ArabicNewSplits6_FineTuningAraBERT_run2_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: 0.6463
• Qwk: 0.7500
• Mse: 0.6463
• Rmse: 0.8039

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	2.2588	-0.0233	2.2588	1.5029
No log	0.2222	4	1.5405	0.1363	1.5405	1.2412
No log	0.3333	6	1.3849	0.1718	1.3849	1.1768
No log	0.4444	8	1.3620	0.2065	1.3620	1.1671
No log	0.5556	10	1.1089	0.2871	1.1089	1.0530
No log	0.6667	12	1.0663	0.3276	1.0663	1.0326
No log	0.7778	14	1.1024	0.3826	1.1024	1.0500
No log	0.8889	16	0.9647	0.4285	0.9647	0.9822
No log	1.0	18	1.0225	0.5138	1.0225	1.0112
No log	1.1111	20	1.0196	0.5265	1.0196	1.0097
No log	1.2222	22	0.8975	0.4882	0.8975	0.9473
No log	1.3333	24	0.8989	0.4944	0.8989	0.9481
No log	1.4444	26	0.8669	0.5039	0.8669	0.9311
No log	1.5556	28	0.8673	0.5799	0.8673	0.9313
No log	1.6667	30	0.9829	0.5381	0.9829	0.9914
No log	1.7778	32	0.9476	0.5394	0.9476	0.9735
No log	1.8889	34	0.8114	0.6330	0.8114	0.9008
No log	2.0	36	0.7608	0.6596	0.7608	0.8722
No log	2.1111	38	0.7687	0.6763	0.7687	0.8768
No log	2.2222	40	0.7960	0.6752	0.7960	0.8922
No log	2.3333	42	0.9475	0.6040	0.9475	0.9734
No log	2.4444	44	1.0283	0.5675	1.0283	1.0140
No log	2.5556	46	0.8513	0.6492	0.8513	0.9226
No log	2.6667	48	0.7050	0.7126	0.7050	0.8396
No log	2.7778	50	0.7166	0.7160	0.7166	0.8465
No log	2.8889	52	0.7844	0.6501	0.7844	0.8857
No log	3.0	54	0.7967	0.6460	0.7967	0.8926
No log	3.1111	56	0.7384	0.6636	0.7384	0.8593
No log	3.2222	58	0.7140	0.6737	0.7140	0.8450
No log	3.3333	60	0.7171	0.6737	0.7171	0.8468
No log	3.4444	62	0.6771	0.6953	0.6771	0.8229
No log	3.5556	64	0.6525	0.7380	0.6525	0.8078
No log	3.6667	66	0.7088	0.7248	0.7088	0.8419
No log	3.7778	68	0.7669	0.7362	0.7669	0.8758
No log	3.8889	70	0.7363	0.7481	0.7363	0.8581
No log	4.0	72	0.7227	0.7539	0.7227	0.8501
No log	4.1111	74	0.7545	0.7266	0.7545	0.8686
No log	4.2222	76	0.7285	0.7396	0.7285	0.8535
No log	4.3333	78	0.6981	0.7559	0.6981	0.8355
No log	4.4444	80	0.6675	0.7575	0.6675	0.8170
No log	4.5556	82	0.6565	0.7575	0.6565	0.8103
No log	4.6667	84	0.6485	0.7603	0.6485	0.8053
No log	4.7778	86	0.6736	0.7575	0.6736	0.8207
No log	4.8889	88	0.6438	0.7651	0.6438	0.8023
No log	5.0	90	0.6325	0.7353	0.6325	0.7953
No log	5.1111	92	0.6373	0.7416	0.6373	0.7983
No log	5.2222	94	0.6278	0.7400	0.6278	0.7923
No log	5.3333	96	0.6253	0.7361	0.6253	0.7907
No log	5.4444	98	0.6378	0.7298	0.6378	0.7986
No log	5.5556	100	0.6253	0.7298	0.6253	0.7908
No log	5.6667	102	0.6554	0.7269	0.6554	0.8096
No log	5.7778	104	0.8158	0.7220	0.8158	0.9032
No log	5.8889	106	0.8528	0.7080	0.8528	0.9235
No log	6.0	108	0.7530	0.7315	0.7530	0.8678
No log	6.1111	110	0.6391	0.7369	0.6391	0.7994
No log	6.2222	112	0.6448	0.7054	0.6448	0.8030
No log	6.3333	114	0.6401	0.7054	0.6401	0.8001
No log	6.4444	116	0.6179	0.7321	0.6179	0.7861
No log	6.5556	118	0.6641	0.7501	0.6641	0.8149
No log	6.6667	120	0.7534	0.7467	0.7534	0.8680
No log	6.7778	122	0.7540	0.7392	0.7540	0.8684
No log	6.8889	124	0.6838	0.7482	0.6838	0.8269
No log	7.0	126	0.6095	0.7552	0.6095	0.7807
No log	7.1111	128	0.6003	0.7357	0.6003	0.7748
No log	7.2222	130	0.6214	0.7310	0.6214	0.7883
No log	7.3333	132	0.6161	0.7287	0.6161	0.7849
No log	7.4444	134	0.6003	0.7511	0.6003	0.7748
No log	7.5556	136	0.6050	0.7619	0.6050	0.7778
No log	7.6667	138	0.6248	0.7552	0.6248	0.7904
No log	7.7778	140	0.6403	0.7640	0.6403	0.8002
No log	7.8889	142	0.6684	0.7387	0.6684	0.8176
No log	8.0	144	0.6836	0.7442	0.6836	0.8268
No log	8.1111	146	0.6799	0.7442	0.6799	0.8245
No log	8.2222	148	0.6505	0.7659	0.6505	0.8065
No log	8.3333	150	0.6160	0.7619	0.6160	0.7849
No log	8.4444	152	0.6023	0.7563	0.6023	0.7761
No log	8.5556	154	0.6013	0.7587	0.6013	0.7755
No log	8.6667	156	0.6027	0.7587	0.6027	0.7763
No log	8.7778	158	0.6033	0.7587	0.6033	0.7767
No log	8.8889	160	0.6072	0.7563	0.6072	0.7792
No log	9.0	162	0.6137	0.7619	0.6137	0.7834
No log	9.1111	164	0.6178	0.7619	0.6178	0.7860
No log	9.2222	166	0.6243	0.7619	0.6243	0.7901
No log	9.3333	168	0.6272	0.7619	0.6272	0.7920
No log	9.4444	170	0.6276	0.7619	0.6276	0.7922
No log	9.5556	172	0.6313	0.7619	0.6313	0.7945
No log	9.6667	174	0.6375	0.7694	0.6375	0.7984
No log	9.7778	176	0.6416	0.7610	0.6416	0.8010
No log	9.8889	178	0.6448	0.7568	0.6448	0.8030
No log	10.0	180	0.6463	0.7500	0.6463	0.8039

Framework versions

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