ArabicNewSplits8_FineTuningAraBERT_noAug_task2_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: 0.6714
• Qwk: 0.5156
• Mse: 0.6714
• Rmse: 0.8194

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	3.9333	0.0094	3.9333	1.9833
No log	1.3333	4	3.1065	0.0284	3.1065	1.7625
No log	2.0	6	1.4978	0.1188	1.4978	1.2238
No log	2.6667	8	0.8659	0.1086	0.8659	0.9305
No log	3.3333	10	0.7877	0.1417	0.7877	0.8875
No log	4.0	12	0.7653	0.1152	0.7653	0.8748
No log	4.6667	14	0.7857	0.2197	0.7857	0.8864
No log	5.3333	16	0.8088	0.1876	0.8088	0.8994
No log	6.0	18	0.8477	0.2236	0.8477	0.9207
No log	6.6667	20	0.8801	0.3965	0.8801	0.9381
No log	7.3333	22	0.9859	0.3850	0.9859	0.9929
No log	8.0	24	0.8407	0.4453	0.8407	0.9169
No log	8.6667	26	0.8650	0.3596	0.8650	0.9301
No log	9.3333	28	1.0178	0.3159	1.0178	1.0089
No log	10.0	30	0.8846	0.3841	0.8846	0.9405
No log	10.6667	32	0.8729	0.4134	0.8729	0.9343
No log	11.3333	34	0.9629	0.3843	0.9629	0.9813
No log	12.0	36	1.0289	0.3577	1.0289	1.0144
No log	12.6667	38	0.9523	0.3680	0.9523	0.9758
No log	13.3333	40	0.8803	0.4312	0.8803	0.9382
No log	14.0	42	0.8190	0.4571	0.8190	0.9050
No log	14.6667	44	0.8317	0.5034	0.8317	0.9119
No log	15.3333	46	0.8657	0.3865	0.8657	0.9304
No log	16.0	48	0.8819	0.4260	0.8819	0.9391
No log	16.6667	50	0.8053	0.3936	0.8053	0.8974
No log	17.3333	52	0.7487	0.4849	0.7487	0.8653
No log	18.0	54	0.7399	0.5179	0.7399	0.8602
No log	18.6667	56	0.7127	0.5501	0.7127	0.8442
No log	19.3333	58	0.7189	0.5274	0.7189	0.8479
No log	20.0	60	0.8036	0.4739	0.8036	0.8964
No log	20.6667	62	0.8782	0.4078	0.8782	0.9371
No log	21.3333	64	0.8042	0.4893	0.8042	0.8968
No log	22.0	66	0.7393	0.5426	0.7393	0.8598
No log	22.6667	68	0.6552	0.5967	0.6552	0.8095
No log	23.3333	70	0.5832	0.6010	0.5832	0.7637
No log	24.0	72	0.6234	0.5990	0.6234	0.7896
No log	24.6667	74	0.7360	0.5391	0.7360	0.8579
No log	25.3333	76	0.7060	0.5401	0.7060	0.8403
No log	26.0	78	0.5976	0.5991	0.5976	0.7731
No log	26.6667	80	0.6236	0.6085	0.6236	0.7897
No log	27.3333	82	0.7617	0.4818	0.7617	0.8728
No log	28.0	84	0.8521	0.4956	0.8521	0.9231
No log	28.6667	86	0.8280	0.4847	0.8280	0.9100
No log	29.3333	88	0.7597	0.5058	0.7597	0.8716
No log	30.0	90	0.7043	0.5265	0.7043	0.8392
No log	30.6667	92	0.6696	0.5449	0.6696	0.8183
No log	31.3333	94	0.6765	0.5257	0.6765	0.8225
No log	32.0	96	0.6607	0.4854	0.6607	0.8129
No log	32.6667	98	0.7377	0.5375	0.7377	0.8589
No log	33.3333	100	0.7520	0.5354	0.7520	0.8672
No log	34.0	102	0.7180	0.5432	0.7180	0.8473
No log	34.6667	104	0.6776	0.5701	0.6776	0.8232
No log	35.3333	106	0.7057	0.5520	0.7057	0.8401
No log	36.0	108	0.7387	0.5115	0.7387	0.8595
No log	36.6667	110	0.7285	0.5400	0.7285	0.8535
No log	37.3333	112	0.7033	0.5384	0.7033	0.8386
No log	38.0	114	0.7042	0.5540	0.7042	0.8391
No log	38.6667	116	0.6853	0.5401	0.6853	0.8278
No log	39.3333	118	0.6426	0.5350	0.6426	0.8016
No log	40.0	120	0.6468	0.5639	0.6468	0.8042
No log	40.6667	122	0.7160	0.5316	0.7160	0.8462
No log	41.3333	124	0.8095	0.4899	0.8095	0.8997
No log	42.0	126	0.9099	0.4697	0.9099	0.9539
No log	42.6667	128	0.9520	0.4376	0.9520	0.9757
No log	43.3333	130	0.9413	0.4172	0.9413	0.9702
No log	44.0	132	0.8579	0.4620	0.8579	0.9262
No log	44.6667	134	0.8168	0.4568	0.8168	0.9038
No log	45.3333	136	0.7457	0.4770	0.7457	0.8636
No log	46.0	138	0.6970	0.5363	0.6970	0.8349
No log	46.6667	140	0.7055	0.5308	0.7055	0.8399
No log	47.3333	142	0.7795	0.4838	0.7795	0.8829
No log	48.0	144	0.9026	0.4931	0.9026	0.9501
No log	48.6667	146	0.9002	0.4913	0.9002	0.9488
No log	49.3333	148	0.8216	0.5155	0.8216	0.9064
No log	50.0	150	0.7192	0.5	0.7192	0.8480
No log	50.6667	152	0.6756	0.5515	0.6756	0.8219
No log	51.3333	154	0.6874	0.5498	0.6874	0.8291
No log	52.0	156	0.7089	0.5245	0.7089	0.8420
No log	52.6667	158	0.6861	0.5341	0.6861	0.8283
No log	53.3333	160	0.6669	0.5162	0.6669	0.8166
No log	54.0	162	0.6678	0.5175	0.6678	0.8172
No log	54.6667	164	0.7092	0.5141	0.7092	0.8421
No log	55.3333	166	0.7692	0.4730	0.7692	0.8770
No log	56.0	168	0.7779	0.4156	0.7779	0.8820
No log	56.6667	170	0.8330	0.3975	0.8330	0.9127
No log	57.3333	172	0.7961	0.4104	0.7961	0.8923
No log	58.0	174	0.7145	0.5035	0.7145	0.8453
No log	58.6667	176	0.6759	0.5159	0.6759	0.8221
No log	59.3333	178	0.6719	0.5159	0.6719	0.8197
No log	60.0	180	0.6749	0.5242	0.6749	0.8216
No log	60.6667	182	0.6978	0.5302	0.6978	0.8353
No log	61.3333	184	0.7359	0.5253	0.7359	0.8578
No log	62.0	186	0.7563	0.5448	0.7563	0.8696
No log	62.6667	188	0.7452	0.5448	0.7452	0.8633
No log	63.3333	190	0.7065	0.5552	0.7065	0.8405
No log	64.0	192	0.6786	0.5517	0.6786	0.8238
No log	64.6667	194	0.6687	0.5587	0.6687	0.8177
No log	65.3333	196	0.7004	0.5228	0.7004	0.8369
No log	66.0	198	0.7167	0.5228	0.7167	0.8466
No log	66.6667	200	0.7327	0.5138	0.7327	0.8560
No log	67.3333	202	0.7706	0.4946	0.7706	0.8778
No log	68.0	204	0.7944	0.4995	0.7944	0.8913
No log	68.6667	206	0.7765	0.5065	0.7765	0.8812
No log	69.3333	208	0.7269	0.5185	0.7269	0.8526
No log	70.0	210	0.6693	0.5610	0.6693	0.8181
No log	70.6667	212	0.6532	0.5427	0.6532	0.8082
No log	71.3333	214	0.6493	0.5599	0.6493	0.8058
No log	72.0	216	0.6607	0.5818	0.6607	0.8129
No log	72.6667	218	0.6760	0.5597	0.6760	0.8222
No log	73.3333	220	0.6780	0.5597	0.6780	0.8234
No log	74.0	222	0.6769	0.5744	0.6769	0.8227
No log	74.6667	224	0.6671	0.5763	0.6671	0.8167
No log	75.3333	226	0.6615	0.5763	0.6615	0.8133
No log	76.0	228	0.6669	0.5576	0.6669	0.8167
No log	76.6667	230	0.6772	0.5269	0.6772	0.8229
No log	77.3333	232	0.6880	0.55	0.6880	0.8294
No log	78.0	234	0.6937	0.5412	0.6937	0.8329
No log	78.6667	236	0.6773	0.5570	0.6773	0.8230
No log	79.3333	238	0.6547	0.5607	0.6547	0.8091
No log	80.0	240	0.6534	0.5589	0.6534	0.8084
No log	80.6667	242	0.6587	0.5538	0.6587	0.8116
No log	81.3333	244	0.6634	0.5506	0.6634	0.8145
No log	82.0	246	0.6711	0.5477	0.6711	0.8192
No log	82.6667	248	0.6724	0.5617	0.6724	0.8200
No log	83.3333	250	0.6700	0.5778	0.6700	0.8185
No log	84.0	252	0.6626	0.6010	0.6626	0.8140
No log	84.6667	254	0.6548	0.6010	0.6548	0.8092
No log	85.3333	256	0.6542	0.5819	0.6542	0.8088
No log	86.0	258	0.6516	0.5873	0.6516	0.8072
No log	86.6667	260	0.6570	0.5358	0.6570	0.8106
No log	87.3333	262	0.6662	0.5359	0.6662	0.8162
No log	88.0	264	0.6673	0.5374	0.6673	0.8169
No log	88.6667	266	0.6587	0.5592	0.6587	0.8116
No log	89.3333	268	0.6560	0.5502	0.6560	0.8100
No log	90.0	270	0.6508	0.5519	0.6508	0.8068
No log	90.6667	272	0.6492	0.5519	0.6492	0.8058
No log	91.3333	274	0.6478	0.5668	0.6478	0.8049
No log	92.0	276	0.6531	0.5519	0.6531	0.8081
No log	92.6667	278	0.6605	0.5147	0.6605	0.8127
No log	93.3333	280	0.6680	0.5147	0.6680	0.8173
No log	94.0	282	0.6705	0.5134	0.6705	0.8189
No log	94.6667	284	0.6688	0.5156	0.6688	0.8178
No log	95.3333	286	0.6657	0.5305	0.6657	0.8159
No log	96.0	288	0.6640	0.5448	0.6640	0.8149
No log	96.6667	290	0.6647	0.5448	0.6647	0.8153
No log	97.3333	292	0.6670	0.5305	0.6670	0.8167
No log	98.0	294	0.6696	0.5156	0.6696	0.8183
No log	98.6667	296	0.6715	0.5156	0.6715	0.8194
No log	99.3333	298	0.6717	0.5156	0.6717	0.8196
No log	100.0	300	0.6714	0.5156	0.6714	0.8194

Framework versions

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