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-dominance
    results: []

robbert-v2-dutch-base-finetuned-emotion-dominance

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: 0.0394
• Rmse: 0.1984

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
0.1031	1.0	25	0.0433	0.2080
0.0444	2.0	50	0.0433	0.2082
0.0371	3.0	75	0.0443	0.2106
0.0318	4.0	100	0.0470	0.2167
0.0289	5.0	125	0.0611	0.2472
0.0267	6.0	150	0.0394	0.1984
0.0217	7.0	175	0.0419	0.2047
0.0199	8.0	200	0.0412	0.2029
0.0173	9.0	225	0.0477	0.2184
0.0164	10.0	250	0.0490	0.2213
0.0145	11.0	275	0.0417	0.2043
0.0126	12.0	300	0.0454	0.2130
0.0149	13.0	325	0.0421	0.2052
0.0113	14.0	350	0.0424	0.2059
0.0117	15.0	375	0.0426	0.2063
0.0107	16.0	400	0.0437	0.2091
0.0097	17.0	425	0.0406	0.2015
0.0102	18.0	450	0.0488	0.2209
0.01	19.0	475	0.0421	0.2053
0.0101	20.0	500	0.0383	0.1957
0.01	21.0	525	0.0404	0.2009
0.0092	22.0	550	0.0522	0.2285
0.0086	23.0	575	0.0390	0.1975
0.0085	24.0	600	0.0455	0.2133
0.0075	25.0	625	0.0427	0.2066
0.0071	26.0	650	0.0398	0.1995
0.0073	27.0	675	0.0424	0.2060
0.0079	28.0	700	0.0422	0.2055
0.0068	29.0	725	0.0388	0.1970
0.0071	30.0	750	0.0382	0.1953
0.0065	31.0	775	0.0394	0.1986
0.0066	32.0	800	0.0390	0.1975
0.006	33.0	825	0.0395	0.1987
0.0059	34.0	850	0.0404	0.2010
0.0062	35.0	875	0.0378	0.1944
0.0063	36.0	900	0.0374	0.1935
0.0056	37.0	925	0.0398	0.1995
0.0058	38.0	950	0.0383	0.1957
0.0056	39.0	975	0.0378	0.1945
0.0057	40.0	1000	0.0407	0.2017
0.006	41.0	1025	0.0392	0.1980
0.0057	42.0	1050	0.0398	0.1994
0.0053	43.0	1075	0.0377	0.1941
0.0056	44.0	1100	0.0392	0.1980
0.0053	45.0	1125	0.0410	0.2024
0.0057	46.0	1150	0.0396	0.1990
0.0048	47.0	1175	0.0398	0.1995
0.0054	48.0	1200	0.0397	0.1992
0.005	49.0	1225	0.0389	0.1972
0.0051	50.0	1250	0.0394	0.1984

Framework versions

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