ArabicNewSplits7_usingWellWrittenEssays_FineTuningAraBERT_run2_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: 1.5330
• Qwk: -0.0885
• Mse: 1.5330
• Rmse: 1.2381

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	1.8006	0.0163	1.8006	1.3418
No log	10.0	30	1.3684	-0.0096	1.3684	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	2.3930	-0.0433	2.3930	1.5469
No log	12.6667	38	1.9317	-0.0404	1.9317	1.3899
No log	13.3333	40	1.2396	-0.0735	1.2396	1.1134
No log	14.0	42	1.0660	-0.0818	1.0660	1.0324
No log	14.6667	44	1.1604	-0.0352	1.1604	1.0772
No log	15.3333	46	1.8329	0.0148	1.8329	1.3539
No log	16.0	48	2.4501	-0.0143	2.4501	1.5653
No log	16.6667	50	2.2218	-0.0286	2.2218	1.4906
No log	17.3333	52	1.6470	-0.0165	1.6470	1.2834
No log	18.0	54	1.4077	-0.0237	1.4077	1.1865
No log	18.6667	56	1.4229	-0.0237	1.4229	1.1929
No log	19.3333	58	1.8946	-0.0688	1.8946	1.3765
No log	20.0	60	1.9517	-0.0688	1.9517	1.3970
No log	20.6667	62	1.5407	-0.0366	1.5407	1.2413
No log	21.3333	64	1.1813	-0.0142	1.1813	1.0869
No log	22.0	66	1.3252	0.0042	1.3252	1.1512
No log	22.6667	68	1.7463	-0.0458	1.7463	1.3215
No log	23.3333	70	1.9613	-0.0461	1.9613	1.4004
No log	24.0	72	1.8016	-0.0236	1.8016	1.3422
No log	24.6667	74	1.6531	-0.1064	1.6531	1.2857
No log	25.3333	76	1.5697	-0.0288	1.5697	1.2529
No log	26.0	78	1.8366	-0.0625	1.8366	1.3552
No log	26.6667	80	2.1480	-0.0633	2.1480	1.4656
No log	27.3333	82	2.1879	-0.0633	2.1879	1.4792
No log	28.0	84	1.8770	-0.0069	1.8770	1.3700
No log	28.6667	86	1.3593	-0.0288	1.3593	1.1659
No log	29.3333	88	1.1785	-0.0204	1.1785	1.0856
No log	30.0	90	1.3096	-0.0920	1.3096	1.1444
No log	30.6667	92	1.5952	-0.0230	1.5952	1.2630
No log	31.3333	94	1.7814	0.0154	1.7814	1.3347
No log	32.0	96	1.5944	-0.0146	1.5944	1.2627
No log	32.6667	98	1.6305	-0.0080	1.6305	1.2769
No log	33.3333	100	1.9172	-0.0242	1.9172	1.3846
No log	34.0	102	2.2228	-0.0457	2.2228	1.4909
No log	34.6667	104	2.0270	-0.0443	2.0270	1.4237
No log	35.3333	106	1.6676	0.0013	1.6676	1.2913
No log	36.0	108	1.5496	0.0068	1.5496	1.2448
No log	36.6667	110	1.4546	-0.1150	1.4546	1.2061
No log	37.3333	112	1.4394	-0.0630	1.4394	1.1997
No log	38.0	114	1.7047	0.0186	1.7047	1.3056
No log	38.6667	116	1.7555	0.0365	1.7555	1.3249
No log	39.3333	118	1.4929	-0.0412	1.4929	1.2218
No log	40.0	120	1.3908	-0.0324	1.3908	1.1793
No log	40.6667	122	1.4592	-0.0412	1.4592	1.2080
No log	41.3333	124	1.3763	-0.0324	1.3763	1.1731
No log	42.0	126	1.3700	-0.0629	1.3700	1.1705
No log	42.6667	128	1.4514	-0.0443	1.4514	1.2047
No log	43.3333	130	1.4416	-0.0443	1.4416	1.2007
No log	44.0	132	1.4298	-0.0712	1.4298	1.1957
No log	44.6667	134	1.4377	-0.0453	1.4377	1.1990
No log	45.3333	136	1.4203	-0.0453	1.4203	1.1918
No log	46.0	138	1.3841	-0.0629	1.3841	1.1765
No log	46.6667	140	1.3792	-0.0344	1.3792	1.1744
No log	47.3333	142	1.4287	-0.0119	1.4287	1.1953
No log	48.0	144	1.4893	-0.0452	1.4893	1.2204
No log	48.6667	146	1.5718	-0.0452	1.5718	1.2537
No log	49.3333	148	1.4197	-0.0687	1.4197	1.1915
No log	50.0	150	1.2319	-0.0568	1.2319	1.1099
No log	50.6667	152	1.2680	0.0025	1.2680	1.1261
No log	51.3333	154	1.3795	-0.0947	1.3795	1.1745
No log	52.0	156	1.6447	-0.0451	1.6447	1.2824
No log	52.6667	158	1.7140	-0.0681	1.7140	1.3092
No log	53.3333	160	1.5299	-0.0452	1.5299	1.2369
No log	54.0	162	1.3123	-0.0029	1.3123	1.1455
No log	54.6667	164	1.2616	0.0025	1.2616	1.1232
No log	55.3333	166	1.3681	-0.0647	1.3681	1.1696
No log	56.0	168	1.4572	-0.0697	1.4572	1.2071
No log	56.6667	170	1.5916	-0.0712	1.5916	1.2616
No log	57.3333	172	1.6030	-0.0712	1.6030	1.2661
No log	58.0	174	1.4628	-0.0966	1.4628	1.2095
No log	58.6667	176	1.4087	-0.0966	1.4087	1.1869
No log	59.3333	178	1.3296	-0.0647	1.3296	1.1531
No log	60.0	180	1.2196	-0.0204	1.2196	1.1043
No log	60.6667	182	1.1756	-0.0187	1.1756	1.0843
No log	61.3333	184	1.2247	-0.0862	1.2247	1.1067
No log	62.0	186	1.2495	-0.0268	1.2495	1.1178
No log	62.6667	188	1.3921	-0.0947	1.3921	1.1799
No log	63.3333	190	1.5428	-0.0449	1.5428	1.2421
No log	64.0	192	1.7045	0.0365	1.7045	1.3056
No log	64.6667	194	1.8424	0.0130	1.8424	1.3573
No log	65.3333	196	1.7952	0.0365	1.7952	1.3398
No log	66.0	198	1.6889	-0.0025	1.6889	1.2996
No log	66.6667	200	1.6414	-0.0025	1.6414	1.2812
No log	67.3333	202	1.6129	-0.0025	1.6129	1.2700
No log	68.0	204	1.5220	-0.0645	1.5220	1.2337
No log	68.6667	206	1.4838	-0.0860	1.4838	1.2181
No log	69.3333	208	1.4428	-0.0630	1.4428	1.2012
No log	70.0	210	1.3701	-0.0611	1.3701	1.1705
No log	70.6667	212	1.2879	-0.0550	1.2879	1.1349
No log	71.3333	214	1.3061	-0.0582	1.3061	1.1428
No log	72.0	216	1.4053	-0.1160	1.4053	1.1854
No log	72.6667	218	1.5148	-0.0440	1.5148	1.2308
No log	73.3333	220	1.5998	-0.0448	1.5998	1.2648
No log	74.0	222	1.5891	-0.0448	1.5891	1.2606
No log	74.6667	224	1.5375	-0.0431	1.5375	1.2400
No log	75.3333	226	1.5213	-0.0431	1.5213	1.2334
No log	76.0	228	1.5058	-0.0431	1.5058	1.2271
No log	76.6667	230	1.5548	-0.0440	1.5548	1.2469
No log	77.3333	232	1.5895	-0.0448	1.5895	1.2608
No log	78.0	234	1.6629	-0.0025	1.6629	1.2895
No log	78.6667	236	1.6802	-0.0025	1.6802	1.2962
No log	79.3333	238	1.7038	-0.0025	1.7038	1.3053
No log	80.0	240	1.6813	-0.0025	1.6813	1.2967
No log	80.6667	242	1.6391	-0.0025	1.6391	1.2803
No log	81.3333	244	1.5635	-0.0440	1.5635	1.2504
No log	82.0	246	1.4783	-0.0880	1.4783	1.2159
No log	82.6667	248	1.4411	-0.1138	1.4411	1.2005
No log	83.3333	250	1.4004	-0.1188	1.4004	1.1834
No log	84.0	252	1.4030	-0.1188	1.4030	1.1845
No log	84.6667	254	1.4359	-0.0880	1.4359	1.1983
No log	85.3333	256	1.4506	-0.0880	1.4506	1.2044
No log	86.0	258	1.4603	-0.0880	1.4603	1.2084
No log	86.6667	260	1.4757	-0.0880	1.4757	1.2148
No log	87.3333	262	1.4934	-0.0865	1.4934	1.2220
No log	88.0	264	1.4908	-0.0880	1.4908	1.2210
No log	88.6667	266	1.4896	-0.0880	1.4896	1.2205
No log	89.3333	268	1.5073	-0.0865	1.5073	1.2277
No log	90.0	270	1.5139	-0.0865	1.5139	1.2304
No log	90.6667	272	1.5041	-0.0880	1.5041	1.2264
No log	91.3333	274	1.4969	-0.0913	1.4969	1.2235
No log	92.0	276	1.4942	-0.0913	1.4942	1.2224
No log	92.6667	278	1.4984	-0.0913	1.4984	1.2241
No log	93.3333	280	1.5035	-0.0913	1.5035	1.2262
No log	94.0	282	1.4987	-0.0913	1.4987	1.2242
No log	94.6667	284	1.5085	-0.0913	1.5085	1.2282
No log	95.3333	286	1.5220	-0.0896	1.5220	1.2337
No log	96.0	288	1.5250	-0.0896	1.5250	1.2349
No log	96.6667	290	1.5347	-0.0870	1.5347	1.2388
No log	97.3333	292	1.5390	-0.0870	1.5390	1.2405
No log	98.0	294	1.5400	-0.0870	1.5400	1.2410
No log	98.6667	296	1.5368	-0.0870	1.5368	1.2397
No log	99.3333	298	1.5344	-0.0885	1.5344	1.2387
No log	100.0	300	1.5330	-0.0885	1.5330	1.2381

Framework versions

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