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

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

robbert-v2-dutch-base-finetuned-emotion-valence is a text classification model from antalvdb. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Jul 26, 2024
Updated: Jul 29, 2024
SHA: 3c1cf1a7

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Base models

pdelobelle/robbert-v2-dutch-base

Model card

From the Hugging Face model README

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

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Created: Jul 26, 2024
Updated: Jul 29, 2024