arabert_cross_development_task1_fold5

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: 0.3225
• Qwk: 0.7089
• Mse: 0.3218

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	1.6054	0.1118	1.6042
No log	0.2667	4	0.7796	0.3420	0.7792
No log	0.4	6	0.8606	0.4875	0.8600
No log	0.5333	8	0.7185	0.6145	0.7176
No log	0.6667	10	0.4960	0.5784	0.4951
No log	0.8	12	0.4504	0.5814	0.4497
No log	0.9333	14	0.4103	0.6104	0.4096
No log	1.0667	16	0.3725	0.6808	0.3715
No log	1.2	18	0.4101	0.8013	0.4091
No log	1.3333	20	0.3292	0.7235	0.3286
No log	1.4667	22	0.3212	0.6809	0.3206
No log	1.6	24	0.3406	0.7512	0.3398
No log	1.7333	26	0.3852	0.7534	0.3842
No log	1.8667	28	0.3920	0.7341	0.3909
No log	2.0	30	0.4486	0.7835	0.4475
No log	2.1333	32	0.4015	0.7929	0.4005
No log	2.2667	34	0.2966	0.7228	0.2959
No log	2.4	36	0.3163	0.6675	0.3156
No log	2.5333	38	0.2965	0.7270	0.2958
No log	2.6667	40	0.3334	0.7868	0.3325
No log	2.8	42	0.3892	0.7967	0.3882
No log	2.9333	44	0.3635	0.7640	0.3626
No log	3.0667	46	0.3415	0.7020	0.3408
No log	3.2	48	0.3452	0.6985	0.3445
No log	3.3333	50	0.3467	0.7485	0.3460
No log	3.4667	52	0.3606	0.7778	0.3598
No log	3.6	54	0.3419	0.7735	0.3412
No log	3.7333	56	0.3217	0.7477	0.3210
No log	3.8667	58	0.3254	0.6951	0.3248
No log	4.0	60	0.3366	0.6811	0.3360
No log	4.1333	62	0.3255	0.7328	0.3248
No log	4.2667	64	0.3255	0.7574	0.3248
No log	4.4	66	0.3264	0.7713	0.3257
No log	4.5333	68	0.3260	0.7538	0.3253
No log	4.6667	70	0.3303	0.7599	0.3295
No log	4.8	72	0.3278	0.7285	0.3270
No log	4.9333	74	0.3399	0.7039	0.3391
No log	5.0667	76	0.3696	0.6751	0.3689
No log	5.2	78	0.3565	0.6740	0.3558
No log	5.3333	80	0.3177	0.7247	0.3171
No log	5.4667	82	0.3107	0.7637	0.3100
No log	5.6	84	0.3037	0.7643	0.3031
No log	5.7333	86	0.2968	0.7380	0.2962
No log	5.8667	88	0.3026	0.6895	0.3020
No log	6.0	90	0.2948	0.7283	0.2942
No log	6.1333	92	0.2968	0.7351	0.2962
No log	6.2667	94	0.3054	0.6898	0.3048
No log	6.4	96	0.3335	0.6564	0.3329
No log	6.5333	98	0.3257	0.6723	0.3250
No log	6.6667	100	0.3148	0.7398	0.3141
No log	6.8	102	0.3244	0.7519	0.3237
No log	6.9333	104	0.3201	0.7549	0.3194
No log	7.0667	106	0.3197	0.7204	0.3190
No log	7.2	108	0.3241	0.7042	0.3234
No log	7.3333	110	0.3257	0.7232	0.3250
No log	7.4667	112	0.3300	0.7399	0.3293
No log	7.6	114	0.3300	0.7379	0.3292
No log	7.7333	116	0.3299	0.7286	0.3292
No log	7.8667	118	0.3273	0.7096	0.3266
No log	8.0	120	0.3291	0.6958	0.3284
No log	8.1333	122	0.3265	0.6826	0.3258
No log	8.2667	124	0.3190	0.7007	0.3182
No log	8.4	126	0.3131	0.7151	0.3123
No log	8.5333	128	0.3135	0.7284	0.3127
No log	8.6667	130	0.3156	0.7284	0.3148
No log	8.8	132	0.3172	0.7253	0.3164
No log	8.9333	134	0.3198	0.7157	0.3190
No log	9.0667	136	0.3222	0.7034	0.3214
No log	9.2	138	0.3213	0.7089	0.3205
No log	9.3333	140	0.3194	0.7170	0.3186
No log	9.4667	142	0.3189	0.7241	0.3181
No log	9.6	144	0.3199	0.7239	0.3191
No log	9.7333	146	0.3215	0.7103	0.3207
No log	9.8667	148	0.3223	0.7089	0.3215
No log	10.0	150	0.3225	0.7089	0.3218

Framework versions

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