ArabicNewSplits7_usingWellWrittenEssays_FineTuningAraBERT_run3_AugV5_k1_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: 1.5317
• Qwk: -0.0901
• Mse: 1.5317
• Rmse: 1.2376

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	3.7150	-0.0057	3.7150	1.9274
No log	1.3333	4	2.2429	0.0431	2.2429	1.4976
No log	2.0	6	1.6550	0.0147	1.6550	1.2865
No log	2.6667	8	1.5632	-0.0051	1.5632	1.2503
No log	3.3333	10	1.2145	0.0391	1.2145	1.1020
No log	4.0	12	1.4622	0.0584	1.4622	1.2092
No log	4.6667	14	1.3739	-0.0245	1.3739	1.1721
No log	5.3333	16	1.8290	0.0245	1.8290	1.3524
No log	6.0	18	2.7398	-0.0444	2.7398	1.6552
No log	6.6667	20	2.4443	0.0085	2.4443	1.5634
No log	7.3333	22	1.7095	0.0200	1.7095	1.3075
No log	8.0	24	1.7826	0.0181	1.7826	1.3351
No log	8.6667	26	2.3441	-0.0350	2.3441	1.5311
No log	9.3333	28	1.8005	0.0163	1.8005	1.3418
No log	10.0	30	1.3685	-0.0096	1.3685	1.1698
No log	10.6667	32	1.5163	-0.0756	1.5163	1.2314
No log	11.3333	34	2.4190	-0.0593	2.4190	1.5553
No log	12.0	36	2.3929	-0.0433	2.3929	1.5469
No log	12.6667	38	1.9318	-0.0404	1.9318	1.3899
No log	13.3333	40	1.2396	-0.0735	1.2396	1.1134
No log	14.0	42	1.0659	-0.0818	1.0659	1.0324
No log	14.6667	44	1.1602	-0.0352	1.1602	1.0771
No log	15.3333	46	1.8322	0.0148	1.8322	1.3536
No log	16.0	48	2.4496	-0.0143	2.4496	1.5651
No log	16.6667	50	2.2215	-0.0286	2.2215	1.4905
No log	17.3333	52	1.6471	-0.0165	1.6471	1.2834
No log	18.0	54	1.4077	-0.0237	1.4077	1.1865
No log	18.6667	56	1.4228	-0.0237	1.4228	1.1928
No log	19.3333	58	1.8944	-0.0688	1.8944	1.3764
No log	20.0	60	1.9516	-0.0688	1.9516	1.3970
No log	20.6667	62	1.5406	-0.0366	1.5406	1.2412
No log	21.3333	64	1.1813	-0.0142	1.1813	1.0869
No log	22.0	66	1.3253	0.0042	1.3253	1.1512
No log	22.6667	68	1.7462	-0.0458	1.7462	1.3215
No log	23.3333	70	1.9611	-0.0461	1.9611	1.4004
No log	24.0	72	1.8019	-0.0236	1.8019	1.3423
No log	24.6667	74	1.6535	-0.1064	1.6535	1.2859
No log	25.3333	76	1.5697	-0.0544	1.5697	1.2529
No log	26.0	78	1.8363	-0.0625	1.8363	1.3551
No log	26.6667	80	2.1476	-0.0633	2.1476	1.4655
No log	27.3333	82	2.1876	-0.0633	2.1876	1.4791
No log	28.0	84	1.8772	-0.0069	1.8772	1.3701
No log	28.6667	86	1.3597	-0.0288	1.3597	1.1661
No log	29.3333	88	1.1790	-0.0204	1.1790	1.0858
No log	30.0	90	1.3102	-0.0920	1.3102	1.1447
No log	30.6667	92	1.5957	-0.0230	1.5957	1.2632
No log	31.3333	94	1.7821	0.0154	1.7821	1.3350
No log	32.0	96	1.5949	-0.0146	1.5949	1.2629
No log	32.6667	98	1.6306	-0.0080	1.6306	1.2769
No log	33.3333	100	1.9167	-0.0242	1.9167	1.3844
No log	34.0	102	2.2213	-0.0457	2.2213	1.4904
No log	34.6667	104	2.0252	-0.0443	2.0252	1.4231
No log	35.3333	106	1.6688	0.0013	1.6688	1.2918
No log	36.0	108	1.5543	0.0068	1.5543	1.2467
No log	36.6667	110	1.4571	-0.1150	1.4571	1.2071
No log	37.3333	112	1.4384	-0.0630	1.4384	1.1993
No log	38.0	114	1.7029	0.0186	1.7029	1.3050
No log	38.6667	116	1.7560	0.0365	1.7560	1.3251
No log	39.3333	118	1.4946	-0.0412	1.4946	1.2225
No log	40.0	120	1.3933	-0.0324	1.3933	1.1804
No log	40.6667	122	1.4620	-0.0412	1.4620	1.2091
No log	41.3333	124	1.3784	-0.0324	1.3784	1.1741
No log	42.0	126	1.3684	-0.0629	1.3684	1.1698
No log	42.6667	128	1.4520	-0.0443	1.4520	1.2050
No log	43.3333	130	1.4436	-0.0443	1.4436	1.2015
No log	44.0	132	1.4337	-0.0453	1.4337	1.1974
No log	44.6667	134	1.4440	-0.0453	1.4440	1.2017
No log	45.3333	136	1.4290	-0.0453	1.4290	1.1954
No log	46.0	138	1.3906	-0.0621	1.3906	1.1792
No log	46.6667	140	1.3843	-0.0344	1.3843	1.1766
No log	47.3333	142	1.4342	-0.0119	1.4342	1.1976
No log	48.0	144	1.4955	-0.0452	1.4955	1.2229
No log	48.6667	146	1.5797	-0.0452	1.5797	1.2569
No log	49.3333	148	1.4263	-0.0453	1.4263	1.1943
No log	50.0	150	1.2363	-0.0268	1.2363	1.1119
No log	50.6667	152	1.2701	0.0025	1.2701	1.1270
No log	51.3333	154	1.3753	-0.0947	1.3753	1.1727
No log	52.0	156	1.6379	-0.0451	1.6379	1.2798
No log	52.6667	158	1.7100	-0.0681	1.7100	1.3077
No log	53.3333	160	1.5299	-0.0452	1.5299	1.2369
No log	54.0	162	1.3191	-0.0029	1.3191	1.1485
No log	54.6667	164	1.2735	-0.0297	1.2735	1.1285
No log	55.3333	166	1.3827	-0.0665	1.3827	1.1759
No log	56.0	168	1.4652	-0.0697	1.4652	1.2105
No log	56.6667	170	1.5923	-0.0712	1.5923	1.2619
No log	57.3333	172	1.6058	-0.0712	1.6058	1.2672
No log	58.0	174	1.4698	-0.0712	1.4698	1.2123
No log	58.6667	176	1.4160	-0.0966	1.4160	1.1900
No log	59.3333	178	1.3308	-0.0647	1.3308	1.1536
No log	60.0	180	1.2144	-0.0204	1.2144	1.1020
No log	60.6667	182	1.1694	-0.0187	1.1694	1.0814
No log	61.3333	184	1.2197	-0.0862	1.2197	1.1044
No log	62.0	186	1.2461	-0.0268	1.2461	1.1163
No log	62.6667	188	1.3906	-0.0942	1.3906	1.1792
No log	63.3333	190	1.5419	-0.0449	1.5419	1.2418
No log	64.0	192	1.7031	0.0171	1.7031	1.3050
No log	64.6667	194	1.8381	0.0349	1.8381	1.3558
No log	65.3333	196	1.7873	0.0365	1.7873	1.3369
No log	66.0	198	1.6840	-0.0025	1.6840	1.2977
No log	66.6667	200	1.6421	-0.0025	1.6421	1.2814
No log	67.3333	202	1.6158	-0.0025	1.6158	1.2711
No log	68.0	204	1.5262	-0.0645	1.5262	1.2354
No log	68.6667	206	1.4844	-0.0860	1.4844	1.2183
No log	69.3333	208	1.4413	-0.0622	1.4413	1.2005
No log	70.0	210	1.3663	-0.0315	1.3663	1.1689
No log	70.6667	212	1.2820	-0.0550	1.2820	1.1322
No log	71.3333	214	1.2990	-0.0582	1.2990	1.1397
No log	72.0	216	1.3979	-0.0898	1.3979	1.1823
No log	72.6667	218	1.5095	-0.0440	1.5095	1.2286
No log	73.3333	220	1.5965	-0.0448	1.5965	1.2635
No log	74.0	222	1.5875	-0.0448	1.5875	1.2599
No log	74.6667	224	1.5369	-0.0431	1.5369	1.2397
No log	75.3333	226	1.5201	-0.0431	1.5201	1.2329
No log	76.0	228	1.5008	-0.0431	1.5008	1.2251
No log	76.6667	230	1.5483	-0.0440	1.5483	1.2443
No log	77.3333	232	1.5814	-0.0448	1.5814	1.2576
No log	78.0	234	1.6552	-0.0025	1.6552	1.2865
No log	78.6667	236	1.6739	-0.0025	1.6739	1.2938
No log	79.3333	238	1.6989	-0.0025	1.6989	1.3034
No log	80.0	240	1.6782	-0.0025	1.6782	1.2954
No log	80.6667	242	1.6360	-0.0025	1.6360	1.2791
No log	81.3333	244	1.5600	-0.0440	1.5600	1.2490
No log	82.0	246	1.4731	-0.0880	1.4731	1.2137
No log	82.6667	248	1.4346	-0.1192	1.4346	1.1978
No log	83.3333	250	1.3932	-0.1186	1.3932	1.1803
No log	84.0	252	1.3963	-0.1186	1.3963	1.1817
No log	84.6667	254	1.4310	-0.0908	1.4310	1.1963
No log	85.3333	256	1.4471	-0.0896	1.4471	1.2029
No log	86.0	258	1.4580	-0.0880	1.4580	1.2075
No log	86.6667	260	1.4739	-0.0880	1.4739	1.2140
No log	87.3333	262	1.4917	-0.0880	1.4917	1.2213
No log	88.0	264	1.4888	-0.0913	1.4888	1.2201
No log	88.6667	266	1.4861	-0.0913	1.4861	1.2191
No log	89.3333	268	1.5024	-0.0880	1.5024	1.2257
No log	90.0	270	1.5094	-0.0880	1.5094	1.2286
No log	90.6667	272	1.5003	-0.0896	1.5003	1.2249
No log	91.3333	274	1.4941	-0.0913	1.4941	1.2223
No log	92.0	276	1.4922	-0.0913	1.4922	1.2216
No log	92.6667	278	1.4969	-0.0913	1.4969	1.2235
No log	93.3333	280	1.5022	-0.0913	1.5022	1.2256
No log	94.0	282	1.4975	-0.0913	1.4975	1.2237
No log	94.6667	284	1.5072	-0.0913	1.5072	1.2277
No log	95.3333	286	1.5210	-0.0913	1.5210	1.2333
No log	96.0	288	1.5242	-0.0918	1.5242	1.2346
No log	96.6667	290	1.5339	-0.0901	1.5339	1.2385
No log	97.3333	292	1.5380	-0.0885	1.5380	1.2402
No log	98.0	294	1.5389	-0.0885	1.5389	1.2405
No log	98.6667	296	1.5356	-0.0901	1.5356	1.2392
No log	99.3333	298	1.5331	-0.0901	1.5331	1.2382
No log	100.0	300	1.5317	-0.0901	1.5317	1.2376

Framework versions

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