Arabic_FineTuningAraBERT_AugV5_k2_task1_organization_fold1

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: 0.6928
• Qwk: 0.6488
• Mse: 0.6928
• Rmse: 0.8324

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.0645	2	3.2588	0.0755	3.2588	1.8052
No log	0.1290	4	2.2185	0.1881	2.2185	1.4894
No log	0.1935	6	1.8494	0.0	1.8494	1.3599
No log	0.2581	8	1.4947	0.0	1.4947	1.2226
No log	0.3226	10	1.1893	0.0	1.1893	1.0906
No log	0.3871	12	1.0582	0.0	1.0582	1.0287
No log	0.4516	14	1.0554	0.0	1.0554	1.0273
No log	0.5161	16	1.1632	0.0	1.1632	1.0785
No log	0.5806	18	1.2129	0.0	1.2129	1.1013
No log	0.6452	20	1.1313	0.0	1.1313	1.0636
No log	0.7097	22	1.0789	0.0	1.0789	1.0387
No log	0.7742	24	1.0381	0.0187	1.0381	1.0189
No log	0.8387	26	0.9852	0.2146	0.9852	0.9926
No log	0.9032	28	0.9659	0.2613	0.9659	0.9828
No log	0.9677	30	0.9620	0.2376	0.9620	0.9808
No log	1.0323	32	0.9632	0.1910	0.9632	0.9814
No log	1.0968	34	0.9409	0.1910	0.9409	0.9700
No log	1.1613	36	0.8438	0.2632	0.8438	0.9186
No log	1.2258	38	0.7711	0.2921	0.7711	0.8781
No log	1.2903	40	0.7565	0.4199	0.7565	0.8698
No log	1.3548	42	0.8360	0.2613	0.8360	0.9144
No log	1.4194	44	1.0154	0.2146	1.0154	1.0077
No log	1.4839	46	1.0170	0.2146	1.0170	1.0085
No log	1.5484	48	0.9211	0.2146	0.9211	0.9597
No log	1.6129	50	0.8266	0.4286	0.8266	0.9091
No log	1.6774	52	0.7388	0.3913	0.7388	0.8595
No log	1.7419	54	0.7320	0.4199	0.7320	0.8556
No log	1.8065	56	0.7463	0.3708	0.7463	0.8639
No log	1.8710	58	0.7903	0.3834	0.7903	0.8890
No log	1.9355	60	0.8218	0.4286	0.8218	0.9066
No log	2.0	62	0.8105	0.4	0.8105	0.9003
No log	2.0645	64	0.7506	0.3636	0.7506	0.8663
No log	2.1290	66	0.7074	0.3425	0.7074	0.8411
No log	2.1935	68	0.6949	0.4	0.6949	0.8336
No log	2.2581	70	0.6923	0.4	0.6923	0.8321
No log	2.3226	72	0.6849	0.3708	0.6849	0.8276
No log	2.3871	74	0.6800	0.3425	0.6800	0.8246
No log	2.4516	76	0.6856	0.3708	0.6856	0.8280
No log	2.5161	78	0.7206	0.4	0.7206	0.8489
No log	2.5806	80	0.7458	0.4	0.7458	0.8636
No log	2.6452	82	0.7865	0.4	0.7865	0.8869
No log	2.7097	84	0.7714	0.4	0.7714	0.8783
No log	2.7742	86	0.7297	0.4	0.7297	0.8542
No log	2.8387	88	0.6938	0.4	0.6938	0.8329
No log	2.9032	90	0.6866	0.4664	0.6866	0.8286
No log	2.9677	92	0.6969	0.4979	0.6969	0.8348
No log	3.0323	94	0.7069	0.5078	0.7069	0.8408
No log	3.0968	96	0.7196	0.4360	0.7196	0.8483
No log	3.1613	98	0.7742	0.4615	0.7742	0.8799
No log	3.2258	100	0.8040	0.3740	0.8040	0.8967
No log	3.2903	102	0.8239	0.4280	0.8239	0.9077
No log	3.3548	104	0.7697	0.375	0.7697	0.8773
No log	3.4194	106	0.6902	0.5702	0.6902	0.8308
No log	3.4839	108	0.6574	0.5702	0.6574	0.8108
No log	3.5484	110	0.6496	0.5625	0.6496	0.8060
No log	3.6129	112	0.6463	0.6263	0.6463	0.8039
No log	3.6774	114	0.6425	0.6263	0.6425	0.8015
No log	3.7419	116	0.6256	0.6263	0.6256	0.7909
No log	3.8065	118	0.6080	0.5882	0.6080	0.7797
No log	3.8710	120	0.6767	0.6111	0.6767	0.8226
No log	3.9355	122	0.7441	0.4122	0.7441	0.8626
No log	4.0	124	0.7353	0.5039	0.7353	0.8575
No log	4.0645	126	0.7098	0.6111	0.7098	0.8425
No log	4.1290	128	0.6767	0.6111	0.6767	0.8226
No log	4.1935	130	0.6430	0.5911	0.6430	0.8018
No log	4.2581	132	0.6690	0.5911	0.6690	0.8179
No log	4.3226	134	0.7304	0.6111	0.7304	0.8546
No log	4.3871	136	0.8502	0.6111	0.8502	0.9221
No log	4.4516	138	0.9068	0.6387	0.9068	0.9522
No log	4.5161	140	0.8387	0.6020	0.8387	0.9158
No log	4.5806	142	0.7981	0.6111	0.7981	0.8934
No log	4.6452	144	0.7320	0.5455	0.7320	0.8556
No log	4.7097	146	0.7020	0.5455	0.7020	0.8378
No log	4.7742	148	0.6707	0.5363	0.6707	0.8190
No log	4.8387	150	0.6531	0.5333	0.6531	0.8081
No log	4.9032	152	0.6652	0.5455	0.6652	0.8156
No log	4.9677	154	0.7190	0.6111	0.7190	0.8479
No log	5.0323	156	0.7183	0.6111	0.7183	0.8475
No log	5.0968	158	0.6569	0.6111	0.6569	0.8105
No log	5.1613	160	0.6119	0.6111	0.6119	0.7823
No log	5.2258	162	0.6086	0.5911	0.6086	0.7801
No log	5.2903	164	0.6147	0.6000	0.6147	0.7840
No log	5.3548	166	0.6262	0.5522	0.6262	0.7913
No log	5.4194	168	0.6368	0.5522	0.6368	0.7980
No log	5.4839	170	0.6765	0.5455	0.6765	0.8225
No log	5.5484	172	0.7170	0.6291	0.7170	0.8467
No log	5.6129	174	0.7256	0.5911	0.7256	0.8518
No log	5.6774	176	0.7007	0.5911	0.7007	0.8371
No log	5.7419	178	0.6750	0.5911	0.6750	0.8216
No log	5.8065	180	0.6577	0.5911	0.6577	0.8110
No log	5.8710	182	0.6665	0.5911	0.6665	0.8164
No log	5.9355	184	0.6761	0.5455	0.6761	0.8223
No log	6.0	186	0.7069	0.5831	0.7069	0.8408
No log	6.0645	188	0.7041	0.5455	0.7041	0.8391
No log	6.1290	190	0.7042	0.5455	0.7042	0.8392
No log	6.1935	192	0.7336	0.5831	0.7336	0.8565
No log	6.2581	194	0.7710	0.6387	0.7710	0.8780
No log	6.3226	196	0.7610	0.6387	0.7610	0.8724
No log	6.3871	198	0.7231	0.5831	0.7231	0.8504
No log	6.4516	200	0.6948	0.5455	0.6948	0.8336
No log	6.5161	202	0.6865	0.5455	0.6865	0.8286
No log	6.5806	204	0.6906	0.5455	0.6906	0.8310
No log	6.6452	206	0.7321	0.6111	0.7321	0.8556
No log	6.7097	208	0.7441	0.6020	0.7441	0.8626
No log	6.7742	210	0.7014	0.6111	0.7014	0.8375
No log	6.8387	212	0.6877	0.5911	0.6877	0.8293
No log	6.9032	214	0.6825	0.5911	0.6825	0.8262
No log	6.9677	216	0.7006	0.5455	0.7006	0.8370
No log	7.0323	218	0.7219	0.5908	0.7219	0.8496
No log	7.0968	220	0.7444	0.5831	0.7444	0.8628
No log	7.1613	222	0.7626	0.5831	0.7626	0.8733
No log	7.2258	224	0.7543	0.5831	0.7543	0.8685
No log	7.2903	226	0.7292	0.5831	0.7292	0.8539
No log	7.3548	228	0.7330	0.5831	0.7330	0.8562
No log	7.4194	230	0.7137	0.5831	0.7137	0.8448
No log	7.4839	232	0.6915	0.5831	0.6915	0.8316
No log	7.5484	234	0.6643	0.5831	0.6643	0.8151
No log	7.6129	236	0.6801	0.5831	0.6801	0.8247
No log	7.6774	238	0.7095	0.5831	0.7095	0.8423
No log	7.7419	240	0.7413	0.6291	0.7413	0.8610
No log	7.8065	242	0.7676	0.6198	0.7676	0.8761
No log	7.8710	244	0.7785	0.6387	0.7785	0.8823
No log	7.9355	246	0.7696	0.6198	0.7696	0.8772
No log	8.0	248	0.7497	0.6291	0.7497	0.8658
No log	8.0645	250	0.7363	0.6291	0.7363	0.8581
No log	8.1290	252	0.7070	0.5831	0.7070	0.8408
No log	8.1935	254	0.6947	0.5831	0.6947	0.8335
No log	8.2581	256	0.6983	0.5831	0.6983	0.8356
No log	8.3226	258	0.7251	0.6291	0.7251	0.8515
No log	8.3871	260	0.7556	0.6387	0.7556	0.8692
No log	8.4516	262	0.7563	0.6387	0.7563	0.8697
No log	8.5161	264	0.7651	0.6387	0.7651	0.8747
No log	8.5806	266	0.7857	0.6387	0.7857	0.8864
No log	8.6452	268	0.8015	0.6387	0.8015	0.8953
No log	8.7097	270	0.8103	0.6387	0.8103	0.9002
No log	8.7742	272	0.7983	0.6387	0.7983	0.8935
No log	8.8387	274	0.7795	0.6387	0.7795	0.8829
No log	8.9032	276	0.7516	0.6013	0.7516	0.8669
No log	8.9677	278	0.7302	0.5831	0.7302	0.8545
No log	9.0323	280	0.7125	0.5831	0.7125	0.8441
No log	9.0968	282	0.7038	0.5831	0.7038	0.8389
No log	9.1613	284	0.6973	0.5831	0.6973	0.8350
No log	9.2258	286	0.6995	0.5831	0.6995	0.8364
No log	9.2903	288	0.7119	0.5831	0.7119	0.8438
No log	9.3548	290	0.7243	0.6488	0.7243	0.8510
No log	9.4194	292	0.7230	0.6488	0.7230	0.8503
No log	9.4839	294	0.7152	0.6488	0.7152	0.8457
No log	9.5484	296	0.7044	0.6488	0.7044	0.8393
No log	9.6129	298	0.7006	0.6488	0.7006	0.8370
No log	9.6774	300	0.6949	0.6488	0.6949	0.8336
No log	9.7419	302	0.6891	0.6488	0.6891	0.8301
No log	9.8065	304	0.6881	0.6488	0.6881	0.8295
No log	9.8710	306	0.6902	0.6488	0.6902	0.8308
No log	9.9355	308	0.6926	0.6488	0.6926	0.8322
No log	10.0	310	0.6928	0.6488	0.6928	0.8324

Framework versions

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