ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k4_task3_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: 0.6238
• Qwk: 0.3143
• Mse: 0.6238
• Rmse: 0.7898

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	0.0870	2	3.1862	-0.0149	3.1862	1.7850
No log	0.1739	4	1.5063	-0.0070	1.5063	1.2273
No log	0.2609	6	0.9292	0.0154	0.9292	0.9640
No log	0.3478	8	0.5718	0.0569	0.5718	0.7562
No log	0.4348	10	0.6197	-0.0159	0.6197	0.7872
No log	0.5217	12	0.6197	0.0	0.6197	0.7872
No log	0.6087	14	0.5831	0.0	0.5831	0.7636
No log	0.6957	16	0.6344	0.1008	0.6344	0.7965
No log	0.7826	18	0.7904	0.1392	0.7904	0.8891
No log	0.8696	20	0.9634	0.0078	0.9634	0.9815
No log	0.9565	22	0.9495	0.0431	0.9495	0.9744
No log	1.0435	24	0.6932	0.1038	0.6932	0.8326
No log	1.1304	26	0.8008	0.0	0.8008	0.8949
No log	1.2174	28	1.5664	0.0345	1.5664	1.2516
No log	1.3043	30	1.2554	0.0118	1.2554	1.1204
No log	1.3913	32	0.6798	-0.0963	0.6798	0.8245
No log	1.4783	34	0.7198	0.0	0.7198	0.8484
No log	1.5652	36	0.5975	0.0	0.5975	0.7730
No log	1.6522	38	0.6422	0.1304	0.6422	0.8013
No log	1.7391	40	0.7374	0.1515	0.7374	0.8587
No log	1.8261	42	0.7895	0.1930	0.7895	0.8886
No log	1.9130	44	0.7567	0.0857	0.7567	0.8699
No log	2.0	46	0.6101	0.1020	0.6101	0.7811
No log	2.0870	48	0.5858	0.0	0.5858	0.7653
No log	2.1739	50	0.5961	0.0	0.5961	0.7721
No log	2.2609	52	0.5845	0.0909	0.5845	0.7645
No log	2.3478	54	0.7893	0.1351	0.7893	0.8884
No log	2.4348	56	1.0004	0.1220	1.0004	1.0002
No log	2.5217	58	0.9796	0.1220	0.9796	0.9897
No log	2.6087	60	0.8244	0.1644	0.8244	0.9080
No log	2.6957	62	0.6330	0.1186	0.6330	0.7956
No log	2.7826	64	0.5820	-0.0196	0.5820	0.7629
No log	2.8696	66	0.5777	0.1888	0.5777	0.7601
No log	2.9565	68	0.6395	0.2970	0.6395	0.7997
No log	3.0435	70	0.5984	0.2516	0.5984	0.7736
No log	3.1304	72	0.5057	0.1892	0.5057	0.7111
No log	3.2174	74	0.6451	0.2157	0.6451	0.8032
No log	3.3043	76	0.6688	0.2077	0.6688	0.8178
No log	3.3913	78	0.5754	0.2000	0.5754	0.7586
No log	3.4783	80	0.5170	0.1788	0.5170	0.7190
No log	3.5652	82	0.5106	0.2704	0.5106	0.7146
No log	3.6522	84	0.5492	0.3073	0.5492	0.7411
No log	3.7391	86	0.5183	0.3333	0.5183	0.7199
No log	3.8261	88	0.6397	0.2079	0.6397	0.7998
No log	3.9130	90	1.0533	0.2065	1.0533	1.0263
No log	4.0	92	1.0456	0.2131	1.0456	1.0226
No log	4.0870	94	0.7287	0.2442	0.7287	0.8536
No log	4.1739	96	0.6168	0.2549	0.6168	0.7853
No log	4.2609	98	0.7530	0.2227	0.7530	0.8678
No log	4.3478	100	0.8038	0.2838	0.8038	0.8965
No log	4.4348	102	0.6810	0.2161	0.6810	0.8252
No log	4.5217	104	0.6466	0.1186	0.6466	0.8041
No log	4.6087	106	0.7086	0.1600	0.7086	0.8418
No log	4.6957	108	0.6853	0.1600	0.6853	0.8278
No log	4.7826	110	0.5760	0.1086	0.5760	0.7589
No log	4.8696	112	0.6470	0.2941	0.6470	0.8044
No log	4.9565	114	0.7274	0.2821	0.7274	0.8529
No log	5.0435	116	0.8475	0.2829	0.8475	0.9206
No log	5.1304	118	0.7097	0.2775	0.7097	0.8425
No log	5.2174	120	0.6660	0.2982	0.6660	0.8161
No log	5.3043	122	0.6022	0.4123	0.6022	0.7760
No log	5.3913	124	0.6507	0.4383	0.6507	0.8067
No log	5.4783	126	0.6149	0.3982	0.6149	0.7842
No log	5.5652	128	0.5994	0.4027	0.5994	0.7742
No log	5.6522	130	0.5849	0.3973	0.5849	0.7648
No log	5.7391	132	0.6693	0.4236	0.6693	0.8181
No log	5.8261	134	0.8193	0.2640	0.8193	0.9051
No log	5.9130	136	0.8652	0.2389	0.8652	0.9302
No log	6.0	138	0.8833	0.2389	0.8833	0.9398
No log	6.0870	140	0.7653	0.3333	0.7653	0.8748
No log	6.1739	142	0.6552	0.3091	0.6552	0.8094
No log	6.2609	144	0.6699	0.2961	0.6699	0.8185
No log	6.3478	146	0.6794	0.3306	0.6794	0.8242
No log	6.4348	148	0.8103	0.2961	0.8103	0.9002
No log	6.5217	150	1.0933	0.1822	1.0933	1.0456
No log	6.6087	152	1.1452	0.1367	1.1452	1.0702
No log	6.6957	154	0.9101	0.1803	0.9101	0.9540
No log	6.7826	156	0.6609	0.3333	0.6609	0.8130
No log	6.8696	158	0.6195	0.3427	0.6195	0.7871
No log	6.9565	160	0.6210	0.3365	0.6210	0.7881
No log	7.0435	162	0.6222	0.3645	0.6222	0.7888
No log	7.1304	164	0.6285	0.3722	0.6285	0.7928
No log	7.2174	166	0.7106	0.2982	0.7106	0.8430
No log	7.3043	168	0.8324	0.3188	0.8324	0.9124
No log	7.3913	170	0.8147	0.2775	0.8147	0.9026
No log	7.4783	172	0.7574	0.2356	0.7574	0.8703
No log	7.5652	174	0.6774	0.3665	0.6774	0.8230
No log	7.6522	176	0.6319	0.3455	0.6319	0.7949
No log	7.7391	178	0.6340	0.3739	0.6340	0.7962
No log	7.8261	180	0.6514	0.3886	0.6514	0.8071
No log	7.9130	182	0.7147	0.3684	0.7147	0.8454
No log	8.0	184	0.7701	0.2320	0.7701	0.8776
No log	8.0870	186	0.7174	0.3613	0.7174	0.8470
No log	8.1739	188	0.6104	0.4188	0.6104	0.7813
No log	8.2609	190	0.5496	0.3665	0.5496	0.7414
No log	8.3478	192	0.5452	0.4010	0.5452	0.7384
No log	8.4348	194	0.5427	0.3962	0.5427	0.7367
No log	8.5217	196	0.5535	0.3761	0.5535	0.7440
No log	8.6087	198	0.6090	0.4188	0.6090	0.7804
No log	8.6957	200	0.6788	0.3648	0.6788	0.8239
No log	8.7826	202	0.7177	0.3580	0.7177	0.8472
No log	8.8696	204	0.6920	0.3277	0.6920	0.8319
No log	8.9565	206	0.6380	0.3939	0.6380	0.7988
No log	9.0435	208	0.5787	0.3684	0.5787	0.7607
No log	9.1304	210	0.5580	0.3684	0.5580	0.7470
No log	9.2174	212	0.5506	0.3585	0.5506	0.7420
No log	9.3043	214	0.5491	0.3585	0.5491	0.7410
No log	9.3913	216	0.5550	0.3684	0.5550	0.7450
No log	9.4783	218	0.5647	0.3684	0.5647	0.7515
No log	9.5652	220	0.5790	0.3684	0.5790	0.7609
No log	9.6522	222	0.5947	0.3398	0.5947	0.7711
No log	9.7391	224	0.6088	0.3427	0.6088	0.7803
No log	9.8261	226	0.6191	0.3427	0.6191	0.7868
No log	9.9130	228	0.6229	0.3143	0.6229	0.7893
No log	10.0	230	0.6238	0.3143	0.6238	0.7898

Framework versions

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