ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task2_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: 0.7177
• Qwk: 0.5181
• Mse: 0.7177
• Rmse: 0.8472

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	2.8651	-0.0187	2.8651	1.6927
No log	0.2105	4	1.4887	0.2152	1.4887	1.2201
No log	0.3158	6	0.9179	0.0770	0.9179	0.9581
No log	0.4211	8	0.9707	0.1068	0.9707	0.9852
No log	0.5263	10	0.9437	0.1458	0.9437	0.9714
No log	0.6316	12	0.7649	0.0166	0.7649	0.8746
No log	0.7368	14	0.8296	0.0	0.8296	0.9108
No log	0.8421	16	1.0204	0.1366	1.0204	1.0101
No log	0.9474	18	1.0106	0.1827	1.0106	1.0053
No log	1.0526	20	1.0467	0.1747	1.0467	1.0231
No log	1.1579	22	0.9335	0.1705	0.9335	0.9662
No log	1.2632	24	0.8177	0.0680	0.8177	0.9043
No log	1.3684	26	0.7094	0.0537	0.7094	0.8423
No log	1.4737	28	0.6920	0.0943	0.6920	0.8319
No log	1.5789	30	0.7189	0.1638	0.7189	0.8479
No log	1.6842	32	0.8750	0.3157	0.8750	0.9354
No log	1.7895	34	1.0990	0.2172	1.0990	1.0483
No log	1.8947	36	1.0880	0.2608	1.0880	1.0431
No log	2.0	38	0.9432	0.3646	0.9432	0.9712
No log	2.1053	40	0.7189	0.4132	0.7189	0.8479
No log	2.2105	42	0.5272	0.4509	0.5272	0.7261
No log	2.3158	44	0.5078	0.3783	0.5078	0.7126
No log	2.4211	46	0.5161	0.4327	0.5161	0.7184
No log	2.5263	48	0.6644	0.4764	0.6644	0.8151
No log	2.6316	50	0.9921	0.3160	0.9921	0.9960
No log	2.7368	52	1.2042	0.2147	1.2042	1.0974
No log	2.8421	54	1.0183	0.2564	1.0183	1.0091
No log	2.9474	56	0.6517	0.4908	0.6517	0.8073
No log	3.0526	58	0.5349	0.4068	0.5349	0.7314
No log	3.1579	60	0.5788	0.3104	0.5788	0.7608
No log	3.2632	62	0.5639	0.2909	0.5639	0.7509
No log	3.3684	64	0.5501	0.2804	0.5501	0.7417
No log	3.4737	66	0.7165	0.4651	0.7165	0.8464
No log	3.5789	68	0.9581	0.2957	0.9581	0.9788
No log	3.6842	70	1.1050	0.2550	1.1050	1.0512
No log	3.7895	72	1.1111	0.2695	1.1111	1.0541
No log	3.8947	74	0.8771	0.3716	0.8771	0.9365
No log	4.0	76	0.6091	0.5038	0.6091	0.7805
No log	4.1053	78	0.5409	0.4697	0.5409	0.7355
No log	4.2105	80	0.5504	0.4262	0.5504	0.7419
No log	4.3158	82	0.5701	0.4282	0.5701	0.7550
No log	4.4211	84	0.6417	0.4673	0.6417	0.8011
No log	4.5263	86	0.7428	0.4665	0.7428	0.8618
No log	4.6316	88	0.7349	0.4663	0.7349	0.8572
No log	4.7368	90	0.6970	0.4190	0.6970	0.8349
No log	4.8421	92	0.6465	0.4323	0.6465	0.8040
No log	4.9474	94	0.6019	0.4472	0.6019	0.7758
No log	5.0526	96	0.5864	0.4509	0.5864	0.7658
No log	5.1579	98	0.5937	0.4491	0.5937	0.7705
No log	5.2632	100	0.6206	0.4836	0.6206	0.7878
No log	5.3684	102	0.6802	0.5475	0.6802	0.8247
No log	5.4737	104	0.6763	0.5370	0.6763	0.8224
No log	5.5789	106	0.6112	0.5515	0.6112	0.7818
No log	5.6842	108	0.5955	0.5157	0.5955	0.7717
No log	5.7895	110	0.6057	0.5157	0.6057	0.7783
No log	5.8947	112	0.6198	0.5110	0.6198	0.7873
No log	6.0	114	0.6533	0.5280	0.6533	0.8083
No log	6.1053	116	0.6951	0.5249	0.6951	0.8337
No log	6.2105	118	0.7341	0.5249	0.7341	0.8568
No log	6.3158	120	0.7373	0.5249	0.7373	0.8587
No log	6.4211	122	0.7404	0.5249	0.7404	0.8604
No log	6.5263	124	0.7509	0.5047	0.7509	0.8666
No log	6.6316	126	0.7302	0.5043	0.7302	0.8545
No log	6.7368	128	0.7226	0.4829	0.7226	0.8501
No log	6.8421	130	0.7113	0.4599	0.7113	0.8434
No log	6.9474	132	0.7446	0.4599	0.7446	0.8629
No log	7.0526	134	0.7696	0.4599	0.7696	0.8772
No log	7.1579	136	0.7851	0.4829	0.7851	0.8861
No log	7.2632	138	0.8120	0.4829	0.8120	0.9011
No log	7.3684	140	0.8259	0.4616	0.8259	0.9088
No log	7.4737	142	0.8146	0.4838	0.8146	0.9026
No log	7.5789	144	0.7746	0.4838	0.7746	0.8801
No log	7.6842	146	0.7206	0.4739	0.7206	0.8489
No log	7.7895	148	0.6857	0.4852	0.6857	0.8281
No log	7.8947	150	0.6846	0.4861	0.6846	0.8274
No log	8.0	152	0.6863	0.4852	0.6863	0.8284
No log	8.1053	154	0.6956	0.4954	0.6956	0.8340
No log	8.2105	156	0.7036	0.4960	0.7036	0.8388
No log	8.3158	158	0.7155	0.4954	0.7155	0.8459
No log	8.4211	160	0.7064	0.5071	0.7064	0.8405
No log	8.5263	162	0.6938	0.5077	0.6938	0.8330
No log	8.6316	164	0.6954	0.5081	0.6954	0.8339
No log	8.7368	166	0.7158	0.4960	0.7158	0.8461
No log	8.8421	168	0.7433	0.4956	0.7433	0.8622
No log	8.9474	170	0.7596	0.5053	0.7596	0.8716
No log	9.0526	172	0.7625	0.5053	0.7625	0.8732
No log	9.1579	174	0.7608	0.5053	0.7608	0.8722
No log	9.2632	176	0.7440	0.5170	0.7440	0.8625
No log	9.3684	178	0.7288	0.5179	0.7288	0.8537
No log	9.4737	180	0.7224	0.5179	0.7224	0.8499
No log	9.5789	182	0.7185	0.5181	0.7185	0.8476
No log	9.6842	184	0.7157	0.5181	0.7157	0.8460
No log	9.7895	186	0.7152	0.5181	0.7152	0.8457
No log	9.8947	188	0.7167	0.5181	0.7167	0.8466
No log	10.0	190	0.7177	0.5181	0.7177	0.8472

Framework versions

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