robbert-2023-dutch-base-ft-nlp-xxl

This model is a fine-tuned version of DTAI-KULeuven/robbert-2023-dutch-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0118

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss
2.8326	0.06	10	2.6788
2.7533	0.12	20	2.5468
2.4636	0.19	30	2.5083
2.6891	0.25	40	2.4572
2.5285	0.31	50	2.4016
2.5102	0.37	60	2.4493
2.5021	0.43	70	2.3338
2.4623	0.5	80	2.3530
2.3883	0.56	90	2.3881
2.4773	0.62	100	2.3410
2.4389	0.68	110	2.3148
2.3577	0.75	120	2.3326
2.3497	0.81	130	2.3429
2.3806	0.87	140	2.2916
2.433	0.93	150	2.2801
2.4703	0.99	160	2.2703
2.1623	1.06	170	2.3148
2.3273	1.12	180	2.2596
2.2054	1.18	190	2.1914
2.3115	1.24	200	2.2161
2.109	1.3	210	2.1979
2.375	1.37	220	2.2155
2.2816	1.43	230	2.1992
2.3764	1.49	240	2.1825
2.1229	1.55	250	2.2547
2.1761	1.61	260	2.1983
2.2285	1.68	270	2.2590
2.3079	1.74	280	2.1666
2.2963	1.8	290	2.2389
2.3471	1.86	300	2.1583
2.2031	1.93	310	2.2457
2.3073	1.99	320	2.2102
2.1813	2.05	330	2.1898
2.1958	2.11	340	2.2095
2.2239	2.17	350	2.2107
2.1024	2.24	360	2.2168
2.1895	2.3	370	2.1944
2.1631	2.36	380	2.2287
2.1258	2.42	390	2.1830
2.236	2.48	400	2.1641
2.1493	2.55	410	2.1377
2.1368	2.61	420	2.1640
2.1932	2.67	430	2.2102
2.2071	2.73	440	2.1461
2.2059	2.8	450	2.2398
2.2088	2.86	460	2.1055
2.2002	2.92	470	2.2272
2.1892	2.98	480	2.1622
2.1382	3.04	490	2.1392
2.0724	3.11	500	2.1669
2.09	3.17	510	2.1585
2.1398	3.23	520	2.1565
2.1023	3.29	530	2.1532
1.9628	3.35	540	2.1312
2.1294	3.42	550	2.1337
2.0734	3.48	560	2.1854
2.0503	3.54	570	2.1351
1.9727	3.6	580	2.1715
2.0652	3.66	590	2.1348
1.9942	3.73	600	2.2555
2.0017	3.79	610	2.1412
2.0962	3.85	620	2.1442
2.1212	3.91	630	2.1866
2.0276	3.98	640	2.0766
2.0726	4.04	650	2.0432
2.0554	4.1	660	2.1925
1.9865	4.16	670	2.1344
1.9676	4.22	680	2.1379
2.0355	4.29	690	2.1465
1.9982	4.35	700	2.0861
2.0307	4.41	710	2.1359
2.1014	4.47	720	2.0703
1.9608	4.53	730	2.0898
2.1068	4.6	740	2.2018
2.0099	4.66	750	2.1502
2.0715	4.72	760	2.0592
2.1272	4.78	770	2.1833
2.1069	4.84	780	2.0944
1.96	4.91	790	2.1344
2.0613	4.97	800	2.1366
1.9297	5.03	810	2.0956
2.0172	5.09	820	2.1792
2.0134	5.16	830	2.0792
1.9867	5.22	840	2.1058
1.9391	5.28	850	2.1820
1.8802	5.34	860	2.1274
1.9789	5.4	870	2.0956
2.0665	5.47	880	2.1209
2.0909	5.53	890	2.1557
1.9261	5.59	900	2.0976
2.0246	5.65	910	2.1127
1.9727	5.71	920	2.1670
1.8429	5.78	930	2.0906
2.001	5.84	940	2.0951
1.9363	5.9	950	2.0593
2.0033	5.96	960	2.0947
1.9868	6.02	970	2.0643
1.9011	6.09	980	2.1598
1.9562	6.15	990	2.0961
1.8923	6.21	1000	2.1436
1.9066	6.27	1010	2.0773
1.9805	6.34	1020	2.1261
1.829	6.4	1030	2.0962
1.8745	6.46	1040	2.0881
1.8518	6.52	1050	2.0200
1.9164	6.58	1060	2.0809
1.7968	6.65	1070	2.1169
1.9029	6.71	1080	2.0290
1.9383	6.77	1090	2.0806
1.8375	6.83	1100	2.0816
1.8289	6.89	1110	2.0660
1.894	6.96	1120	2.0229
1.843	7.02	1130	2.1239
1.8515	7.08	1140	2.0687
1.8899	7.14	1150	2.0832
1.903	7.2	1160	2.0882
1.8505	7.27	1170	2.0213
1.8155	7.33	1180	2.0808
1.9355	7.39	1190	2.0649
1.8213	7.45	1200	2.0817
1.9897	7.52	1210	2.1589
1.8044	7.58	1220	2.1288
1.9347	7.64	1230	2.0927
1.9311	7.7	1240	2.0180
1.922	7.76	1250	2.0163
1.8572	7.83	1260	2.0632
1.8858	7.89	1270	2.0255
1.8692	7.95	1280	2.0807
1.9486	8.01	1290	2.0829
1.8184	8.07	1300	2.0721
1.884	8.14	1310	2.0809
1.7928	8.2	1320	2.0462
1.8337	8.26	1330	2.0486
1.8443	8.32	1340	2.0113
1.8546	8.39	1350	2.0348
1.9359	8.45	1360	1.9960
1.874	8.51	1370	2.0198
1.9366	8.57	1380	2.1198
1.8081	8.63	1390	2.0964
1.8655	8.7	1400	2.0571
1.8357	8.76	1410	2.0432
1.8409	8.82	1420	2.0679
1.7785	8.88	1430	2.0930
1.766	8.94	1440	2.1041
1.8542	9.01	1450	2.0035
1.7403	9.07	1460	2.0662
1.8109	9.13	1470	1.9674
1.8191	9.19	1480	2.0274
1.7713	9.25	1490	2.1420
1.7628	9.32	1500	2.0899
1.8273	9.38	1510	1.9969
1.7786	9.44	1520	2.0089
1.7618	9.5	1530	2.0572
1.8247	9.57	1540	2.0710
1.7363	9.63	1550	1.9818
1.8374	9.69	1560	2.0177
1.8838	9.75	1570	2.0528
1.709	9.81	1580	1.9890
1.8743	9.88	1590	2.0105
1.855	9.94	1600	1.9971
1.8659	10.0	1610	2.0052
1.8172	10.06	1620	2.0004
1.7537	10.12	1630	2.1136
1.7822	10.19	1640	2.0685
1.7855	10.25	1650	2.0326
1.7825	10.31	1660	2.0402
1.7391	10.37	1670	2.0100
1.755	10.43	1680	2.0587
1.7649	10.5	1690	2.0548
1.7742	10.56	1700	2.0025
1.8407	10.62	1710	2.0164
1.828	10.68	1720	1.9975
1.7487	10.75	1730	2.0598
1.7521	10.81	1740	2.0318
1.7253	10.87	1750	2.1049
1.7245	10.93	1760	2.0569
1.8093	10.99	1770	1.9909
1.6967	11.06	1780	2.0660
1.7274	11.12	1790	2.0615
1.901	11.18	1800	2.0775
1.7667	11.24	1810	2.0470
1.8173	11.3	1820	2.0141
1.6841	11.37	1830	2.0541
1.7374	11.43	1840	2.0526
1.7307	11.49	1850	2.0060
1.7778	11.55	1860	2.0601
1.7656	11.61	1870	2.0358
1.7167	11.68	1880	2.1360
1.7	11.74	1890	2.0746
1.833	11.8	1900	2.0382
1.7076	11.86	1910	1.9974
1.7491	11.93	1920	2.0558
1.7912	11.99	1930	2.0598
1.7654	12.05	1940	2.0048
1.6612	12.11	1950	2.0457
1.7856	12.17	1960	2.0841
1.8026	12.24	1970	2.1041
1.696	12.3	1980	2.0776
1.7901	12.36	1990	2.0176
1.7881	12.42	2000	2.0118

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0