README.md

metadata

license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
  - generated_from_trainer
model-index:
  - name: robbert-v2-dutch-base-finetuned-emotion-valence
    results: []

robbert-v2-dutch-base-finetuned-emotion-valence

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: 0.0317
• Rmse: 0.1781

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	Rmse
0.0813	1.0	25	0.0510	0.2258
0.0445	2.0	50	0.0381	0.1952
0.0409	3.0	75	0.0466	0.2158
0.0308	4.0	100	0.0351	0.1874
0.0257	5.0	125	0.0393	0.1983
0.0231	6.0	150	0.0442	0.2103
0.0203	7.0	175	0.0447	0.2115
0.0191	8.0	200	0.0372	0.1929
0.0156	9.0	225	0.0425	0.2061
0.0154	10.0	250	0.0367	0.1917
0.0138	11.0	275	0.0365	0.1910
0.0128	12.0	300	0.0432	0.2078
0.0137	13.0	325	0.0329	0.1814
0.0118	14.0	350	0.0327	0.1809
0.0118	15.0	375	0.0378	0.1945
0.0109	16.0	400	0.0360	0.1897
0.0103	17.0	425	0.0325	0.1803
0.0096	18.0	450	0.0327	0.1809
0.0091	19.0	475	0.0430	0.2072
0.0081	20.0	500	0.0345	0.1856
0.0094	21.0	525	0.0365	0.1912
0.0084	22.0	550	0.0350	0.1870
0.0075	23.0	575	0.0324	0.1800
0.0069	24.0	600	0.0330	0.1816
0.0087	25.0	625	0.0347	0.1863
0.0079	26.0	650	0.0297	0.1722
0.0071	27.0	675	0.0311	0.1763
0.0076	28.0	700	0.0322	0.1795
0.0064	29.0	725	0.0338	0.1839
0.0067	30.0	750	0.0326	0.1806
0.0061	31.0	775	0.0327	0.1808
0.0064	32.0	800	0.0339	0.1842
0.0062	33.0	825	0.0300	0.1732
0.0062	34.0	850	0.0331	0.1819
0.0055	35.0	875	0.0318	0.1782
0.0059	36.0	900	0.0323	0.1797
0.0056	37.0	925	0.0311	0.1765
0.0055	38.0	950	0.0310	0.1762
0.0053	39.0	975	0.0325	0.1802
0.0056	40.0	1000	0.0310	0.1761
0.0054	41.0	1025	0.0323	0.1799
0.0057	42.0	1050	0.0351	0.1873
0.0053	43.0	1075	0.0347	0.1861
0.0054	44.0	1100	0.0330	0.1816
0.0059	45.0	1125	0.0313	0.1769
0.0053	46.0	1150	0.0312	0.1766
0.0051	47.0	1175	0.0325	0.1804
0.0057	48.0	1200	0.0304	0.1745
0.0048	49.0	1225	0.0317	0.1782
0.005	50.0	1250	0.0317	0.1781

Framework versions

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