arabert_cross_development_task1_fold3

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: 0.5861
• Qwk: 0.7634
• Mse: 0.5861

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: 64
• eval_batch_size: 64
• 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
No log	0.1333	2	1.7502	0.1271	1.7502
No log	0.2667	4	1.2292	0.1987	1.2292
No log	0.4	6	1.3080	0.3555	1.3080
No log	0.5333	8	0.9607	0.4976	0.9607
No log	0.6667	10	0.8578	0.6328	0.8578
No log	0.8	12	0.8243	0.6683	0.8243
No log	0.9333	14	0.6736	0.6082	0.6736
No log	1.0667	16	0.6841	0.5941	0.6841
No log	1.2	18	0.6510	0.6300	0.6510
No log	1.3333	20	0.7744	0.7345	0.7744
No log	1.4667	22	0.6786	0.7336	0.6786
No log	1.6	24	0.5769	0.6663	0.5769
No log	1.7333	26	0.5647	0.6751	0.5647
No log	1.8667	28	0.6191	0.7228	0.6191
No log	2.0	30	0.6480	0.7149	0.6480
No log	2.1333	32	0.5930	0.6377	0.5930
No log	2.2667	34	0.5792	0.6840	0.5792
No log	2.4	36	0.6399	0.7684	0.6399
No log	2.5333	38	0.6099	0.7730	0.6099
No log	2.6667	40	0.5513	0.7336	0.5513
No log	2.8	42	0.5787	0.7674	0.5787
No log	2.9333	44	0.6353	0.7926	0.6353
No log	3.0667	46	0.5670	0.7594	0.5670
No log	3.2	48	0.6004	0.7827	0.6004
No log	3.3333	50	0.6263	0.7869	0.6263
No log	3.4667	52	0.5762	0.7498	0.5762
No log	3.6	54	0.5570	0.7472	0.5570
No log	3.7333	56	0.6280	0.7790	0.6280
No log	3.8667	58	0.6056	0.7764	0.6056
No log	4.0	60	0.5239	0.7299	0.5239
No log	4.1333	62	0.5192	0.7305	0.5192
No log	4.2667	64	0.5703	0.7631	0.5703
No log	4.4	66	0.6518	0.7918	0.6518
No log	4.5333	68	0.7302	0.7883	0.7302
No log	4.6667	70	0.6654	0.7960	0.6654
No log	4.8	72	0.5714	0.7539	0.5714
No log	4.9333	74	0.5149	0.7046	0.5149
No log	5.0667	76	0.5129	0.6751	0.5129
No log	5.2	78	0.5200	0.7257	0.5200
No log	5.3333	80	0.5968	0.7701	0.5968
No log	5.4667	82	0.6356	0.7899	0.6356
No log	5.6	84	0.5976	0.7718	0.5976
No log	5.7333	86	0.5510	0.7528	0.5510
No log	5.8667	88	0.5499	0.7505	0.5499
No log	6.0	90	0.5581	0.7507	0.5581
No log	6.1333	92	0.5846	0.7624	0.5846
No log	6.2667	94	0.6247	0.7828	0.6247
No log	6.4	96	0.6363	0.7865	0.6363
No log	6.5333	98	0.6065	0.7792	0.6065
No log	6.6667	100	0.5753	0.7552	0.5753
No log	6.8	102	0.5617	0.7438	0.5617
No log	6.9333	104	0.5593	0.7415	0.5593
No log	7.0667	106	0.5501	0.7410	0.5501
No log	7.2	108	0.5736	0.7489	0.5736
No log	7.3333	110	0.6235	0.7773	0.6235
No log	7.4667	112	0.6392	0.7840	0.6392
No log	7.6	114	0.6211	0.7732	0.6211
No log	7.7333	116	0.5970	0.7733	0.5970
No log	7.8667	118	0.5611	0.7530	0.5611
No log	8.0	120	0.5439	0.7470	0.5439
No log	8.1333	122	0.5497	0.7484	0.5497
No log	8.2667	124	0.5836	0.7580	0.5836
No log	8.4	126	0.6389	0.7749	0.6389
No log	8.5333	128	0.6778	0.7817	0.6778
No log	8.6667	130	0.6794	0.7790	0.6794
No log	8.8	132	0.6575	0.7769	0.6575
No log	8.9333	134	0.6182	0.7797	0.6182
No log	9.0667	136	0.5921	0.7689	0.5921
No log	9.2	138	0.5771	0.7588	0.5771
No log	9.3333	140	0.5657	0.7530	0.5657
No log	9.4667	142	0.5672	0.7530	0.5672
No log	9.6	144	0.5759	0.7639	0.5759
No log	9.7333	146	0.5809	0.7611	0.5809
No log	9.8667	148	0.5847	0.7611	0.5847
No log	10.0	150	0.5861	0.7634	0.5861

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1