ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k7_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: 0.6651
• Qwk: 0.7810
• Mse: 0.6651
• Rmse: 0.8155

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	0.0741	2	2.2341	0.0658	2.2341	1.4947
No log	0.1481	4	1.4901	0.1646	1.4901	1.2207
No log	0.2222	6	1.3686	0.1495	1.3686	1.1699
No log	0.2963	8	1.4558	0.2417	1.4558	1.2066
No log	0.3704	10	1.5222	0.2258	1.5222	1.2338
No log	0.4444	12	1.6418	0.3452	1.6418	1.2813
No log	0.5185	14	1.4933	0.2115	1.4933	1.2220
No log	0.5926	16	1.3815	0.1451	1.3815	1.1754
No log	0.6667	18	1.3249	0.1266	1.3249	1.1510
No log	0.7407	20	1.2925	0.1428	1.2925	1.1369
No log	0.8148	22	1.2546	0.1582	1.2546	1.1201
No log	0.8889	24	1.2141	0.1582	1.2141	1.1019
No log	0.9630	26	1.2029	0.2230	1.2029	1.0968
No log	1.0370	28	1.1660	0.2503	1.1660	1.0798
No log	1.1111	30	1.1022	0.2881	1.1022	1.0499
No log	1.1852	32	1.0668	0.3493	1.0668	1.0329
No log	1.2593	34	1.0379	0.3830	1.0379	1.0188
No log	1.3333	36	1.0117	0.3778	1.0117	1.0058
No log	1.4074	38	0.9864	0.4617	0.9864	0.9932
No log	1.4815	40	1.0400	0.4308	1.0400	1.0198
No log	1.5556	42	1.0009	0.4562	1.0009	1.0004
No log	1.6296	44	0.9046	0.5944	0.9046	0.9511
No log	1.7037	46	0.9117	0.5825	0.9117	0.9548
No log	1.7778	48	1.0467	0.5114	1.0467	1.0231
No log	1.8519	50	1.1533	0.4909	1.1533	1.0739
No log	1.9259	52	1.1148	0.4722	1.1148	1.0558
No log	2.0	54	1.0001	0.4622	1.0001	1.0000
No log	2.0741	56	0.9432	0.4589	0.9432	0.9712
No log	2.1481	58	0.8978	0.5159	0.8978	0.9475
No log	2.2222	60	0.8749	0.5723	0.8749	0.9354
No log	2.2963	62	0.8721	0.5796	0.8721	0.9339
No log	2.3704	64	0.8008	0.6801	0.8008	0.8949
No log	2.4444	66	0.7644	0.7028	0.7644	0.8743
No log	2.5185	68	0.7733	0.6326	0.7733	0.8794
No log	2.5926	70	0.7266	0.6582	0.7266	0.8524
No log	2.6667	72	0.7962	0.7095	0.7962	0.8923
No log	2.7407	74	0.8425	0.6790	0.8425	0.9179
No log	2.8148	76	0.7567	0.6893	0.7567	0.8699
No log	2.8889	78	0.6955	0.6837	0.6955	0.8340
No log	2.9630	80	0.6925	0.6955	0.6925	0.8322
No log	3.0370	82	0.7768	0.6855	0.7768	0.8814
No log	3.1111	84	0.9313	0.6444	0.9313	0.9650
No log	3.1852	86	0.9274	0.6534	0.9274	0.9630
No log	3.2593	88	0.7334	0.7309	0.7334	0.8564
No log	3.3333	90	0.6338	0.7232	0.6338	0.7961
No log	3.4074	92	0.6257	0.6876	0.6257	0.7910
No log	3.4815	94	0.6195	0.6971	0.6195	0.7871
No log	3.5556	96	0.6323	0.7675	0.6323	0.7952
No log	3.6296	98	0.8309	0.7046	0.8309	0.9115
No log	3.7037	100	0.9123	0.6654	0.9123	0.9551
No log	3.7778	102	0.7910	0.7376	0.7910	0.8894
No log	3.8519	104	0.7028	0.7645	0.7028	0.8383
No log	3.9259	106	0.7082	0.7648	0.7082	0.8415
No log	4.0	108	0.7174	0.7338	0.7174	0.8470
No log	4.0741	110	0.7850	0.7070	0.7850	0.8860
No log	4.1481	112	0.8197	0.6934	0.8197	0.9054
No log	4.2222	114	0.8199	0.7144	0.8199	0.9055
No log	4.2963	116	0.7182	0.7391	0.7182	0.8475
No log	4.3704	118	0.7055	0.7511	0.7055	0.8399
No log	4.4444	120	0.7875	0.7226	0.7875	0.8874
No log	4.5185	122	0.8571	0.6920	0.8571	0.9258
No log	4.5926	124	0.7653	0.7378	0.7653	0.8748
No log	4.6667	126	0.6896	0.7504	0.6896	0.8304
No log	4.7407	128	0.6169	0.7520	0.6169	0.7854
No log	4.8148	130	0.6080	0.7418	0.6080	0.7797
No log	4.8889	132	0.6338	0.7457	0.6338	0.7961
No log	4.9630	134	0.7403	0.7685	0.7403	0.8604
No log	5.0370	136	0.7744	0.7237	0.7744	0.8800
No log	5.1111	138	0.7826	0.7052	0.7826	0.8846
No log	5.1852	140	0.7122	0.7591	0.7122	0.8439
No log	5.2593	142	0.6418	0.7472	0.6418	0.8011
No log	5.3333	144	0.6346	0.7478	0.6346	0.7966
No log	5.4074	146	0.6527	0.7627	0.6527	0.8079
No log	5.4815	148	0.6853	0.7592	0.6853	0.8279
No log	5.5556	150	0.8236	0.7231	0.8236	0.9075
No log	5.6296	152	0.9605	0.6408	0.9605	0.9801
No log	5.7037	154	0.9008	0.6875	0.9008	0.9491
No log	5.7778	156	0.7797	0.7031	0.7797	0.8830
No log	5.8519	158	0.6615	0.7592	0.6615	0.8133
No log	5.9259	160	0.6146	0.7524	0.6146	0.7839
No log	6.0	162	0.6062	0.7570	0.6062	0.7786
No log	6.0741	164	0.6294	0.7393	0.6294	0.7933
No log	6.1481	166	0.7293	0.7294	0.7293	0.8540
No log	6.2222	168	0.7915	0.7001	0.7915	0.8897
No log	6.2963	170	0.8090	0.7086	0.8090	0.8994
No log	6.3704	172	0.7224	0.7216	0.7224	0.8499
No log	6.4444	174	0.6675	0.7589	0.6675	0.8170
No log	6.5185	176	0.6286	0.7586	0.6286	0.7929
No log	6.5926	178	0.6171	0.7506	0.6171	0.7856
No log	6.6667	180	0.6380	0.7832	0.6380	0.7988
No log	6.7407	182	0.6627	0.7826	0.6627	0.8141
No log	6.8148	184	0.6765	0.7697	0.6765	0.8225
No log	6.8889	186	0.6456	0.7869	0.6456	0.8035
No log	6.9630	188	0.6400	0.7869	0.6400	0.8000
No log	7.0370	190	0.6444	0.7869	0.6444	0.8028
No log	7.1111	192	0.6921	0.7583	0.6921	0.8319
No log	7.1852	194	0.7665	0.7355	0.7665	0.8755
No log	7.2593	196	0.8178	0.7153	0.8178	0.9043
No log	7.3333	198	0.8141	0.7153	0.8141	0.9023
No log	7.4074	200	0.7483	0.7357	0.7483	0.8650
No log	7.4815	202	0.6757	0.7827	0.6757	0.8220
No log	7.5556	204	0.6299	0.7758	0.6299	0.7937
No log	7.6296	206	0.6145	0.7650	0.6145	0.7839
No log	7.7037	208	0.6339	0.7832	0.6339	0.7962
No log	7.7778	210	0.6896	0.7787	0.6896	0.8304
No log	7.8519	212	0.7536	0.7361	0.7536	0.8681
No log	7.9259	214	0.7873	0.7230	0.7873	0.8873
No log	8.0	216	0.7770	0.7430	0.7770	0.8815
No log	8.0741	218	0.7333	0.7647	0.7333	0.8563
No log	8.1481	220	0.6890	0.7774	0.6890	0.8301
No log	8.2222	222	0.6520	0.7816	0.6520	0.8075
No log	8.2963	224	0.6430	0.7816	0.6430	0.8019
No log	8.3704	226	0.6543	0.7786	0.6543	0.8089
No log	8.4444	228	0.6920	0.7686	0.6920	0.8319
No log	8.5185	230	0.7155	0.7721	0.7155	0.8459
No log	8.5926	232	0.7262	0.7721	0.7262	0.8522
No log	8.6667	234	0.7230	0.7721	0.7230	0.8503
No log	8.7407	236	0.7095	0.7749	0.7095	0.8423
No log	8.8148	238	0.6969	0.7715	0.6969	0.8348
No log	8.8889	240	0.6801	0.7726	0.6801	0.8247
No log	8.9630	242	0.6564	0.7815	0.6564	0.8102
No log	9.0370	244	0.6506	0.7815	0.6506	0.8066
No log	9.1111	246	0.6598	0.7815	0.6598	0.8123
No log	9.1852	248	0.6603	0.7815	0.6603	0.8126
No log	9.2593	250	0.6597	0.7815	0.6597	0.8122
No log	9.3333	252	0.6547	0.7815	0.6547	0.8091
No log	9.4074	254	0.6568	0.7815	0.6568	0.8104
No log	9.4815	256	0.6555	0.7815	0.6555	0.8097
No log	9.5556	258	0.6538	0.7815	0.6538	0.8086
No log	9.6296	260	0.6524	0.7815	0.6524	0.8077
No log	9.7037	262	0.6564	0.7810	0.6564	0.8102
No log	9.7778	264	0.6601	0.7810	0.6601	0.8125
No log	9.8519	266	0.6626	0.7810	0.6626	0.8140
No log	9.9259	268	0.6644	0.7810	0.6644	0.8151
No log	10.0	270	0.6651	0.7810	0.6651	0.8155

Framework versions

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