ArabicNewSplits7_FineTuningAraBERT_noAug_task7_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: 0.4626
• Qwk: 0.6158
• Mse: 0.4626
• Rmse: 0.6802

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	2.6700	-0.0262	2.6700	1.6340
No log	1.3333	4	1.5029	0.0364	1.5029	1.2259
No log	2.0	6	0.7173	0.2879	0.7173	0.8470
No log	2.6667	8	0.7574	0.3194	0.7574	0.8703
No log	3.3333	10	0.8797	0.3280	0.8797	0.9379
No log	4.0	12	0.6559	0.4328	0.6559	0.8099
No log	4.6667	14	0.4708	0.5869	0.4708	0.6861
No log	5.3333	16	0.4773	0.5999	0.4773	0.6909
No log	6.0	18	0.4375	0.6759	0.4375	0.6614
No log	6.6667	20	0.4422	0.6771	0.4422	0.6650
No log	7.3333	22	0.4303	0.6683	0.4303	0.6560
No log	8.0	24	0.4416	0.6683	0.4416	0.6645
No log	8.6667	26	0.4701	0.6580	0.4701	0.6856
No log	9.3333	28	0.5629	0.5692	0.5629	0.7502
No log	10.0	30	0.5964	0.5178	0.5964	0.7723
No log	10.6667	32	0.5232	0.5421	0.5232	0.7234
No log	11.3333	34	0.5155	0.5750	0.5155	0.7180
No log	12.0	36	0.5009	0.6096	0.5009	0.7078
No log	12.6667	38	0.4949	0.6235	0.4949	0.7035
No log	13.3333	40	0.5159	0.5692	0.5159	0.7182
No log	14.0	42	0.4424	0.6667	0.4424	0.6651
No log	14.6667	44	0.5664	0.5140	0.5664	0.7526
No log	15.3333	46	0.5353	0.5944	0.5353	0.7316
No log	16.0	48	0.4479	0.5846	0.4479	0.6693
No log	16.6667	50	0.4569	0.6335	0.4569	0.6759
No log	17.3333	52	0.4442	0.5719	0.4442	0.6665
No log	18.0	54	0.4874	0.5577	0.4874	0.6982
No log	18.6667	56	0.4850	0.6415	0.4850	0.6964
No log	19.3333	58	0.4217	0.5941	0.4217	0.6494
No log	20.0	60	0.4734	0.6168	0.4734	0.6880
No log	20.6667	62	0.4757	0.6168	0.4757	0.6897
No log	21.3333	64	0.4165	0.6171	0.4165	0.6454
No log	22.0	66	0.4716	0.6709	0.4716	0.6868
No log	22.6667	68	0.5215	0.6596	0.5215	0.7221
No log	23.3333	70	0.4418	0.6541	0.4418	0.6647
No log	24.0	72	0.4351	0.6555	0.4351	0.6596
No log	24.6667	74	0.4416	0.6351	0.4416	0.6645
No log	25.3333	76	0.4708	0.6346	0.4708	0.6861
No log	26.0	78	0.5544	0.6731	0.5544	0.7446
No log	26.6667	80	0.5948	0.5845	0.5948	0.7712
No log	27.3333	82	0.5343	0.5601	0.5343	0.7310
No log	28.0	84	0.4770	0.5719	0.4770	0.6907
No log	28.6667	86	0.4715	0.5860	0.4715	0.6867
No log	29.3333	88	0.4853	0.5784	0.4853	0.6967
No log	30.0	90	0.5677	0.5665	0.5677	0.7534
No log	30.6667	92	0.6200	0.4664	0.6200	0.7874
No log	31.3333	94	0.6804	0.5239	0.6804	0.8249
No log	32.0	96	0.5698	0.4854	0.5698	0.7548
No log	32.6667	98	0.4941	0.6145	0.4941	0.7029
No log	33.3333	100	0.4988	0.6101	0.4988	0.7062
No log	34.0	102	0.5561	0.4997	0.5561	0.7457
No log	34.6667	104	0.6652	0.4812	0.6652	0.8156
No log	35.3333	106	0.6406	0.5101	0.6406	0.8004
No log	36.0	108	0.5301	0.5442	0.5301	0.7281
No log	36.6667	110	0.4719	0.6351	0.4719	0.6870
No log	37.3333	112	0.4764	0.5941	0.4764	0.6903
No log	38.0	114	0.5241	0.5831	0.5241	0.7240
No log	38.6667	116	0.6512	0.5612	0.6512	0.8070
No log	39.3333	118	0.6787	0.5085	0.6787	0.8238
No log	40.0	120	0.6142	0.5059	0.6142	0.7837
No log	40.6667	122	0.5199	0.5801	0.5199	0.7211
No log	41.3333	124	0.4751	0.5816	0.4751	0.6893
No log	42.0	126	0.4478	0.6068	0.4478	0.6692
No log	42.6667	128	0.4459	0.5888	0.4459	0.6677
No log	43.3333	130	0.4677	0.6431	0.4677	0.6839
No log	44.0	132	0.5440	0.5275	0.5440	0.7376
No log	44.6667	134	0.6516	0.4665	0.6516	0.8072
No log	45.3333	136	0.6939	0.3770	0.6939	0.8330
No log	46.0	138	0.6448	0.4330	0.6448	0.8030
No log	46.6667	140	0.5622	0.5385	0.5622	0.7498
No log	47.3333	142	0.5024	0.5817	0.5024	0.7088
No log	48.0	144	0.4722	0.6251	0.4722	0.6872
No log	48.6667	146	0.4423	0.6359	0.4423	0.6650
No log	49.3333	148	0.4377	0.6171	0.4377	0.6616
No log	50.0	150	0.4650	0.6052	0.4650	0.6819
No log	50.6667	152	0.5206	0.6415	0.5206	0.7215
No log	51.3333	154	0.5803	0.5858	0.5803	0.7617
No log	52.0	156	0.6362	0.6322	0.6362	0.7976
No log	52.6667	158	0.5932	0.6248	0.5932	0.7702
No log	53.3333	160	0.4963	0.5872	0.4963	0.7045
No log	54.0	162	0.4538	0.6241	0.4538	0.6737
No log	54.6667	164	0.4397	0.6643	0.4397	0.6631
No log	55.3333	166	0.4365	0.6747	0.4365	0.6607
No log	56.0	168	0.4359	0.6655	0.4359	0.6602
No log	56.6667	170	0.4342	0.6643	0.4342	0.6589
No log	57.3333	172	0.4453	0.6431	0.4453	0.6673
No log	58.0	174	0.4867	0.5845	0.4867	0.6976
No log	58.6667	176	0.5457	0.5357	0.5457	0.7387
No log	59.3333	178	0.5956	0.5410	0.5956	0.7717
No log	60.0	180	0.6150	0.5609	0.6150	0.7842
No log	60.6667	182	0.6342	0.5026	0.6342	0.7964
No log	61.3333	184	0.6139	0.5609	0.6139	0.7835
No log	62.0	186	0.5778	0.5426	0.5778	0.7602
No log	62.6667	188	0.5440	0.5970	0.5440	0.7375
No log	63.3333	190	0.5293	0.5872	0.5293	0.7276
No log	64.0	192	0.5088	0.5872	0.5088	0.7133
No log	64.6667	194	0.4789	0.6431	0.4789	0.6921
No log	65.3333	196	0.4559	0.6241	0.4559	0.6752
No log	66.0	198	0.4538	0.6158	0.4538	0.6736
No log	66.6667	200	0.4533	0.6254	0.4533	0.6733
No log	67.3333	202	0.4580	0.6431	0.4580	0.6768
No log	68.0	204	0.4668	0.6431	0.4668	0.6832
No log	68.6667	206	0.4777	0.6228	0.4777	0.6912
No log	69.3333	208	0.4862	0.6228	0.4862	0.6973
No log	70.0	210	0.5004	0.5845	0.5004	0.7074
No log	70.6667	212	0.5176	0.5872	0.5176	0.7194
No log	71.3333	214	0.5190	0.5845	0.5190	0.7204
No log	72.0	216	0.5008	0.5647	0.5008	0.7077
No log	72.6667	218	0.5036	0.5845	0.5036	0.7097
No log	73.3333	220	0.4995	0.5845	0.4995	0.7068
No log	74.0	222	0.5093	0.5845	0.5093	0.7137
No log	74.6667	224	0.5206	0.5845	0.5206	0.7215
No log	75.3333	226	0.5242	0.5473	0.5242	0.7240
No log	76.0	228	0.5106	0.5845	0.5106	0.7145
No log	76.6667	230	0.5026	0.5845	0.5026	0.7089
No log	77.3333	232	0.4899	0.6052	0.4899	0.6999
No log	78.0	234	0.4842	0.6052	0.4842	0.6959
No log	78.6667	236	0.4752	0.6431	0.4752	0.6893
No log	79.3333	238	0.4754	0.6346	0.4754	0.6895
No log	80.0	240	0.4817	0.6346	0.4817	0.6940
No log	80.6667	242	0.4952	0.6346	0.4952	0.7037
No log	81.3333	244	0.5071	0.5996	0.5071	0.7121
No log	82.0	246	0.5184	0.6015	0.5184	0.7200
No log	82.6667	248	0.5229	0.6015	0.5229	0.7231
No log	83.3333	250	0.5067	0.6360	0.5067	0.7118
No log	84.0	252	0.4959	0.6158	0.4959	0.7042
No log	84.6667	254	0.4837	0.6158	0.4837	0.6955
No log	85.3333	256	0.4715	0.6158	0.4715	0.6867
No log	86.0	258	0.4602	0.6254	0.4602	0.6784
No log	86.6667	260	0.4563	0.6351	0.4563	0.6755
No log	87.3333	262	0.4557	0.6351	0.4557	0.6751
No log	88.0	264	0.4579	0.6254	0.4579	0.6767
No log	88.6667	266	0.4606	0.6254	0.4606	0.6787
No log	89.3333	268	0.4622	0.6254	0.4622	0.6798
No log	90.0	270	0.4652	0.6158	0.4652	0.6821
No log	90.6667	272	0.4698	0.6158	0.4698	0.6854
No log	91.3333	274	0.4778	0.6158	0.4778	0.6912
No log	92.0	276	0.4899	0.6158	0.4899	0.6999
No log	92.6667	278	0.4995	0.6346	0.4995	0.7068
No log	93.3333	280	0.5031	0.6353	0.5031	0.7093
No log	94.0	282	0.5003	0.6346	0.5003	0.7073
No log	94.6667	284	0.4962	0.6346	0.4962	0.7044
No log	95.3333	286	0.4900	0.6158	0.4900	0.7000
No log	96.0	288	0.4830	0.6158	0.4830	0.6950
No log	96.6667	290	0.4769	0.6158	0.4769	0.6906
No log	97.3333	292	0.4714	0.6158	0.4714	0.6866
No log	98.0	294	0.4679	0.6158	0.4679	0.6840
No log	98.6667	296	0.4652	0.6158	0.4652	0.6821
No log	99.3333	298	0.4633	0.6158	0.4633	0.6807
No log	100.0	300	0.4626	0.6158	0.4626	0.6802

Framework versions

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