4	1.6075	0.1917	1.6075	1.2679
No log	0.2857	6	1.3963	0.1914	1.3963	1.1816
No log	0.3810	8	1.3428	0.2092	1.3428	1.1588
No log	0.4762	10	1.4381	0.1757	1.4381	1.1992
No log	0.5714	12	1.3845	0.2535	1.3845	1.1766
No log	0.6667	14	1.2096	0.3256	1.2096	1.0998
No log	0.7619	16	1.2696	0.4150	1.2696	1.1267
No log	0.8571	18	1.2827	0.2115	1.2827	1.1326
No log	0.9524	20	1.3187	0.2542	1.3187	1.1483
No log	1.0476	22	1.3004	0.2695	1.3004	1.1404
No log	1.1429	24	1.2279	0.2991	1.2279	1.1081
No log	1.2381	26	1.1328	0.2991	1.1328	1.0643
No log	1.3333	28	1.0992	0.3342	1.0992	1.0484
No log	1.4286	30	1.0866	0.3538	1.0866	1.0424
No log	1.5238	32	1.0473	0.3344	1.0473	1.0234
No log	1.6190	34	1.0018	0.3920	1.0018	1.0009
No log	1.7143	36	0.9730	0.4786	0.9730	0.9864
No log	1.8095	38	0.9185	0.5027	0.9185	0.9584
No log	1.9048	40	0.8852	0.5342	0.8852	0.9408
No log	2.0	42	0.8663	0.5585	0.8663	0.9307
No log	2.0952	44	0.8868	0.5313	0.8868	0.9417
No log	2.1905	46	0.9275	0.5503	0.9275	0.9631
No log	2.2857	48	0.9614	0.5428	0.9614	0.9805
No log	2.3810	50	0.8632	0.5363	0.8632	0.9291
No log	2.4762	52	0.8310	0.5603	0.8310	0.9116
No log	2.5714	54	0.8587	0.5806	0.8587	0.9267
No log	2.6667	56	0.8643	0.5876	0.8643	0.9297
No log	2.7619	58	0.8517	0.6246	0.8517	0.9229
No log	2.8571	60	0.8241	0.6545	0.8241	0.9078
No log	2.9524	62	0.7932	0.5805	0.7932	0.8906
No log	3.0476	64	0.8481	0.5426	0.8481	0.9209
No log	3.1429	66	0.8272	0.5398	0.8272	0.9095
No log	3.2381	68	0.7982	0.6441	0.7982	0.8934
No log	3.3333	70	0.9506	0.5910	0.9506	0.9750
No log	3.4286	72	1.0133	0.5745	1.0133	1.0066
No log	3.5238	74	1.0623	0.5561	1.0623	1.0307
No log	3.6190	76	0.9367	0.6226	0.9367	0.9679
No log	3.7143	78	0.8342	0.6517	0.8342	0.9134
No log	3.8095	80	0.7344	0.6554	0.7344	0.8570
No log	3.9048	82	0.7209	0.6636	0.7209	0.8491
No log	4.0	84	0.7255	0.6743	0.7255	0.8518
No log	4.0952	86	0.7626	0.6663	0.7626	0.8733
No log	4.1905	88	0.8657	0.6535	0.8657	0.9305
No log	4.2857	90	1.0346	0.5899	1.0346	1.0172
No log	4.3810	92	1.1264	0.5697	1.1264	1.0613
No log	4.4762	94	1.0858	0.5697	1.0858	1.0420
No log	4.5714	96	0.9184	0.6483	0.9184	0.9583
No log	4.6667	98	0.7354	0.6822	0.7354	0.8575
No log	4.7619	100	0.6951	0.6847	0.6951	0.8338
No log	4.8571	102	0.6824	0.6968	0.6824	0.8261
No log	4.9524	104	0.7326	0.6732	0.7326	0.8559
No log	5.0476	106	0.8846	0.6759	0.8846	0.9405
No log	5.1429	108	1.0194	0.6474	1.0194	1.0096
No log	5.2381	110	0.9861	0.6541	0.9861	0.9930
No log	5.3333	112	0.9347	0.6530	0.9347	0.9668
No log	5.4286	114	0.9441	0.6458	0.9441	0.9716
No log	5.5238	116	0.9118	0.6573	0.9118	0.9549
No log	5.6190	118	0.9727	0.6561	0.9727	0.9863
No log	5.7143	120	1.0022	0.6471	1.0022	1.0011
No log	5.8095	122	0.9299	0.6687	0.9299	0.9643
No log	5.9048	124	0.8760	0.6559	0.8760	0.9360
No log	6.0	126	0.8259	0.6990	0.8259	0.9088
No log	6.0952	128	0.7652	0.6888	0.7652	0.8748
No log	6.1905	130	0.7354	0.6961	0.7354	0.8575
No log	6.2857	132	0.7532	0.7062	0.7532	0.8679
No log	6.3810	134	0.8266	0.6979	0.8266	0.9092
No log	6.4762	136	0.9212	0.6606	0.9212	0.9598
No log	6.5714	138	0.9031	0.6654	0.9031	0.9503
No log	6.6667	140	0.8545	0.6886	0.8545	0.9244
No log	6.7619	142	0.7787	0.7108	0.7787	0.8825
No log	6.8571	144	0.7030	0.7270	0.7030	0.8384
No log	6.9524	146	0.6768	0.7177	0.6768	0.8227
No log	7.0476	148	0.6743	0.7177	0.6743	0.8212
No log	7.1429	150	0.7197	0.7272	0.7197	0.8483
No log	7.2381	152	0.8165	0.6800	0.8165	0.9036
No log	7.3333	154	0.9181	0.6671	0.9181	0.9582
No log	7.4286	156	1.0155	0.6265	1.0155	1.0077
No log	7.5238	158	1.0252	0.6418	1.0252	1.0125
No log	7.6190	160	0.9684	0.6525	0.9684	0.9841
No log	7.7143	162	0.8914	0.6456	0.8914	0.9441
No log	7.8095	164	0.8216	0.6806	0.8216	0.9064
No log	7.9048	166	0.7952	0.6814	0.7952	0.8917
No log	8.0	168	0.8119	0.6739	0.8119	0.9011
No log	8.0952	170	0.8447	0.6722	0.8447	0.9191
No log	8.1905	172	0.8836	0.6408	0.8836	0.9400
No log	8.2857	174	0.9357	0.6441	0.9357	0.9673
No log	8.3810	176	0.9503	0.6545	0.9503	0.9748
No log	8.4762	178	0.9361	0.6577	0.9361	0.9675
No log	8.5714	180	0.8954	0.6764	0.8954	0.9463
No log	8.6667	182	0.8567	0.6820	0.8567	0.9256
No log	8.7619	184	0.8146	0.7141	0.8146	0.9026
No log	8.8571	186	0.7700	0.7146	0.7700	0.8775
No log	8.9524	188	0.7302	0.7229	0.7302	0.8545
No log	9.0476	190	0.7138	0.7352	0.7138	0.8448
No log	9.1429	192	0.7036	0.7352	0.7036	0.8388
No log	9.2381	194	0.7100	0.7352	0.7100	0.8426
No log	9.3333	196	0.7303	0.7286	0.7303	0.8546
No log	9.4286	198	0.7554	0.7186	0.7554	0.8691
No log	9.5238	200	0.7804	0.7053	0.7804	0.8834
No log	9.6190	202	0.7921	0.7086	0.7921	0.8900
No log	9.7143	204	0.8014	0.7086	0.8014	0.8952
No log	9.8095	206	0.8133	0.7086	0.8133	0.9018
No log	9.9048	208	0.8197	0.6921	0.8197	0.9054
No log	10.0	210	0.8226	0.6881	0.8226	0.9070

Framework versions

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