ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k4_task5_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: 0.6942
• Qwk: 0.7330
• Mse: 0.6942
• Rmse: 0.8332

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	0.1111	2	2.1651	0.0319	2.1651	1.4714
No log	0.2222	4	1.4278	0.2640	1.4278	1.1949
No log	0.3333	6	1.4002	0.1495	1.4002	1.1833
No log	0.4444	8	1.4463	0.1475	1.4463	1.2026
No log	0.5556	10	1.4628	0.1371	1.4628	1.2095
No log	0.6667	12	1.4531	0.1205	1.4531	1.2054
No log	0.7778	14	1.4696	0.1069	1.4696	1.2123
No log	0.8889	16	1.4574	0.1837	1.4574	1.2072
No log	1.0	18	1.4193	0.2336	1.4193	1.1913
No log	1.1111	20	1.3294	0.3326	1.3294	1.1530
No log	1.2222	22	1.1572	0.3108	1.1572	1.0758
No log	1.3333	24	1.0873	0.2859	1.0873	1.0427
No log	1.4444	26	1.0659	0.2599	1.0659	1.0324
No log	1.5556	28	1.0435	0.2735	1.0435	1.0215
No log	1.6667	30	1.0279	0.4611	1.0279	1.0138
No log	1.7778	32	0.9187	0.5032	0.9187	0.9585
No log	1.8889	34	0.8603	0.5439	0.8603	0.9275
No log	2.0	36	0.8060	0.5614	0.8060	0.8978
No log	2.1111	38	0.7643	0.6331	0.7643	0.8742
No log	2.2222	40	0.8320	0.6531	0.8320	0.9122
No log	2.3333	42	0.7869	0.6551	0.7869	0.8871
No log	2.4444	44	0.7117	0.6692	0.7117	0.8437
No log	2.5556	46	0.8022	0.5940	0.8022	0.8956
No log	2.6667	48	0.9674	0.4979	0.9674	0.9836
No log	2.7778	50	0.9427	0.5032	0.9427	0.9709
No log	2.8889	52	0.8431	0.5670	0.8431	0.9182
No log	3.0	54	0.7544	0.5879	0.7544	0.8686
No log	3.1111	56	0.6665	0.6944	0.6665	0.8164
No log	3.2222	58	0.6946	0.6797	0.6946	0.8334
No log	3.3333	60	0.6941	0.6957	0.6941	0.8331
No log	3.4444	62	0.6934	0.6409	0.6934	0.8327
No log	3.5556	64	0.7268	0.6244	0.7268	0.8525
No log	3.6667	66	0.7177	0.6423	0.7177	0.8472
No log	3.7778	68	0.7335	0.6696	0.7335	0.8565
No log	3.8889	70	0.9257	0.6090	0.9257	0.9621
No log	4.0	72	1.0442	0.5684	1.0442	1.0219
No log	4.1111	74	0.9367	0.6051	0.9367	0.9678
No log	4.2222	76	0.7525	0.6857	0.7525	0.8675
No log	4.3333	78	0.6949	0.7002	0.6949	0.8336
No log	4.4444	80	0.6757	0.6983	0.6757	0.8220
No log	4.5556	82	0.6671	0.7115	0.6671	0.8168
No log	4.6667	84	0.6966	0.7170	0.6966	0.8346
No log	4.7778	86	0.7631	0.6988	0.7631	0.8735
No log	4.8889	88	0.7817	0.6885	0.7817	0.8842
No log	5.0	90	0.7291	0.7109	0.7291	0.8539
No log	5.1111	92	0.6849	0.7096	0.6849	0.8276
No log	5.2222	94	0.6670	0.7052	0.6670	0.8167
No log	5.3333	96	0.6547	0.7032	0.6547	0.8092
No log	5.4444	98	0.6712	0.7098	0.6712	0.8193
No log	5.5556	100	0.6686	0.7078	0.6686	0.8177
No log	5.6667	102	0.6389	0.7026	0.6389	0.7993
No log	5.7778	104	0.6318	0.7091	0.6318	0.7949
No log	5.8889	106	0.6343	0.7315	0.6343	0.7964
No log	6.0	108	0.6427	0.7441	0.6427	0.8017
No log	6.1111	110	0.6776	0.7154	0.6776	0.8232
No log	6.2222	112	0.7421	0.7122	0.7421	0.8614
No log	6.3333	114	0.7433	0.7124	0.7433	0.8622
No log	6.4444	116	0.7126	0.7265	0.7126	0.8441
No log	6.5556	118	0.6961	0.7149	0.6961	0.8343
No log	6.6667	120	0.6630	0.7263	0.6630	0.8142
No log	6.7778	122	0.6331	0.7313	0.6331	0.7957
No log	6.8889	124	0.6305	0.7313	0.6305	0.7940
No log	7.0	126	0.6187	0.7268	0.6187	0.7866
No log	7.1111	128	0.6201	0.7268	0.6201	0.7875
No log	7.2222	130	0.6470	0.7328	0.6470	0.8043
No log	7.3333	132	0.6613	0.7328	0.6613	0.8132
No log	7.4444	134	0.6540	0.7313	0.6540	0.8087
No log	7.5556	136	0.6737	0.7384	0.6737	0.8208
No log	7.6667	138	0.6735	0.7285	0.6735	0.8207
No log	7.7778	140	0.6602	0.7313	0.6602	0.8125
No log	7.8889	142	0.6453	0.7382	0.6453	0.8033
No log	8.0	144	0.6400	0.7049	0.6400	0.8000
No log	8.1111	146	0.6430	0.7174	0.6430	0.8019
No log	8.2222	148	0.6512	0.7314	0.6512	0.8070
No log	8.3333	150	0.6572	0.7223	0.6572	0.8107
No log	8.4444	152	0.6488	0.7291	0.6488	0.8055
No log	8.5556	154	0.6382	0.7283	0.6382	0.7989
No log	8.6667	156	0.6368	0.7237	0.6368	0.7980
No log	8.7778	158	0.6417	0.7337	0.6417	0.8011
No log	8.8889	160	0.6491	0.7224	0.6491	0.8057
No log	9.0	162	0.6490	0.7269	0.6490	0.8056
No log	9.1111	164	0.6538	0.7240	0.6538	0.8086
No log	9.2222	166	0.6639	0.7300	0.6639	0.8148
No log	9.3333	168	0.6721	0.7255	0.6721	0.8198
No log	9.4444	170	0.6837	0.7375	0.6837	0.8268
No log	9.5556	172	0.6955	0.7330	0.6955	0.8339
No log	9.6667	174	0.7003	0.7265	0.7003	0.8368
No log	9.7778	176	0.6986	0.7344	0.6986	0.8358
No log	9.8889	178	0.6956	0.7329	0.6956	0.8340
No log	10.0	180	0.6942	0.7330	0.6942	0.8332

Framework versions

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