ArabicNewSplits8_FineTuningAraBERT_noAug_task6_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: 0.8340
• Qwk: 0.5723
• Mse: 0.8340
• Rmse: 0.9132

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.6667	2	3.4526	-0.0317	3.4526	1.8581
No log	1.3333	4	2.2343	0.0404	2.2343	1.4948
No log	2.0	6	1.2101	0.2208	1.2101	1.1001
No log	2.6667	8	0.9193	0.0	0.9193	0.9588
No log	3.3333	10	1.0335	0.0335	1.0335	1.0166
No log	4.0	12	0.8504	0.0218	0.8504	0.9222
No log	4.6667	14	0.7086	0.4351	0.7086	0.8418
No log	5.3333	16	0.7541	0.4673	0.7541	0.8684
No log	6.0	18	0.8298	0.1506	0.8298	0.9110
No log	6.6667	20	0.7384	0.3826	0.7384	0.8593
No log	7.3333	22	0.7834	0.3974	0.7834	0.8851
No log	8.0	24	0.8762	0.4259	0.8762	0.9361
No log	8.6667	26	1.0062	0.4243	1.0062	1.0031
No log	9.3333	28	1.1543	0.3660	1.1543	1.0744
No log	10.0	30	1.1532	0.3431	1.1532	1.0739
No log	10.6667	32	0.9774	0.4068	0.9774	0.9886
No log	11.3333	34	0.8913	0.3946	0.8913	0.9441
No log	12.0	36	0.9398	0.4270	0.9398	0.9694
No log	12.6667	38	0.9502	0.4216	0.9502	0.9748
No log	13.3333	40	0.8479	0.4825	0.8479	0.9208
No log	14.0	42	0.8696	0.4985	0.8696	0.9325
No log	14.6667	44	0.8998	0.4798	0.8998	0.9486
No log	15.3333	46	1.0532	0.4270	1.0532	1.0263
No log	16.0	48	1.2459	0.4253	1.2459	1.1162
No log	16.6667	50	1.1062	0.4235	1.1062	1.0518
No log	17.3333	52	0.9501	0.4368	0.9501	0.9747
No log	18.0	54	0.8000	0.5703	0.8000	0.8944
No log	18.6667	56	0.7786	0.5422	0.7786	0.8824
No log	19.3333	58	0.8515	0.6029	0.8515	0.9228
No log	20.0	60	1.0081	0.4269	1.0081	1.0040
No log	20.6667	62	1.0674	0.4284	1.0674	1.0331
No log	21.3333	64	1.0013	0.4189	1.0013	1.0006
No log	22.0	66	0.8419	0.5115	0.8419	0.9175
No log	22.6667	68	0.7435	0.6547	0.7435	0.8623
No log	23.3333	70	0.7719	0.6163	0.7719	0.8786
No log	24.0	72	0.7651	0.5351	0.7651	0.8747
No log	24.6667	74	0.9053	0.4363	0.9053	0.9515
No log	25.3333	76	1.0090	0.4974	1.0090	1.0045
No log	26.0	78	0.9488	0.5155	0.9488	0.9741
No log	26.6667	80	0.7880	0.5981	0.7880	0.8877
No log	27.3333	82	0.6494	0.6654	0.6494	0.8058
No log	28.0	84	0.6348	0.6444	0.6348	0.7967
No log	28.6667	86	0.6857	0.6173	0.6857	0.8281
No log	29.3333	88	0.8317	0.5460	0.8317	0.9120
No log	30.0	90	0.8303	0.5545	0.8303	0.9112
No log	30.6667	92	0.8252	0.5981	0.8252	0.9084
No log	31.3333	94	0.8322	0.5981	0.8322	0.9123
No log	32.0	96	0.9369	0.4573	0.9369	0.9680
No log	32.6667	98	0.8769	0.4764	0.8769	0.9364
No log	33.3333	100	0.6936	0.6296	0.6936	0.8328
No log	34.0	102	0.6399	0.6264	0.6399	0.8000
No log	34.6667	104	0.6302	0.6351	0.6302	0.7938
No log	35.3333	106	0.6795	0.6403	0.6795	0.8243
No log	36.0	108	0.8245	0.5534	0.8245	0.9080
No log	36.6667	110	0.9075	0.5082	0.9075	0.9526
No log	37.3333	112	0.8475	0.5747	0.8475	0.9206
No log	38.0	114	0.8374	0.5603	0.8374	0.9151
No log	38.6667	116	0.8240	0.5766	0.8240	0.9077
No log	39.3333	118	0.7487	0.5591	0.7487	0.8653
No log	40.0	120	0.7509	0.5641	0.7509	0.8666
No log	40.6667	122	0.7738	0.5610	0.7738	0.8797
No log	41.3333	124	0.8370	0.4535	0.8370	0.9149
No log	42.0	126	0.8331	0.4494	0.8331	0.9128
No log	42.6667	128	0.7992	0.5462	0.7992	0.8940
No log	43.3333	130	0.7800	0.5809	0.7800	0.8832
No log	44.0	132	0.8098	0.5543	0.8098	0.8999
No log	44.6667	134	0.8243	0.5371	0.8243	0.9079
No log	45.3333	136	0.7971	0.5388	0.7971	0.8928
No log	46.0	138	0.7945	0.5040	0.7945	0.8914
No log	46.6667	140	0.7726	0.4768	0.7726	0.8790
No log	47.3333	142	0.7802	0.5077	0.7802	0.8833
No log	48.0	144	0.8691	0.4032	0.8691	0.9323
No log	48.6667	146	0.8543	0.4858	0.8543	0.9243
No log	49.3333	148	0.7738	0.4584	0.7738	0.8797
No log	50.0	150	0.6812	0.4978	0.6812	0.8254
No log	50.6667	152	0.6483	0.5688	0.6483	0.8051
No log	51.3333	154	0.6532	0.5983	0.6532	0.8082
No log	52.0	156	0.6650	0.5983	0.6650	0.8154
No log	52.6667	158	0.7280	0.5670	0.7280	0.8532
No log	53.3333	160	0.7748	0.5678	0.7748	0.8802
No log	54.0	162	0.7402	0.5983	0.7402	0.8603
No log	54.6667	164	0.7146	0.5891	0.7146	0.8453
No log	55.3333	166	0.7148	0.5985	0.7148	0.8455
No log	56.0	168	0.7414	0.6164	0.7414	0.8610
No log	56.6667	170	0.7514	0.6071	0.7514	0.8669
No log	57.3333	172	0.7558	0.6071	0.7558	0.8694
No log	58.0	174	0.7710	0.5901	0.7710	0.8781
No log	58.6667	176	0.7183	0.6200	0.7183	0.8475
No log	59.3333	178	0.6501	0.6380	0.6501	0.8063
No log	60.0	180	0.6183	0.6236	0.6183	0.7863
No log	60.6667	182	0.6362	0.6181	0.6362	0.7976
No log	61.3333	184	0.7140	0.5981	0.7140	0.8450
No log	62.0	186	0.8421	0.5427	0.8421	0.9177
No log	62.6667	188	0.9839	0.4686	0.9839	0.9919
No log	63.3333	190	1.0033	0.4535	1.0033	1.0017
No log	64.0	192	0.9185	0.4976	0.9185	0.9584
No log	64.6667	194	0.8244	0.5571	0.8244	0.9080
No log	65.3333	196	0.7345	0.6078	0.7345	0.8571
No log	66.0	198	0.7035	0.5934	0.7035	0.8388
No log	66.6667	200	0.7409	0.5589	0.7409	0.8608
No log	67.3333	202	0.8480	0.5709	0.8480	0.9208
No log	68.0	204	0.9548	0.5001	0.9548	0.9771
No log	68.6667	206	1.0006	0.4646	1.0006	1.0003
No log	69.3333	208	1.0472	0.4482	1.0472	1.0233
No log	70.0	210	1.0085	0.4646	1.0085	1.0042
No log	70.6667	212	0.9211	0.5296	0.9211	0.9597
No log	71.3333	214	0.8564	0.5678	0.8564	0.9254
No log	72.0	216	0.8190	0.5764	0.8190	0.9050
No log	72.6667	218	0.8210	0.5678	0.8210	0.9061
No log	73.3333	220	0.8126	0.5764	0.8126	0.9014
No log	74.0	222	0.8188	0.5764	0.8188	0.9049
No log	74.6667	224	0.8274	0.5497	0.8274	0.9096
No log	75.3333	226	0.8370	0.5497	0.8370	0.9149
No log	76.0	228	0.8657	0.5001	0.8657	0.9304
No log	76.6667	230	0.8825	0.5001	0.8825	0.9394
No log	77.3333	232	0.8894	0.5244	0.8894	0.9431
No log	78.0	234	0.8879	0.5244	0.8879	0.9423
No log	78.6667	236	0.8604	0.5427	0.8604	0.9276
No log	79.3333	238	0.8326	0.5497	0.8326	0.9124
No log	80.0	240	0.7870	0.5678	0.7870	0.8872
No log	80.6667	242	0.7509	0.5764	0.7509	0.8665
No log	81.3333	244	0.7092	0.5935	0.7092	0.8421
No log	82.0	246	0.6965	0.6074	0.6965	0.8346
No log	82.6667	248	0.6998	0.5935	0.6998	0.8366
No log	83.3333	250	0.7183	0.5935	0.7183	0.8475
No log	84.0	252	0.7599	0.5981	0.7599	0.8717
No log	84.6667	254	0.8164	0.5678	0.8164	0.9036
No log	85.3333	256	0.8851	0.5678	0.8851	0.9408
No log	86.0	258	0.9300	0.5427	0.9300	0.9644
No log	86.6667	260	0.9437	0.5234	0.9437	0.9714
No log	87.3333	262	0.9368	0.5234	0.9368	0.9679
No log	88.0	264	0.9105	0.5427	0.9105	0.9542
No log	88.6667	266	0.8754	0.5427	0.8754	0.9356
No log	89.3333	268	0.8341	0.5497	0.8341	0.9133
No log	90.0	270	0.7874	0.5678	0.7874	0.8874
No log	90.6667	272	0.7526	0.5892	0.7526	0.8675
No log	91.3333	274	0.7188	0.5758	0.7188	0.8478
No log	92.0	276	0.7026	0.5894	0.7026	0.8382
No log	92.6667	278	0.7010	0.5937	0.7010	0.8372
No log	93.3333	280	0.7121	0.5847	0.7121	0.8439
No log	94.0	282	0.7318	0.5892	0.7318	0.8555
No log	94.6667	284	0.7518	0.5709	0.7518	0.8671
No log	95.3333	286	0.7656	0.5709	0.7656	0.8750
No log	96.0	288	0.7820	0.5497	0.7820	0.8843
No log	96.6667	290	0.7991	0.5497	0.7991	0.8939
No log	97.3333	292	0.8130	0.5497	0.8130	0.9017
No log	98.0	294	0.8224	0.5497	0.8224	0.9069
No log	98.6667	296	0.8284	0.5723	0.8284	0.9101
No log	99.3333	298	0.8328	0.5723	0.8328	0.9126
No log	100.0	300	0.8340	0.5723	0.8340	0.9132

Framework versions

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