robbert-v2-dutch-base-finetuned-emotion

This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.3545
• Accuracy: 0.52
• F1: 0.5123

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.5586	1.0	25	1.4429	0.42	0.2485
1.4425	2.0	50	1.3576	0.46	0.3533
1.2834	3.0	75	1.3207	0.5	0.4369
1.1051	4.0	100	1.3228	0.48	0.4217
0.9053	5.0	125	1.3705	0.49	0.4302
0.7326	6.0	150	1.4522	0.53	0.5019
0.5724	7.0	175	1.5445	0.53	0.5064
0.4411	8.0	200	1.6560	0.54	0.5120
0.3476	9.0	225	1.7233	0.51	0.4845
0.2324	10.0	250	1.9150	0.52	0.5056
0.1866	11.0	275	2.0207	0.52	0.4975
0.165	12.0	300	2.0863	0.52	0.5094
0.1291	13.0	325	2.1584	0.5	0.4833
0.0762	14.0	350	2.2296	0.55	0.5332
0.0577	15.0	375	2.3171	0.5	0.4986
0.0424	16.0	400	2.4509	0.5	0.4795
0.0253	17.0	425	2.5444	0.49	0.4917
0.0191	18.0	450	2.5894	0.51	0.5031
0.0123	19.0	475	2.7144	0.5	0.4995
0.01	20.0	500	2.7358	0.53	0.5231
0.0086	21.0	525	2.8282	0.48	0.4825
0.0064	22.0	550	2.8421	0.52	0.5244
0.0059	23.0	575	2.9267	0.53	0.5200
0.005	24.0	600	2.9568	0.52	0.5074
0.0044	25.0	625	3.0420	0.47	0.4755
0.0066	26.0	650	3.0421	0.48	0.4881
0.0039	27.0	675	3.1039	0.51	0.4960
0.0033	28.0	700	3.1226	0.51	0.4955
0.0033	29.0	725	3.1215	0.51	0.4999
0.003	30.0	750	3.1649	0.51	0.4980
0.0025	31.0	775	3.1716	0.5	0.4921
0.0028	32.0	800	3.2371	0.5	0.4956
0.0028	33.0	825	3.1730	0.52	0.5154
0.0055	34.0	850	3.1842	0.49	0.4884
0.0022	35.0	875	3.2324	0.51	0.4955
0.0023	36.0	900	3.2221	0.52	0.5089
0.002	37.0	925	3.2756	0.51	0.4981
0.0021	38.0	950	3.2866	0.51	0.5010
0.0019	39.0	975	3.2882	0.51	0.5010
0.0018	40.0	1000	3.2864	0.51	0.4967
0.0017	41.0	1025	3.3101	0.51	0.4967
0.0017	42.0	1050	3.3215	0.52	0.5089
0.0016	43.0	1075	3.3253	0.51	0.5043
0.0056	44.0	1100	3.3118	0.51	0.5043
0.0016	45.0	1125	3.3566	0.51	0.4981
0.0016	46.0	1150	3.3593	0.51	0.4981
0.0016	47.0	1175	3.3638	0.51	0.4981
0.0017	48.0	1200	3.3605	0.52	0.5089
0.0017	49.0	1225	3.3526	0.52	0.5123
0.0016	50.0	1250	3.3545	0.52	0.5123

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1