ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k3_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: 0.7699
• Qwk: 0.5382
• Mse: 0.7699
• Rmse: 0.8774

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	0.1176	2	4.1029	-0.0009	4.1029	2.0256
No log	0.2353	4	2.2443	0.0820	2.2443	1.4981
No log	0.3529	6	1.1195	0.0774	1.1195	1.0581
No log	0.4706	8	0.7627	0.0662	0.7627	0.8734
No log	0.5882	10	0.6773	0.2984	0.6773	0.8230
No log	0.7059	12	0.7316	0.1677	0.7316	0.8553
No log	0.8235	14	0.9648	0.1213	0.9648	0.9822
No log	0.9412	16	0.7969	0.2172	0.7969	0.8927
No log	1.0588	18	0.6220	0.2333	0.6220	0.7887
No log	1.1765	20	0.6307	0.2698	0.6307	0.7942
No log	1.2941	22	0.6014	0.2245	0.6014	0.7755
No log	1.4118	24	0.5962	0.3398	0.5962	0.7722
No log	1.5294	26	0.6542	0.2917	0.6542	0.8088
No log	1.6471	28	0.9095	0.2206	0.9095	0.9537
No log	1.7647	30	1.1530	0.1496	1.1530	1.0738
No log	1.8824	32	1.0505	0.2163	1.0505	1.0249
No log	2.0	34	0.7046	0.3862	0.7046	0.8394
No log	2.1176	36	0.5501	0.3757	0.5501	0.7417
No log	2.2353	38	0.5414	0.4543	0.5414	0.7358
No log	2.3529	40	0.6031	0.3146	0.6031	0.7766
No log	2.4706	42	0.6433	0.2952	0.6433	0.8020
No log	2.5882	44	0.6057	0.2903	0.6057	0.7783
No log	2.7059	46	0.5356	0.4332	0.5356	0.7318
No log	2.8235	48	0.5414	0.4967	0.5414	0.7358
No log	2.9412	50	0.6498	0.4123	0.6498	0.8061
No log	3.0588	52	0.7451	0.3481	0.7451	0.8632
No log	3.1765	54	0.8332	0.3299	0.8332	0.9128
No log	3.2941	56	0.7467	0.4078	0.7467	0.8641
No log	3.4118	58	0.7523	0.4151	0.7523	0.8674
No log	3.5294	60	0.6954	0.4445	0.6954	0.8339
No log	3.6471	62	0.6669	0.4420	0.6669	0.8167
No log	3.7647	64	0.6372	0.4495	0.6372	0.7982
No log	3.8824	66	0.5551	0.4341	0.5551	0.7451
No log	4.0	68	0.5503	0.4151	0.5503	0.7418
No log	4.1176	70	0.5713	0.4156	0.5713	0.7558
No log	4.2353	72	0.6113	0.5079	0.6113	0.7819
No log	4.3529	74	0.6414	0.5100	0.6414	0.8009
No log	4.4706	76	0.6890	0.4885	0.6890	0.8300
No log	4.5882	78	0.7303	0.4836	0.7303	0.8546
No log	4.7059	80	0.7092	0.4835	0.7092	0.8422
No log	4.8235	82	0.6772	0.5490	0.6772	0.8229
No log	4.9412	84	0.6453	0.5955	0.6453	0.8033
No log	5.0588	86	0.6438	0.5550	0.6438	0.8024
No log	5.1765	88	0.6547	0.5550	0.6547	0.8091
No log	5.2941	90	0.6386	0.5898	0.6386	0.7991
No log	5.4118	92	0.6248	0.6046	0.6248	0.7905
No log	5.5294	94	0.6306	0.6046	0.6306	0.7941
No log	5.6471	96	0.6472	0.5872	0.6472	0.8045
No log	5.7647	98	0.6832	0.5825	0.6832	0.8265
No log	5.8824	100	0.7171	0.5271	0.7171	0.8468
No log	6.0	102	0.7564	0.4787	0.7564	0.8697
No log	6.1176	104	0.7711	0.4965	0.7711	0.8781
No log	6.2353	106	0.7521	0.4989	0.7521	0.8673
No log	6.3529	108	0.7327	0.5058	0.7327	0.8560
No log	6.4706	110	0.7053	0.5491	0.7053	0.8398
No log	6.5882	112	0.7022	0.5464	0.7022	0.8380
No log	6.7059	114	0.7232	0.5395	0.7232	0.8504
No log	6.8235	116	0.7559	0.5252	0.7559	0.8694
No log	6.9412	118	0.8045	0.4770	0.8045	0.8969
No log	7.0588	120	0.8445	0.4566	0.8445	0.9190
No log	7.1765	122	0.8508	0.4661	0.8508	0.9224
No log	7.2941	124	0.8182	0.4759	0.8182	0.9045
No log	7.4118	126	0.7997	0.5065	0.7997	0.8943
No log	7.5294	128	0.8248	0.4937	0.8248	0.9082
No log	7.6471	130	0.8471	0.4814	0.8471	0.9204
No log	7.7647	132	0.8631	0.4858	0.8631	0.9290
No log	7.8824	134	0.8662	0.4770	0.8662	0.9307
No log	8.0	136	0.8735	0.4942	0.8735	0.9346
No log	8.1176	138	0.8638	0.4942	0.8638	0.9294
No log	8.2353	140	0.8435	0.4982	0.8435	0.9184
No log	8.3529	142	0.8118	0.4990	0.8118	0.9010
No log	8.4706	144	0.7860	0.5231	0.7860	0.8866
No log	8.5882	146	0.7683	0.5307	0.7683	0.8765
No log	8.7059	148	0.7633	0.4905	0.7633	0.8737
No log	8.8235	150	0.7697	0.4992	0.7697	0.8773
No log	8.9412	152	0.7669	0.4872	0.7669	0.8757
No log	9.0588	154	0.7640	0.4852	0.7640	0.8740
No log	9.1765	156	0.7631	0.4733	0.7631	0.8736
No log	9.2941	158	0.7646	0.5148	0.7646	0.8744
No log	9.4118	160	0.7669	0.5148	0.7669	0.8757
No log	9.5294	162	0.7677	0.5203	0.7677	0.8762
No log	9.6471	164	0.7676	0.5203	0.7676	0.8761
No log	9.7647	166	0.7685	0.5161	0.7685	0.8766
No log	9.8824	168	0.7697	0.5327	0.7697	0.8773
No log	10.0	170	0.7699	0.5382	0.7699	0.8774

Framework versions

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