Arabic_FineTuningAraBERT_AugV4_k1_task3_organization_fold0

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: 1.2978
• Qwk: -0.1846
• Mse: 1.2978
• Rmse: 1.1392

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	0.0645	2	4.3454	0.0	4.3454	2.0846
No log	0.1290	4	2.2549	0.0	2.2549	1.5016
No log	0.1935	6	1.3772	0.0678	1.3772	1.1735
No log	0.2581	8	0.9281	0.0	0.9281	0.9634
No log	0.3226	10	1.0110	-0.0476	1.0110	1.0055
No log	0.3871	12	1.2047	-0.3444	1.2047	1.0976
No log	0.4516	14	0.9172	-0.2222	0.9172	0.9577
No log	0.5161	16	0.8983	0.0	0.8983	0.9478
No log	0.5806	18	0.9134	0.0	0.9134	0.9557
No log	0.6452	20	1.0603	-0.0784	1.0603	1.0297
No log	0.7097	22	0.9632	-0.0185	0.9632	0.9814
No log	0.7742	24	0.9629	0.0	0.9629	0.9813
No log	0.8387	26	0.9685	0.0	0.9685	0.9841
No log	0.9032	28	0.9052	0.0	0.9052	0.9514
No log	0.9677	30	0.9881	-0.0185	0.9881	0.9940
No log	1.0323	32	1.2075	0.0	1.2075	1.0989
No log	1.0968	34	1.2888	0.0	1.2888	1.1353
No log	1.1613	36	1.0739	-0.2222	1.0739	1.0363
No log	1.2258	38	0.9035	0.0	0.9035	0.9505
No log	1.2903	40	0.9170	0.0	0.9170	0.9576
No log	1.3548	42	0.9299	0.0	0.9299	0.9643
No log	1.4194	44	0.8769	0.0	0.8769	0.9364
No log	1.4839	46	0.9029	0.0	0.9029	0.9502
No log	1.5484	48	0.9911	-0.0476	0.9911	0.9955
No log	1.6129	50	0.9837	-0.0342	0.9837	0.9918
No log	1.6774	52	0.8938	-0.0185	0.8938	0.9454
No log	1.7419	54	0.8821	0.0	0.8821	0.9392
No log	1.8065	56	0.9110	0.0	0.9110	0.9544
No log	1.8710	58	0.8745	0.0	0.8745	0.9351
No log	1.9355	60	0.8190	0.0	0.8190	0.9050
No log	2.0	62	0.7849	-0.0185	0.7849	0.8859
No log	2.0645	64	0.8720	0.1538	0.8720	0.9338
No log	2.1290	66	0.9228	-0.0185	0.9228	0.9606
No log	2.1935	68	0.9095	-0.0185	0.9095	0.9537
No log	2.2581	70	0.9259	-0.0185	0.9259	0.9623
No log	2.3226	72	0.9235	-0.2222	0.9235	0.9610
No log	2.3871	74	0.9178	0.0	0.9178	0.9580
No log	2.4516	76	0.9123	0.0	0.9123	0.9552
No log	2.5161	78	0.9324	0.0	0.9324	0.9656
No log	2.5806	80	0.9281	0.0	0.9281	0.9634
No log	2.6452	82	0.9440	-0.0185	0.9440	0.9716
No log	2.7097	84	0.9911	-0.2222	0.9911	0.9955
No log	2.7742	86	0.9857	-0.2222	0.9857	0.9928
No log	2.8387	88	1.0367	-0.2222	1.0367	1.0182
No log	2.9032	90	1.0476	-0.2222	1.0476	1.0235
No log	2.9677	92	1.0485	-0.2222	1.0485	1.0240
No log	3.0323	94	1.0954	-0.2222	1.0954	1.0466
No log	3.0968	96	1.0719	-0.2222	1.0719	1.0353
No log	3.1613	98	1.1567	-0.2222	1.1567	1.0755
No log	3.2258	100	1.2101	-0.2222	1.2101	1.1001
No log	3.2903	102	1.1525	-0.2222	1.1525	1.0735
No log	3.3548	104	1.1651	-0.0185	1.1651	1.0794
No log	3.4194	106	1.3233	-0.3660	1.3233	1.1503
No log	3.4839	108	1.3665	-0.3660	1.3665	1.1690
No log	3.5484	110	1.2144	-0.0185	1.2144	1.1020
No log	3.6129	112	1.2106	0.0149	1.2106	1.1003
No log	3.6774	114	1.2808	-0.2222	1.2808	1.1317
No log	3.7419	116	1.2729	-0.2222	1.2729	1.1282
No log	3.8065	118	1.1844	-0.0185	1.1844	1.0883
No log	3.8710	120	1.1039	-0.0185	1.1039	1.0507
No log	3.9355	122	1.0191	-0.0185	1.0191	1.0095
No log	4.0	124	1.0072	-0.0185	1.0072	1.0036
No log	4.0645	126	0.9959	-0.0185	0.9959	0.9980
No log	4.1290	128	0.9892	-0.0185	0.9892	0.9946
No log	4.1935	130	1.0439	-0.0185	1.0439	1.0217
No log	4.2581	132	1.0962	0.0149	1.0962	1.0470
No log	4.3226	134	1.1861	0.0149	1.1861	1.0891
No log	4.3871	136	1.2487	0.0149	1.2487	1.1174
No log	4.4516	138	1.2765	-0.1538	1.2765	1.1298
No log	4.5161	140	1.2936	0.0149	1.2936	1.1373
No log	4.5806	142	1.2869	-0.1538	1.2869	1.1344
No log	4.6452	144	1.2758	-0.1538	1.2758	1.1295
No log	4.7097	146	1.2475	-0.3453	1.2475	1.1169
No log	4.7742	148	1.2417	0.0149	1.2417	1.1143
No log	4.8387	150	1.2469	-0.1538	1.2469	1.1166
No log	4.9032	152	1.2592	-0.2222	1.2592	1.1222
No log	4.9677	154	1.3525	-0.3660	1.3525	1.1630
No log	5.0323	156	1.2917	-0.2222	1.2917	1.1365
No log	5.0968	158	1.1755	-0.2222	1.1755	1.0842
No log	5.1613	160	1.1856	0.0149	1.1856	1.0888
No log	5.2258	162	1.1547	-0.1846	1.1547	1.0746
No log	5.2903	164	1.1420	-0.2222	1.1420	1.0686
No log	5.3548	166	1.2176	-0.3660	1.2176	1.1035
No log	5.4194	168	1.2462	-0.2222	1.2462	1.1163
No log	5.4839	170	1.1616	-0.2222	1.1616	1.0778
No log	5.5484	172	1.1093	-0.1818	1.1093	1.0532
No log	5.6129	174	1.2220	0.0272	1.2220	1.1054
No log	5.6774	176	1.2411	-0.1000	1.2411	1.1140
No log	5.7419	178	1.1603	0.0149	1.1603	1.0772
No log	5.8065	180	1.1719	-0.1846	1.1719	1.0826
No log	5.8710	182	1.1973	-0.1846	1.1973	1.0942
No log	5.9355	184	1.1874	-0.1818	1.1874	1.0897
No log	6.0	186	1.2143	0.0149	1.2143	1.1019
No log	6.0645	188	1.2547	0.0149	1.2547	1.1201
No log	6.1290	190	1.2610	0.0149	1.2610	1.1229
No log	6.1935	192	1.2104	-0.1818	1.2104	1.1002
No log	6.2581	194	1.1775	-0.3538	1.1775	1.0851
No log	6.3226	196	1.1942	-0.3538	1.1942	1.0928
No log	6.3871	198	1.1992	-0.3538	1.1992	1.0951
No log	6.4516	200	1.1951	-0.3538	1.1951	1.0932
No log	6.5161	202	1.2084	-0.1818	1.2084	1.0993
No log	6.5806	204	1.2040	-0.1818	1.2040	1.0973
No log	6.6452	206	1.2125	-0.3538	1.2125	1.1012
No log	6.7097	208	1.2277	-0.1818	1.2277	1.1080
No log	6.7742	210	1.2427	-0.1818	1.2427	1.1148
No log	6.8387	212	1.2568	-0.1818	1.2568	1.1211
No log	6.9032	214	1.2643	-0.1818	1.2643	1.1244
No log	6.9677	216	1.2734	-0.1818	1.2734	1.1285
No log	7.0323	218	1.2881	-0.1818	1.2881	1.1350
No log	7.0968	220	1.3270	-0.1818	1.3270	1.1520
No log	7.1613	222	1.3619	-0.1818	1.3619	1.1670
No log	7.2258	224	1.3564	-0.1818	1.3564	1.1647
No log	7.2903	226	1.3571	-0.3453	1.3571	1.1650
No log	7.3548	228	1.3488	-0.3453	1.3488	1.1614
No log	7.4194	230	1.3465	-0.3538	1.3465	1.1604
No log	7.4839	232	1.3668	-0.1818	1.3668	1.1691
No log	7.5484	234	1.3563	-0.3538	1.3563	1.1646
No log	7.6129	236	1.3424	-0.3538	1.3424	1.1586
No log	7.6774	238	1.3402	-0.3378	1.3402	1.1577
No log	7.7419	240	1.3372	-0.3378	1.3372	1.1564
No log	7.8065	242	1.3234	-0.3378	1.3234	1.1504
No log	7.8710	244	1.3251	-0.3538	1.3251	1.1511
No log	7.9355	246	1.3260	-0.1818	1.3260	1.1515
No log	8.0	248	1.3401	-0.1818	1.3401	1.1576
No log	8.0645	250	1.3498	-0.3134	1.3498	1.1618
No log	8.1290	252	1.3205	-0.1818	1.3205	1.1491
No log	8.1935	254	1.2949	-0.1846	1.2949	1.1379
No log	8.2581	256	1.2907	-0.3378	1.2907	1.1361
No log	8.3226	258	1.2875	-0.3378	1.2875	1.1347
No log	8.3871	260	1.2686	-0.1871	1.2686	1.1263
No log	8.4516	262	1.2426	-0.1846	1.2426	1.1147
No log	8.5161	264	1.2321	-0.1846	1.2321	1.1100
No log	8.5806	266	1.2322	-0.1818	1.2322	1.1101
No log	8.6452	268	1.2393	-0.1818	1.2393	1.1132
No log	8.7097	270	1.2343	-0.1846	1.2343	1.1110
No log	8.7742	272	1.2370	-0.1846	1.2370	1.1122
No log	8.8387	274	1.2505	-0.1846	1.2505	1.1183
No log	8.9032	276	1.2714	-0.1846	1.2714	1.1276
No log	8.9677	278	1.2886	-0.3453	1.2886	1.1352
No log	9.0323	280	1.2992	-0.1846	1.2992	1.1398
No log	9.0968	282	1.3055	-0.1846	1.3055	1.1426
No log	9.1613	284	1.3060	-0.1818	1.3060	1.1428
No log	9.2258	286	1.3058	-0.1818	1.3058	1.1427
No log	9.2903	288	1.3046	-0.1846	1.3046	1.1422
No log	9.3548	290	1.3065	-0.3453	1.3065	1.1430
No log	9.4194	292	1.3075	-0.3453	1.3075	1.1435
No log	9.4839	294	1.3053	-0.3453	1.3053	1.1425
No log	9.5484	296	1.3027	-0.3453	1.3027	1.1414
No log	9.6129	298	1.3025	-0.1846	1.3025	1.1413
No log	9.6774	300	1.3025	-0.1846	1.3025	1.1413
No log	9.7419	302	1.3004	-0.1846	1.3004	1.1403
No log	9.8065	304	1.2986	-0.1846	1.2986	1.1395
No log	9.8710	306	1.2980	-0.1846	1.2980	1.1393
No log	9.9355	308	1.2978	-0.1846	1.2978	1.1392
No log	10.0	310	1.2978	-0.1846	1.2978	1.1392

Framework versions

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