ArabicNewSplits7_B_usingALLEssays_FineTuningAraBERT_run3_AugV5_k1_task1_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: 1.8551
• Qwk: 0.3111
• Mse: 1.8551
• Rmse: 1.3620

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	6.5473	0.0308	6.5473	2.5588
No log	1.3333	4	5.3659	0.0070	5.3659	2.3164
No log	2.0	6	3.3478	0.1170	3.3478	1.8297
No log	2.6667	8	2.5074	0.0519	2.5074	1.5835
No log	3.3333	10	2.0115	0.2188	2.0115	1.4183
No log	4.0	12	1.6361	0.1538	1.6361	1.2791
No log	4.6667	14	1.7159	0.1132	1.7159	1.3099
No log	5.3333	16	1.7964	0.1455	1.7964	1.3403
No log	6.0	18	1.7458	0.1441	1.7458	1.3213
No log	6.6667	20	1.8439	0.2636	1.8439	1.3579
No log	7.3333	22	1.7747	0.1770	1.7747	1.3322
No log	8.0	24	1.7532	0.1308	1.7532	1.3241
No log	8.6667	26	1.7358	0.1441	1.7358	1.3175
No log	9.3333	28	1.7776	0.2810	1.7776	1.3332
No log	10.0	30	1.7248	0.1404	1.7248	1.3133
No log	10.6667	32	1.7526	0.1053	1.7526	1.3239
No log	11.3333	34	1.8045	0.2344	1.8045	1.3433
No log	12.0	36	1.7427	0.2051	1.7427	1.3201
No log	12.6667	38	1.7656	0.2439	1.7656	1.3288
No log	13.3333	40	2.0262	0.1739	2.0262	1.4234
No log	14.0	42	2.1099	0.1857	2.1099	1.4525
No log	14.6667	44	1.7350	0.3231	1.7350	1.3172
No log	15.3333	46	1.6523	0.375	1.6523	1.2854
No log	16.0	48	1.8760	0.2353	1.8760	1.3697
No log	16.6667	50	2.1235	0.1986	2.1235	1.4572
No log	17.3333	52	1.8114	0.2353	1.8114	1.3459
No log	18.0	54	1.4962	0.3902	1.4962	1.2232
No log	18.6667	56	1.5295	0.3871	1.5295	1.2367
No log	19.3333	58	1.7741	0.3284	1.7741	1.3320
No log	20.0	60	2.3079	0.1379	2.3079	1.5192
No log	20.6667	62	2.3187	0.1379	2.3187	1.5227
No log	21.3333	64	2.0474	0.2113	2.0474	1.4309
No log	22.0	66	1.7421	0.3664	1.7421	1.3199
No log	22.6667	68	1.6194	0.3780	1.6194	1.2725
No log	23.3333	70	1.6458	0.3692	1.6458	1.2829
No log	24.0	72	1.9184	0.2628	1.9184	1.3851
No log	24.6667	74	2.1413	0.2449	2.1413	1.4633
No log	25.3333	76	2.0653	0.2378	2.0653	1.4371
No log	26.0	78	2.0062	0.2143	2.0062	1.4164
No log	26.6667	80	1.7957	0.2482	1.7957	1.3401
No log	27.3333	82	1.5657	0.3817	1.5657	1.2513
No log	28.0	84	1.4822	0.375	1.4822	1.2174
No log	28.6667	86	1.6361	0.3817	1.6361	1.2791
No log	29.3333	88	1.7877	0.2963	1.7877	1.3370
No log	30.0	90	1.8376	0.2941	1.8376	1.3556
No log	30.6667	92	1.7934	0.3284	1.7934	1.3392
No log	31.3333	94	1.8570	0.2464	1.8570	1.3627
No log	32.0	96	1.8573	0.2609	1.8573	1.3628
No log	32.6667	98	1.8530	0.3433	1.8530	1.3612
No log	33.3333	100	1.9053	0.3088	1.9053	1.3803
No log	34.0	102	1.9388	0.2302	1.9388	1.3924
No log	34.6667	104	1.9625	0.2302	1.9625	1.4009
No log	35.3333	106	1.9460	0.2000	1.9460	1.3950
No log	36.0	108	1.8731	0.3259	1.8731	1.3686
No log	36.6667	110	1.7865	0.3636	1.7865	1.3366
No log	37.3333	112	1.7933	0.3308	1.7933	1.3391
No log	38.0	114	1.7407	0.3511	1.7407	1.3193
No log	38.6667	116	1.6915	0.3511	1.6915	1.3006
No log	39.3333	118	1.7202	0.3182	1.7202	1.3116
No log	40.0	120	1.6670	0.3359	1.6670	1.2911
No log	40.6667	122	1.6617	0.3359	1.6617	1.2891
No log	41.3333	124	1.6344	0.3538	1.6344	1.2784
No log	42.0	126	1.5475	0.3937	1.5475	1.2440
No log	42.6667	128	1.5496	0.3937	1.5496	1.2448
No log	43.3333	130	1.6581	0.3182	1.6581	1.2877
No log	44.0	132	1.8803	0.2941	1.8803	1.3713
No log	44.6667	134	2.0202	0.2302	2.0202	1.4213
No log	45.3333	136	2.1635	0.1818	2.1635	1.4709
No log	46.0	138	2.2456	0.1818	2.2456	1.4985
No log	46.6667	140	2.2539	0.1528	2.2539	1.5013
No log	47.3333	142	2.0387	0.2143	2.0387	1.4278
No log	48.0	144	1.7222	0.3308	1.7222	1.3123
No log	48.6667	146	1.5591	0.3968	1.5591	1.2486
No log	49.3333	148	1.5271	0.4355	1.5271	1.2358
No log	50.0	150	1.6247	0.3692	1.6247	1.2746
No log	50.6667	152	1.8538	0.2963	1.8538	1.3615
No log	51.3333	154	2.0903	0.2000	2.0903	1.4458
No log	52.0	156	2.1868	0.1818	2.1868	1.4788
No log	52.6667	158	2.0665	0.2000	2.0665	1.4375
No log	53.3333	160	1.8563	0.2815	1.8563	1.3624
No log	54.0	162	1.7314	0.3308	1.7314	1.3158
No log	54.6667	164	1.7120	0.3308	1.7120	1.3084
No log	55.3333	166	1.7652	0.3308	1.7652	1.3286
No log	56.0	168	1.8420	0.3134	1.8420	1.3572
No log	56.6667	170	1.9523	0.2158	1.9523	1.3973
No log	57.3333	172	2.0651	0.2158	2.0651	1.4371
No log	58.0	174	2.0864	0.2000	2.0864	1.4444
No log	58.6667	176	2.0769	0.2000	2.0769	1.4411
No log	59.3333	178	2.0046	0.2158	2.0046	1.4159
No log	60.0	180	1.9135	0.2774	1.9135	1.3833
No log	60.6667	182	1.8176	0.3308	1.8176	1.3482
No log	61.3333	184	1.7696	0.3308	1.7696	1.3302
No log	62.0	186	1.7900	0.3308	1.7900	1.3379
No log	62.6667	188	1.8225	0.3308	1.8225	1.3500
No log	63.3333	190	1.8351	0.3308	1.8351	1.3546
No log	64.0	192	1.8584	0.2985	1.8584	1.3632
No log	64.6667	194	1.8954	0.2985	1.8954	1.3767
No log	65.3333	196	1.9361	0.2482	1.9361	1.3915
No log	66.0	198	1.9806	0.2482	1.9806	1.4073
No log	66.6667	200	2.0577	0.2000	2.0577	1.4345
No log	67.3333	202	2.1366	0.1702	2.1366	1.4617
No log	68.0	204	2.1558	0.1702	2.1558	1.4683
No log	68.6667	206	2.1078	0.2000	2.1078	1.4518
No log	69.3333	208	2.1196	0.2000	2.1196	1.4559
No log	70.0	210	2.1170	0.2000	2.1170	1.4550
No log	70.6667	212	2.1570	0.1549	2.1570	1.4687
No log	71.3333	214	2.1069	0.2000	2.1069	1.4515
No log	72.0	216	1.9835	0.2464	1.9835	1.4084
No log	72.6667	218	1.8832	0.2815	1.8832	1.3723
No log	73.3333	220	1.8152	0.3308	1.8152	1.3473
No log	74.0	222	1.7260	0.3511	1.7260	1.3138
No log	74.6667	224	1.7209	0.3511	1.7209	1.3118
No log	75.3333	226	1.7022	0.3566	1.7022	1.3047
No log	76.0	228	1.6576	0.3566	1.6576	1.2875
No log	76.6667	230	1.6154	0.3968	1.6154	1.2710
No log	77.3333	232	1.5842	0.4032	1.5842	1.2587
No log	78.0	234	1.5813	0.4032	1.5813	1.2575
No log	78.6667	236	1.5943	0.3968	1.5943	1.2626
No log	79.3333	238	1.6068	0.375	1.6068	1.2676
No log	80.0	240	1.6556	0.3566	1.6556	1.2867
No log	80.6667	242	1.7461	0.3308	1.7461	1.3214
No log	81.3333	244	1.8446	0.2815	1.8446	1.3582
No log	82.0	246	1.9356	0.2774	1.9356	1.3912
No log	82.6667	248	2.0129	0.2302	2.0129	1.4188
No log	83.3333	250	2.0295	0.2302	2.0295	1.4246
No log	84.0	252	2.0599	0.2000	2.0599	1.4352
No log	84.6667	254	2.0856	0.2000	2.0856	1.4442
No log	85.3333	256	2.0987	0.2000	2.0987	1.4487
No log	86.0	258	2.1176	0.2000	2.1176	1.4552
No log	86.6667	260	2.1138	0.2000	2.1138	1.4539
No log	87.3333	262	2.1225	0.1844	2.1225	1.4569
No log	88.0	264	2.1331	0.1844	2.1331	1.4605
No log	88.6667	266	2.1397	0.1844	2.1397	1.4628
No log	89.3333	268	2.1233	0.1844	2.1233	1.4571
No log	90.0	270	2.0984	0.2000	2.0984	1.4486
No log	90.6667	272	2.0630	0.2000	2.0630	1.4363
No log	91.3333	274	2.0330	0.2000	2.0330	1.4258
No log	92.0	276	1.9921	0.2609	1.9921	1.4114
No log	92.6667	278	1.9659	0.2609	1.9659	1.4021
No log	93.3333	280	1.9502	0.2609	1.9502	1.3965
No log	94.0	282	1.9230	0.2774	1.9230	1.3867
No log	94.6667	284	1.9016	0.2774	1.9016	1.3790
No log	95.3333	286	1.8892	0.2774	1.8892	1.3745
No log	96.0	288	1.8812	0.2774	1.8812	1.3716
No log	96.6667	290	1.8733	0.2794	1.8733	1.3687
No log	97.3333	292	1.8680	0.2794	1.8680	1.3667
No log	98.0	294	1.8640	0.2794	1.8640	1.3653
No log	98.6667	296	1.8599	0.2794	1.8599	1.3638
No log	99.3333	298	1.8566	0.3111	1.8566	1.3626
No log	100.0	300	1.8551	0.3111	1.8551	1.3620

Framework versions

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