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--- |
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license: mit |
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base_model: pdelobelle/robbert-v2-dutch-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: robbert-v2-dutch-base-finetuned-emotion-valence |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# robbert-v2-dutch-base-finetuned-emotion-valence |
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This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0317 |
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- Rmse: 0.1781 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rmse | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.0813 | 1.0 | 25 | 0.0510 | 0.2258 | |
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| 0.0445 | 2.0 | 50 | 0.0381 | 0.1952 | |
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| 0.0409 | 3.0 | 75 | 0.0466 | 0.2158 | |
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| 0.0308 | 4.0 | 100 | 0.0351 | 0.1874 | |
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| 0.0257 | 5.0 | 125 | 0.0393 | 0.1983 | |
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| 0.0231 | 6.0 | 150 | 0.0442 | 0.2103 | |
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| 0.0203 | 7.0 | 175 | 0.0447 | 0.2115 | |
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| 0.0191 | 8.0 | 200 | 0.0372 | 0.1929 | |
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| 0.0156 | 9.0 | 225 | 0.0425 | 0.2061 | |
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| 0.0154 | 10.0 | 250 | 0.0367 | 0.1917 | |
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| 0.0138 | 11.0 | 275 | 0.0365 | 0.1910 | |
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| 0.0128 | 12.0 | 300 | 0.0432 | 0.2078 | |
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| 0.0137 | 13.0 | 325 | 0.0329 | 0.1814 | |
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| 0.0118 | 14.0 | 350 | 0.0327 | 0.1809 | |
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| 0.0118 | 15.0 | 375 | 0.0378 | 0.1945 | |
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| 0.0109 | 16.0 | 400 | 0.0360 | 0.1897 | |
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| 0.0103 | 17.0 | 425 | 0.0325 | 0.1803 | |
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| 0.0096 | 18.0 | 450 | 0.0327 | 0.1809 | |
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| 0.0091 | 19.0 | 475 | 0.0430 | 0.2072 | |
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| 0.0081 | 20.0 | 500 | 0.0345 | 0.1856 | |
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| 0.0094 | 21.0 | 525 | 0.0365 | 0.1912 | |
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| 0.0084 | 22.0 | 550 | 0.0350 | 0.1870 | |
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| 0.0075 | 23.0 | 575 | 0.0324 | 0.1800 | |
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| 0.0069 | 24.0 | 600 | 0.0330 | 0.1816 | |
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| 0.0087 | 25.0 | 625 | 0.0347 | 0.1863 | |
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| 0.0079 | 26.0 | 650 | 0.0297 | 0.1722 | |
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| 0.0071 | 27.0 | 675 | 0.0311 | 0.1763 | |
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| 0.0076 | 28.0 | 700 | 0.0322 | 0.1795 | |
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| 0.0064 | 29.0 | 725 | 0.0338 | 0.1839 | |
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| 0.0067 | 30.0 | 750 | 0.0326 | 0.1806 | |
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| 0.0061 | 31.0 | 775 | 0.0327 | 0.1808 | |
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| 0.0064 | 32.0 | 800 | 0.0339 | 0.1842 | |
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| 0.0062 | 33.0 | 825 | 0.0300 | 0.1732 | |
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| 0.0062 | 34.0 | 850 | 0.0331 | 0.1819 | |
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| 0.0055 | 35.0 | 875 | 0.0318 | 0.1782 | |
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| 0.0059 | 36.0 | 900 | 0.0323 | 0.1797 | |
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| 0.0056 | 37.0 | 925 | 0.0311 | 0.1765 | |
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| 0.0055 | 38.0 | 950 | 0.0310 | 0.1762 | |
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| 0.0053 | 39.0 | 975 | 0.0325 | 0.1802 | |
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| 0.0056 | 40.0 | 1000 | 0.0310 | 0.1761 | |
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| 0.0054 | 41.0 | 1025 | 0.0323 | 0.1799 | |
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| 0.0057 | 42.0 | 1050 | 0.0351 | 0.1873 | |
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| 0.0053 | 43.0 | 1075 | 0.0347 | 0.1861 | |
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| 0.0054 | 44.0 | 1100 | 0.0330 | 0.1816 | |
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| 0.0059 | 45.0 | 1125 | 0.0313 | 0.1769 | |
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| 0.0053 | 46.0 | 1150 | 0.0312 | 0.1766 | |
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| 0.0051 | 47.0 | 1175 | 0.0325 | 0.1804 | |
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| 0.0057 | 48.0 | 1200 | 0.0304 | 0.1745 | |
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| 0.0048 | 49.0 | 1225 | 0.0317 | 0.1782 | |
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| 0.005 | 50.0 | 1250 | 0.0317 | 0.1781 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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