ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run1_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: 1.0604
• Qwk: 0.6326
• Mse: 1.0604
• Rmse: 1.0297

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	0.0690	2	2.2075	0.0523	2.2075	1.4858
No log	0.1379	4	1.5020	0.2003	1.5020	1.2256
No log	0.2069	6	1.4887	0.1504	1.4887	1.2201
No log	0.2759	8	1.6544	0.1402	1.6544	1.2862
No log	0.3448	10	1.6839	0.1971	1.6839	1.2976
No log	0.4138	12	1.7452	0.2358	1.7452	1.3211
No log	0.4828	14	1.6096	0.2415	1.6096	1.2687
No log	0.5517	16	1.7261	0.2443	1.7261	1.3138
No log	0.6207	18	1.8083	0.3050	1.8083	1.3447
No log	0.6897	20	1.4812	0.3276	1.4812	1.2170
No log	0.7586	22	1.4893	0.3195	1.4893	1.2204
No log	0.8276	24	1.8315	0.3425	1.8315	1.3533
No log	0.8966	26	1.7551	0.3255	1.7551	1.3248
No log	0.9655	28	1.5377	0.4101	1.5377	1.2400
No log	1.0345	30	1.2794	0.4398	1.2794	1.1311
No log	1.1034	32	1.4198	0.4728	1.4198	1.1916
No log	1.1724	34	1.8963	0.3635	1.8963	1.3771
No log	1.2414	36	1.6886	0.4585	1.6886	1.2995
No log	1.3103	38	1.3896	0.4772	1.3896	1.1788
No log	1.3793	40	1.1757	0.5026	1.1757	1.0843
No log	1.4483	42	1.1766	0.5103	1.1766	1.0847
No log	1.5172	44	1.3178	0.5211	1.3178	1.1480
No log	1.5862	46	1.4922	0.5102	1.4922	1.2216
No log	1.6552	48	1.7426	0.4653	1.7426	1.3201
No log	1.7241	50	1.4646	0.5226	1.4646	1.2102
No log	1.7931	52	1.0729	0.5137	1.0729	1.0358
No log	1.8621	54	1.0305	0.5171	1.0305	1.0152
No log	1.9310	56	1.2636	0.5527	1.2636	1.1241
No log	2.0	58	1.7816	0.5128	1.7816	1.3348
No log	2.0690	60	1.6130	0.5381	1.6130	1.2700
No log	2.1379	62	1.1367	0.5758	1.1367	1.0661
No log	2.2069	64	1.1772	0.5660	1.1772	1.0850
No log	2.2759	66	1.4330	0.5691	1.4330	1.1971
No log	2.3448	68	1.1856	0.5599	1.1856	1.0888
No log	2.4138	70	0.9823	0.5830	0.9823	0.9911
No log	2.4828	72	1.1306	0.5722	1.1306	1.0633
No log	2.5517	74	1.7100	0.4928	1.7100	1.3077
No log	2.6207	76	1.8926	0.4721	1.8926	1.3757
No log	2.6897	78	1.5159	0.5206	1.5159	1.2312
No log	2.7586	80	1.4943	0.5286	1.4943	1.2224
No log	2.8276	82	1.2586	0.5340	1.2586	1.1219
No log	2.8966	84	0.9951	0.5987	0.9951	0.9976
No log	2.9655	86	1.0850	0.5720	1.0850	1.0416
No log	3.0345	88	1.2346	0.5607	1.2346	1.1111
No log	3.1034	90	1.5158	0.5392	1.5158	1.2312
No log	3.1724	92	2.1689	0.4748	2.1689	1.4727
No log	3.2414	94	2.0487	0.4961	2.0487	1.4313
No log	3.3103	96	1.5069	0.5453	1.5069	1.2276
No log	3.3793	98	1.5241	0.5342	1.5241	1.2345
No log	3.4483	100	1.4162	0.5389	1.4162	1.1901
No log	3.5172	102	1.3407	0.5830	1.3407	1.1579
No log	3.5862	104	1.0041	0.5757	1.0041	1.0021
No log	3.6552	106	0.8172	0.6045	0.8172	0.9040
No log	3.7241	108	0.8078	0.6202	0.8078	0.8988
No log	3.7931	110	0.9760	0.5973	0.9760	0.9879
No log	3.8621	112	1.1849	0.5612	1.1849	1.0885
No log	3.9310	114	1.0627	0.5810	1.0627	1.0309
No log	4.0	116	0.9528	0.6105	0.9528	0.9761
No log	4.0690	118	0.9544	0.5915	0.9544	0.9769
No log	4.1379	120	0.9016	0.6027	0.9016	0.9495
No log	4.2069	122	0.9961	0.5825	0.9961	0.9980
No log	4.2759	124	1.3100	0.5826	1.3100	1.1446
No log	4.3448	126	1.3737	0.5958	1.3737	1.1720
No log	4.4138	128	1.2074	0.5807	1.2074	1.0988
No log	4.4828	130	1.0044	0.5984	1.0044	1.0022
No log	4.5517	132	1.0557	0.5809	1.0557	1.0275
No log	4.6207	134	1.2723	0.5836	1.2723	1.1280
No log	4.6897	136	1.2804	0.5736	1.2804	1.1315
No log	4.7586	138	1.0236	0.5479	1.0236	1.0117
No log	4.8276	140	0.9390	0.5849	0.9390	0.9690
No log	4.8966	142	1.0379	0.5338	1.0379	1.0188
No log	4.9655	144	1.3784	0.5909	1.3784	1.1740
No log	5.0345	146	1.5627	0.5657	1.5627	1.2501
No log	5.1034	148	1.3516	0.6001	1.3516	1.1626
No log	5.1724	150	1.1543	0.5785	1.1543	1.0744
No log	5.2414	152	1.1613	0.5922	1.1613	1.0776
No log	5.3103	154	1.4134	0.5771	1.4134	1.1889
No log	5.3793	156	1.7090	0.5472	1.7090	1.3073
No log	5.4483	158	1.8436	0.5196	1.8436	1.3578
No log	5.5172	160	1.5204	0.5710	1.5204	1.2331
No log	5.5862	162	1.1513	0.5926	1.1513	1.0730
No log	5.6552	164	1.1968	0.6030	1.1968	1.0940
No log	5.7241	166	1.5020	0.5528	1.5020	1.2256
No log	5.7931	168	1.6110	0.5405	1.6110	1.2693
No log	5.8621	170	1.4813	0.5473	1.4813	1.2171
No log	5.9310	172	1.4290	0.5619	1.4290	1.1954
No log	6.0	174	1.1447	0.5971	1.1447	1.0699
No log	6.0690	176	1.0517	0.5755	1.0517	1.0255
No log	6.1379	178	0.9704	0.6145	0.9704	0.9851
No log	6.2069	180	0.9637	0.6145	0.9637	0.9817
No log	6.2759	182	1.0428	0.6055	1.0428	1.0212
No log	6.3448	184	1.0442	0.6558	1.0442	1.0218
No log	6.4138	186	0.9113	0.6608	0.9113	0.9546
No log	6.4828	188	0.8840	0.6685	0.8840	0.9402
No log	6.5517	190	0.8882	0.6574	0.8882	0.9424
No log	6.6207	192	0.8998	0.6390	0.8998	0.9486
No log	6.6897	194	0.8338	0.6345	0.8338	0.9131
No log	6.7586	196	0.8207	0.6430	0.8207	0.9059
No log	6.8276	198	0.8629	0.6304	0.8629	0.9289
No log	6.8966	200	0.9802	0.6419	0.9802	0.9900
No log	6.9655	202	1.1212	0.6406	1.1212	1.0589
No log	7.0345	204	1.1229	0.6315	1.1229	1.0597
No log	7.1034	206	0.9867	0.6368	0.9867	0.9933
No log	7.1724	208	0.9110	0.6347	0.9110	0.9545
No log	7.2414	210	0.8407	0.6042	0.8407	0.9169
No log	7.3103	212	0.8411	0.6042	0.8411	0.9171
No log	7.3793	214	0.9024	0.6168	0.9024	0.9500
No log	7.4483	216	1.0185	0.5901	1.0185	1.0092
No log	7.5172	218	1.1304	0.5944	1.1304	1.0632
No log	7.5862	220	1.1866	0.5957	1.1866	1.0893
No log	7.6552	222	1.1249	0.6116	1.1249	1.0606
No log	7.7241	224	1.0311	0.6393	1.0311	1.0154
No log	7.7931	226	0.9980	0.6377	0.9980	0.9990
No log	7.8621	228	1.0058	0.6389	1.0058	1.0029
No log	7.9310	230	1.0450	0.6426	1.0450	1.0223
No log	8.0	232	1.1253	0.6310	1.1253	1.0608
No log	8.0690	234	1.2088	0.6080	1.2088	1.0995
No log	8.1379	236	1.1849	0.6188	1.1849	1.0885
No log	8.2069	238	1.0754	0.6510	1.0754	1.0370
No log	8.2759	240	0.9988	0.6326	0.9988	0.9994
No log	8.3448	242	1.0049	0.6326	1.0049	1.0025
No log	8.4138	244	1.0754	0.6426	1.0754	1.0370
No log	8.4828	246	1.1933	0.6138	1.1933	1.0924
No log	8.5517	248	1.2370	0.5991	1.2370	1.1122
No log	8.6207	250	1.1965	0.6030	1.1965	1.0938
No log	8.6897	252	1.1078	0.6609	1.1078	1.0525
No log	8.7586	254	1.0294	0.6326	1.0294	1.0146
No log	8.8276	256	0.9778	0.6416	0.9778	0.9889
No log	8.8966	258	0.9632	0.6317	0.9632	0.9814
No log	8.9655	260	0.9735	0.6352	0.9735	0.9867
No log	9.0345	262	0.9980	0.6186	0.9980	0.9990
No log	9.1034	264	1.0051	0.6220	1.0051	1.0025
No log	9.1724	266	0.9976	0.6220	0.9976	0.9988
No log	9.2414	268	0.9914	0.6122	0.9914	0.9957
No log	9.3103	270	0.9958	0.6156	0.9958	0.9979
No log	9.3793	272	1.0156	0.6236	1.0156	1.0078
No log	9.4483	274	1.0361	0.6236	1.0361	1.0179
No log	9.5172	276	1.0535	0.6326	1.0535	1.0264
No log	9.5862	278	1.0633	0.6326	1.0633	1.0312
No log	9.6552	280	1.0724	0.6312	1.0724	1.0356
No log	9.7241	282	1.0704	0.6312	1.0704	1.0346
No log	9.7931	284	1.0669	0.6312	1.0669	1.0329
No log	9.8621	286	1.0673	0.6312	1.0673	1.0331
No log	9.9310	288	1.0628	0.6326	1.0628	1.0309
No log	10.0	290	1.0604	0.6326	1.0604	1.0297

Framework versions

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