ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run1_AugV5_k3_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: 1.0344
• Qwk: 0.6352
• Mse: 1.0344
• Rmse: 1.0170

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	0.1053	2	5.2058	-0.0207	5.2058	2.2816
No log	0.2105	4	3.0757	0.0405	3.0757	1.7538
No log	0.3158	6	2.3370	-0.0880	2.3370	1.5287
No log	0.4211	8	1.2550	0.2614	1.2550	1.1203
No log	0.5263	10	1.1155	0.4207	1.1155	1.0562
No log	0.6316	12	1.0947	0.1956	1.0947	1.0463
No log	0.7368	14	1.4606	0.2392	1.4606	1.2085
No log	0.8421	16	1.5005	0.0705	1.5005	1.2250
No log	0.9474	18	1.4596	0.1002	1.4596	1.2081
No log	1.0526	20	1.3328	0.1345	1.3328	1.1545
No log	1.1579	22	1.2963	0.2565	1.2963	1.1385
No log	1.2632	24	1.1735	0.1795	1.1735	1.0833
No log	1.3684	26	1.1387	0.2530	1.1387	1.0671
No log	1.4737	28	1.0691	0.4108	1.0691	1.0340
No log	1.5789	30	1.0206	0.4697	1.0206	1.0102
No log	1.6842	32	0.8976	0.5226	0.8976	0.9474
No log	1.7895	34	0.9624	0.5665	0.9624	0.9810
No log	1.8947	36	0.9932	0.5527	0.9932	0.9966
No log	2.0	38	0.9644	0.5849	0.9644	0.9821
No log	2.1053	40	0.8784	0.6245	0.8784	0.9372
No log	2.2105	42	0.9410	0.6057	0.9410	0.9701
No log	2.3158	44	1.1750	0.5609	1.1750	1.0840
No log	2.4211	46	1.3641	0.4985	1.3641	1.1679
No log	2.5263	48	1.3213	0.5139	1.3213	1.1495
No log	2.6316	50	0.9491	0.5878	0.9491	0.9742
No log	2.7368	52	0.8661	0.6045	0.8661	0.9307
No log	2.8421	54	0.8323	0.5910	0.8323	0.9123
No log	2.9474	56	0.8151	0.6008	0.8151	0.9029
No log	3.0526	58	0.9136	0.5428	0.9136	0.9558
No log	3.1579	60	1.0422	0.5379	1.0422	1.0209
No log	3.2632	62	0.9121	0.5635	0.9121	0.9550
No log	3.3684	64	0.7429	0.6666	0.7429	0.8619
No log	3.4737	66	0.7069	0.6575	0.7069	0.8408
No log	3.5789	68	0.7449	0.6700	0.7449	0.8631
No log	3.6842	70	0.8999	0.6604	0.8999	0.9486
No log	3.7895	72	1.0507	0.6064	1.0507	1.0250
No log	3.8947	74	0.9798	0.6197	0.9798	0.9899
No log	4.0	76	0.9486	0.6013	0.9486	0.9740
No log	4.1053	78	0.8180	0.6600	0.8180	0.9044
No log	4.2105	80	0.7068	0.6759	0.7068	0.8407
No log	4.3158	82	0.6964	0.6795	0.6964	0.8345
No log	4.4211	84	0.7608	0.6998	0.7608	0.8723
No log	4.5263	86	0.9991	0.6192	0.9991	0.9995
No log	4.6316	88	1.2442	0.5384	1.2442	1.1155
No log	4.7368	90	1.3775	0.5279	1.3775	1.1737
No log	4.8421	92	1.3308	0.5299	1.3308	1.1536
No log	4.9474	94	1.1448	0.5937	1.1448	1.0700
No log	5.0526	96	1.0736	0.6036	1.0736	1.0362
No log	5.1579	98	1.0734	0.6036	1.0734	1.0360
No log	5.2632	100	0.9939	0.6308	0.9939	0.9969
No log	5.3684	102	0.9576	0.6669	0.9576	0.9786
No log	5.4737	104	1.0010	0.6397	1.0010	1.0005
No log	5.5789	106	1.0490	0.6217	1.0490	1.0242
No log	5.6842	108	1.0358	0.6166	1.0358	1.0177
No log	5.7895	110	1.1183	0.6031	1.1183	1.0575
No log	5.8947	112	1.0805	0.6301	1.0805	1.0395
No log	6.0	114	0.9444	0.6660	0.9444	0.9718
No log	6.1053	116	0.9423	0.6762	0.9423	0.9707
No log	6.2105	118	0.9569	0.6805	0.9569	0.9782
No log	6.3158	120	0.9393	0.6930	0.9393	0.9692
No log	6.4211	122	0.9684	0.6837	0.9684	0.9841
No log	6.5263	124	1.0027	0.6594	1.0027	1.0014
No log	6.6316	126	1.0763	0.6507	1.0763	1.0374
No log	6.7368	128	1.2391	0.6145	1.2391	1.1131
No log	6.8421	130	1.3103	0.6105	1.3103	1.1447
No log	6.9474	132	1.2617	0.6191	1.2617	1.1233
No log	7.0526	134	1.2642	0.6085	1.2642	1.1244
No log	7.1579	136	1.1499	0.6390	1.1499	1.0723
No log	7.2632	138	1.0890	0.6454	1.0890	1.0436
No log	7.3684	140	1.0593	0.6607	1.0593	1.0292
No log	7.4737	142	1.0361	0.6603	1.0361	1.0179
No log	7.5789	144	0.9973	0.6641	0.9973	0.9986
No log	7.6842	146	0.9904	0.6787	0.9904	0.9952
No log	7.7895	148	1.0017	0.6657	1.0017	1.0008
No log	7.8947	150	0.9965	0.6465	0.9965	0.9983
No log	8.0	152	0.9556	0.6723	0.9556	0.9775
No log	8.1053	154	0.9682	0.6567	0.9682	0.9839
No log	8.2105	156	0.9956	0.6443	0.9956	0.9978
No log	8.3158	158	1.0079	0.6181	1.0079	1.0039
No log	8.4211	160	1.0326	0.6195	1.0326	1.0162
No log	8.5263	162	1.0431	0.6047	1.0431	1.0213
No log	8.6316	164	1.0438	0.6273	1.0438	1.0217
No log	8.7368	166	1.0149	0.6424	1.0149	1.0074
No log	8.8421	168	0.9693	0.6599	0.9693	0.9845
No log	8.9474	170	0.9600	0.6751	0.9600	0.9798
No log	9.0526	172	0.9983	0.6599	0.9983	0.9991
No log	9.1579	174	1.0531	0.6346	1.0531	1.0262
No log	9.2632	176	1.0683	0.6357	1.0683	1.0336
No log	9.3684	178	1.0603	0.6357	1.0603	1.0297
No log	9.4737	180	1.0437	0.6346	1.0437	1.0216
No log	9.5789	182	1.0344	0.6352	1.0344	1.0171
No log	9.6842	184	1.0324	0.6352	1.0324	1.0161
No log	9.7895	186	1.0298	0.6429	1.0298	1.0148
No log	9.8947	188	1.0312	0.6429	1.0312	1.0155
No log	10.0	190	1.0344	0.6352	1.0344	1.0170

Framework versions

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