ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k4_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: 0.6196
• Qwk: 0.7266
• Mse: 0.6196
• Rmse: 0.7872

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	0.0952	2	5.1211	-0.0463	5.1211	2.2630
No log	0.1905	4	3.1870	0.0916	3.1870	1.7852
No log	0.2857	6	1.9636	0.0562	1.9636	1.4013
No log	0.3810	8	1.4818	0.1792	1.4818	1.2173
No log	0.4762	10	1.1630	0.2358	1.1630	1.0784
No log	0.5714	12	1.2285	0.2466	1.2285	1.1084
No log	0.6667	14	1.5901	0.0775	1.5901	1.2610
No log	0.7619	16	1.6189	0.0762	1.6189	1.2724
No log	0.8571	18	1.3261	0.2092	1.3261	1.1516
No log	0.9524	20	1.2629	0.3453	1.2629	1.1238
No log	1.0476	22	1.3507	0.2158	1.3507	1.1622
No log	1.1429	24	1.3346	0.2944	1.3346	1.1553
No log	1.2381	26	0.8427	0.4843	0.8427	0.9180
No log	1.3333	28	0.7163	0.6497	0.7163	0.8464
No log	1.4286	30	0.6627	0.6378	0.6627	0.8140
No log	1.5238	32	0.7964	0.5879	0.7964	0.8924
No log	1.6190	34	1.7332	0.3252	1.7332	1.3165
No log	1.7143	36	2.0381	0.2701	2.0381	1.4276
No log	1.8095	38	1.5626	0.3792	1.5626	1.2501
No log	1.9048	40	0.8712	0.5631	0.8712	0.9334
No log	2.0	42	0.6583	0.6810	0.6583	0.8114
No log	2.0952	44	0.6373	0.6170	0.6373	0.7983
No log	2.1905	46	0.6318	0.6374	0.6318	0.7949
No log	2.2857	48	0.6146	0.6699	0.6146	0.7839
No log	2.3810	50	0.6234	0.7014	0.6234	0.7895
No log	2.4762	52	0.7059	0.6823	0.7059	0.8402
No log	2.5714	54	0.6727	0.6999	0.6727	0.8202
No log	2.6667	56	0.6104	0.7281	0.6104	0.7813
No log	2.7619	58	0.6215	0.7384	0.6215	0.7884
No log	2.8571	60	0.6001	0.7513	0.6001	0.7747
No log	2.9524	62	0.7531	0.6957	0.7531	0.8678
No log	3.0476	64	0.9444	0.6511	0.9444	0.9718
No log	3.1429	66	0.7518	0.6910	0.7518	0.8671
No log	3.2381	68	0.5898	0.7684	0.5898	0.7680
No log	3.3333	70	0.6954	0.7155	0.6954	0.8339
No log	3.4286	72	0.7229	0.6898	0.7229	0.8502
No log	3.5238	74	0.5934	0.7717	0.5934	0.7703
No log	3.6190	76	0.6167	0.7145	0.6167	0.7853
No log	3.7143	78	0.6935	0.6970	0.6935	0.8327
No log	3.8095	80	0.7082	0.6933	0.7082	0.8415
No log	3.9048	82	0.6117	0.7069	0.6117	0.7821
No log	4.0	84	0.5955	0.7650	0.5955	0.7717
No log	4.0952	86	0.6606	0.7356	0.6606	0.8127
No log	4.1905	88	0.6359	0.7449	0.6359	0.7974
No log	4.2857	90	0.5965	0.7344	0.5965	0.7723
No log	4.3810	92	0.6032	0.7461	0.6032	0.7767
No log	4.4762	94	0.5941	0.7435	0.5941	0.7708
No log	4.5714	96	0.5936	0.7482	0.5936	0.7705
No log	4.6667	98	0.5944	0.7564	0.5944	0.7710
No log	4.7619	100	0.5880	0.7416	0.5880	0.7668
No log	4.8571	102	0.6373	0.7226	0.6373	0.7983
No log	4.9524	104	0.8064	0.5975	0.8064	0.8980
No log	5.0476	106	0.8779	0.6058	0.8779	0.9370
No log	5.1429	108	0.8814	0.6058	0.8814	0.9388
No log	5.2381	110	0.7887	0.6285	0.7887	0.8881
No log	5.3333	112	0.6374	0.7168	0.6374	0.7984
No log	5.4286	114	0.6127	0.7351	0.6127	0.7828
No log	5.5238	116	0.6192	0.7251	0.6192	0.7869
No log	5.6190	118	0.6313	0.7388	0.6313	0.7946
No log	5.7143	120	0.6369	0.7538	0.6369	0.7981
No log	5.8095	122	0.7188	0.7144	0.7188	0.8478
No log	5.9048	124	0.7877	0.6961	0.7877	0.8875
No log	6.0	126	0.7416	0.7276	0.7416	0.8612
No log	6.0952	128	0.6663	0.7414	0.6663	0.8163
No log	6.1905	130	0.6223	0.7406	0.6223	0.7889
No log	6.2857	132	0.6263	0.7379	0.6263	0.7914
No log	6.3810	134	0.6508	0.7342	0.6508	0.8067
No log	6.4762	136	0.6382	0.7299	0.6382	0.7989
No log	6.5714	138	0.6115	0.7439	0.6115	0.7820
No log	6.6667	140	0.6295	0.7379	0.6295	0.7934
No log	6.7619	142	0.6568	0.7005	0.6568	0.8105
No log	6.8571	144	0.6437	0.7282	0.6437	0.8023
No log	6.9524	146	0.6264	0.7207	0.6264	0.7914
No log	7.0476	148	0.6309	0.7338	0.6309	0.7943
No log	7.1429	150	0.6648	0.7299	0.6648	0.8153
No log	7.2381	152	0.6618	0.7342	0.6618	0.8135
No log	7.3333	154	0.6340	0.7480	0.6340	0.7962
No log	7.4286	156	0.6394	0.7299	0.6394	0.7996
No log	7.5238	158	0.7234	0.6851	0.7234	0.8505
No log	7.6190	160	0.7988	0.6727	0.7988	0.8937
No log	7.7143	162	0.7916	0.6727	0.7916	0.8897
No log	7.8095	164	0.7166	0.6830	0.7166	0.8465
No log	7.9048	166	0.6485	0.7414	0.6485	0.8053
No log	8.0	168	0.6358	0.7408	0.6358	0.7974
No log	8.0952	170	0.6399	0.7301	0.6399	0.8000
No log	8.1905	172	0.6359	0.7301	0.6359	0.7974
No log	8.2857	174	0.6346	0.7301	0.6346	0.7966
No log	8.3810	176	0.6267	0.7465	0.6267	0.7917
No log	8.4762	178	0.6190	0.7507	0.6190	0.7868
No log	8.5714	180	0.6172	0.7382	0.6172	0.7856
No log	8.6667	182	0.6182	0.7266	0.6182	0.7863
No log	8.7619	184	0.6203	0.7308	0.6203	0.7876
No log	8.8571	186	0.6255	0.7373	0.6255	0.7909
No log	8.9524	188	0.6346	0.7304	0.6346	0.7966
No log	9.0476	190	0.6329	0.7336	0.6329	0.7956
No log	9.1429	192	0.6271	0.7432	0.6271	0.7919
No log	9.2381	194	0.6216	0.7432	0.6216	0.7884
No log	9.3333	196	0.6177	0.7266	0.6177	0.7859
No log	9.4286	198	0.6173	0.7303	0.6173	0.7857
No log	9.5238	200	0.6175	0.7419	0.6175	0.7858
No log	9.6190	202	0.6181	0.7382	0.6181	0.7862
No log	9.7143	204	0.6186	0.7266	0.6186	0.7865
No log	9.8095	206	0.6190	0.7266	0.6190	0.7868
No log	9.9048	208	0.6195	0.7266	0.6195	0.7871
No log	10.0	210	0.6196	0.7266	0.6196	0.7872

Framework versions

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