ArabicNewSplits7_usingWellWrittenEssays_FineTuningAraBERT_run1_AugV5_k1_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: 1.9368
• Qwk: -0.1942
• Mse: 1.9368
• Rmse: 1.3917

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	4.4599	0.0010	4.4599	2.1118
No log	1.3333	4	2.5724	0.0066	2.5724	1.6039
No log	2.0	6	2.3677	-0.0685	2.3677	1.5387
No log	2.6667	8	1.7808	0.0035	1.7808	1.3345
No log	3.3333	10	1.5004	-0.0594	1.5004	1.2249
No log	4.0	12	1.4286	-0.0335	1.4286	1.1952
No log	4.6667	14	1.4240	-0.0816	1.4240	1.1933
No log	5.3333	16	1.5315	-0.0537	1.5315	1.2375
No log	6.0	18	1.6428	-0.0711	1.6428	1.2817
No log	6.6667	20	1.6508	-0.1015	1.6508	1.2848
No log	7.3333	22	1.9175	-0.2465	1.9175	1.3848
No log	8.0	24	1.7667	-0.2059	1.7667	1.3292
No log	8.6667	26	1.6441	-0.0256	1.6441	1.2822
No log	9.3333	28	1.8150	-0.1524	1.8150	1.3472
No log	10.0	30	1.9510	-0.2038	1.9510	1.3968
No log	10.6667	32	2.1854	-0.0801	2.1854	1.4783
No log	11.3333	34	2.0962	-0.0580	2.0962	1.4478
No log	12.0	36	1.9203	-0.1882	1.9203	1.3858
No log	12.6667	38	2.1849	-0.0592	2.1849	1.4782
No log	13.3333	40	2.1786	-0.2753	2.1786	1.4760
No log	14.0	42	2.1941	-0.3498	2.1941	1.4812
No log	14.6667	44	1.9980	-0.1483	1.9980	1.4135
No log	15.3333	46	1.8175	-0.0730	1.8175	1.3482
No log	16.0	48	2.0903	-0.1722	2.0903	1.4458
No log	16.6667	50	2.3389	-0.3786	2.3389	1.5294
No log	17.3333	52	2.5643	-0.3677	2.5643	1.6014
No log	18.0	54	2.1079	-0.1369	2.1079	1.4519
No log	18.6667	56	2.0835	-0.1275	2.0835	1.4434
No log	19.3333	58	2.1585	-0.1894	2.1585	1.4692
No log	20.0	60	2.5183	-0.3293	2.5183	1.5869
No log	20.6667	62	2.2618	-0.3096	2.2618	1.5039
No log	21.3333	64	1.8298	-0.0671	1.8298	1.3527
No log	22.0	66	1.9324	-0.1430	1.9324	1.3901
No log	22.6667	68	2.0309	-0.2349	2.0309	1.4251
No log	23.3333	70	2.0520	-0.2624	2.0520	1.4325
No log	24.0	72	1.8595	-0.0476	1.8595	1.3636
No log	24.6667	74	1.8787	-0.0432	1.8787	1.3707
No log	25.3333	76	1.9277	-0.2045	1.9277	1.3884
No log	26.0	78	2.2734	-0.2440	2.2735	1.5078
No log	26.6667	80	2.7183	-0.3127	2.7183	1.6487
No log	27.3333	82	2.6978	-0.3189	2.6978	1.6425
No log	28.0	84	2.3052	-0.2483	2.3052	1.5183
No log	28.6667	86	2.1287	-0.2288	2.1287	1.4590
No log	29.3333	88	1.9380	-0.1124	1.9380	1.3921
No log	30.0	90	2.0258	-0.0870	2.0258	1.4233
No log	30.6667	92	2.2287	-0.2204	2.2287	1.4929
No log	31.3333	94	2.0969	-0.2367	2.0969	1.4481
No log	32.0	96	1.8379	-0.1302	1.8379	1.3557
No log	32.6667	98	1.7817	-0.0903	1.7817	1.3348
No log	33.3333	100	1.7817	-0.0633	1.7817	1.3348
No log	34.0	102	1.9771	-0.1762	1.9771	1.4061
No log	34.6667	104	2.1645	-0.2362	2.1645	1.4712
No log	35.3333	106	2.0305	-0.2506	2.0305	1.4250
No log	36.0	108	1.6737	-0.0090	1.6737	1.2937
No log	36.6667	110	1.5352	0.0792	1.5352	1.2390
No log	37.3333	112	1.6636	0.0169	1.6636	1.2898
No log	38.0	114	1.9562	-0.1426	1.9562	1.3986
No log	38.6667	116	2.2904	-0.2588	2.2904	1.5134
No log	39.3333	118	2.4307	-0.2903	2.4307	1.5591
No log	40.0	120	2.3966	-0.2408	2.3966	1.5481
No log	40.6667	122	2.3310	-0.2554	2.3310	1.5268
No log	41.3333	124	2.2904	-0.2761	2.2904	1.5134
No log	42.0	126	2.2169	-0.3220	2.2169	1.4889
No log	42.6667	128	2.1390	-0.2804	2.1390	1.4625
No log	43.3333	130	2.1488	-0.2629	2.1488	1.4659
No log	44.0	132	2.1594	-0.2629	2.1594	1.4695
No log	44.6667	134	2.0371	-0.2232	2.0371	1.4273
No log	45.3333	136	2.0902	-0.3080	2.0902	1.4458
No log	46.0	138	2.0335	-0.1893	2.0335	1.4260
No log	46.6667	140	1.9684	-0.1341	1.9684	1.4030
No log	47.3333	142	2.1360	-0.2177	2.1360	1.4615
No log	48.0	144	2.2930	-0.3332	2.2930	1.5143
No log	48.6667	146	2.4067	-0.3526	2.4067	1.5513
No log	49.3333	148	2.4153	-0.2737	2.4153	1.5541
No log	50.0	150	2.2728	-0.2765	2.2728	1.5076
No log	50.6667	152	2.0703	-0.2232	2.0703	1.4388
No log	51.3333	154	1.8943	-0.1463	1.8943	1.3763
No log	52.0	156	1.8563	-0.1328	1.8563	1.3625
No log	52.6667	158	1.9017	-0.2291	1.9017	1.3790
No log	53.3333	160	2.0275	-0.2721	2.0275	1.4239
No log	54.0	162	2.2055	-0.2894	2.2055	1.4851
No log	54.6667	164	2.2930	-0.3145	2.2930	1.5143
No log	55.3333	166	2.2043	-0.2652	2.2043	1.4847
No log	56.0	168	2.0018	-0.1929	2.0018	1.4148
No log	56.6667	170	1.8227	-0.1168	1.8227	1.3501
No log	57.3333	172	1.7863	-0.1168	1.7863	1.3365
No log	58.0	174	1.8778	-0.1328	1.8778	1.3703
No log	58.6667	176	1.9534	-0.2029	1.9534	1.3976
No log	59.3333	178	2.1053	-0.2201	2.1053	1.4510
No log	60.0	180	2.1658	-0.2804	2.1658	1.4717
No log	60.6667	182	2.0522	-0.1855	2.0522	1.4326
No log	61.3333	184	1.9257	-0.1002	1.9257	1.3877
No log	62.0	186	1.8384	-0.1168	1.8384	1.3559
No log	62.6667	188	1.7562	-0.1028	1.7562	1.3252
No log	63.3333	190	1.6884	-0.1146	1.6884	1.2994
No log	64.0	192	1.6778	-0.1146	1.6778	1.2953
No log	64.6667	194	1.7330	-0.1064	1.7330	1.3164
No log	65.3333	196	1.8360	-0.1855	1.8360	1.3550
No log	66.0	198	1.8987	-0.2376	1.8987	1.3779
No log	66.6667	200	1.9145	-0.1417	1.9145	1.3836
No log	67.3333	202	1.8629	-0.0805	1.8629	1.3649
No log	68.0	204	1.8047	-0.0805	1.8047	1.3434
No log	68.6667	206	1.8129	-0.0805	1.8129	1.3465
No log	69.3333	208	1.8482	-0.0805	1.8482	1.3595
No log	70.0	210	1.9819	-0.2148	1.9819	1.4078
No log	70.6667	212	2.0915	-0.2980	2.0915	1.4462
No log	71.3333	214	2.1020	-0.2804	2.1020	1.4498
No log	72.0	216	2.0631	-0.2804	2.0631	1.4364
No log	72.6667	218	1.9710	-0.2029	1.9710	1.4039
No log	73.3333	220	1.8901	-0.2029	1.8901	1.3748
No log	74.0	222	1.8653	-0.2029	1.8653	1.3658
No log	74.6667	224	1.8513	-0.2029	1.8513	1.3606
No log	75.3333	226	1.8635	-0.2029	1.8635	1.3651
No log	76.0	228	1.8888	-0.2029	1.8888	1.3744
No log	76.6667	230	1.9012	-0.2029	1.9012	1.3788
No log	77.3333	232	1.8756	-0.1328	1.8756	1.3695
No log	78.0	234	1.8547	-0.0973	1.8547	1.3619
No log	78.6667	236	1.8045	-0.1168	1.8045	1.3433
No log	79.3333	238	1.7595	-0.1080	1.7595	1.3265
No log	80.0	240	1.7000	-0.1018	1.7000	1.3039
No log	80.6667	242	1.6570	-0.1138	1.6570	1.2873
No log	81.3333	244	1.6573	-0.1138	1.6573	1.2874
No log	82.0	246	1.7066	-0.1055	1.7066	1.3064
No log	82.6667	248	1.7771	-0.1146	1.7771	1.3331
No log	83.3333	250	1.8499	-0.1766	1.8499	1.3601
No log	84.0	252	1.8786	-0.1591	1.8786	1.3706
No log	84.6667	254	1.8487	-0.1766	1.8487	1.3597
No log	85.3333	256	1.8225	-0.1146	1.8225	1.3500
No log	86.0	258	1.8051	-0.1146	1.8051	1.3435
No log	86.6667	260	1.7981	-0.1146	1.7981	1.3409
No log	87.3333	262	1.7828	-0.1055	1.7828	1.3352
No log	88.0	264	1.7679	-0.1055	1.7679	1.3296
No log	88.6667	266	1.7678	-0.1055	1.7678	1.3296
No log	89.3333	268	1.7854	-0.1146	1.7854	1.3362
No log	90.0	270	1.8141	-0.1146	1.8141	1.3469
No log	90.6667	272	1.8370	-0.0973	1.8370	1.3554
No log	91.3333	274	1.8526	-0.0973	1.8526	1.3611
No log	92.0	276	1.8636	-0.0973	1.8636	1.3651
No log	92.6667	278	1.8702	-0.0973	1.8702	1.3676
No log	93.3333	280	1.8768	-0.0973	1.8768	1.3700
No log	94.0	282	1.8953	-0.0973	1.8953	1.3767
No log	94.6667	284	1.9110	-0.1591	1.9110	1.3824
No log	95.3333	286	1.9299	-0.1942	1.9299	1.3892
No log	96.0	288	1.9298	-0.1942	1.9298	1.3892
No log	96.6667	290	1.9281	-0.1942	1.9281	1.3886
No log	97.3333	292	1.9328	-0.1942	1.9328	1.3903
No log	98.0	294	1.9327	-0.1942	1.9327	1.3902
No log	98.6667	296	1.9348	-0.1942	1.9348	1.3910
No log	99.3333	298	1.9368	-0.1942	1.9368	1.3917
No log	100.0	300	1.9368	-0.1942	1.9368	1.3917

Framework versions

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