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Training completed!

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README.md ADDED
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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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+
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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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+
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+ # robbert-v2-dutch-base-finetuned-emotion-valence
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+
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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: 0.0327
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+ - Rmse: 0.1808
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rmse |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.0887 | 1.0 | 25 | 0.0611 | 0.2472 |
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+ | 0.044 | 2.0 | 50 | 0.0403 | 0.2008 |
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+ | 0.0356 | 3.0 | 75 | 0.0435 | 0.2085 |
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+ | 0.0301 | 4.0 | 100 | 0.0415 | 0.2038 |
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+ | 0.0246 | 5.0 | 125 | 0.0377 | 0.1941 |
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+ | 0.0224 | 6.0 | 150 | 0.0355 | 0.1885 |
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+ | 0.0218 | 7.0 | 175 | 0.0379 | 0.1946 |
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+ | 0.0174 | 8.0 | 200 | 0.0436 | 0.2088 |
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+ | 0.0156 | 9.0 | 225 | 0.0361 | 0.1901 |
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+ | 0.0138 | 10.0 | 250 | 0.0379 | 0.1947 |
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+ | 0.0135 | 11.0 | 275 | 0.0386 | 0.1965 |
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+ | 0.013 | 12.0 | 300 | 0.0402 | 0.2005 |
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+ | 0.0125 | 13.0 | 325 | 0.0325 | 0.1804 |
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+ | 0.0117 | 14.0 | 350 | 0.0349 | 0.1868 |
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+ | 0.0109 | 15.0 | 375 | 0.0366 | 0.1914 |
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+ | 0.0108 | 16.0 | 400 | 0.0382 | 0.1953 |
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+ | 0.0091 | 17.0 | 425 | 0.0336 | 0.1833 |
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+ | 0.0097 | 18.0 | 450 | 0.0325 | 0.1802 |
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+ | 0.0098 | 19.0 | 475 | 0.0406 | 0.2014 |
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+ | 0.0094 | 20.0 | 500 | 0.0330 | 0.1816 |
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+ | 0.0088 | 21.0 | 525 | 0.0349 | 0.1868 |
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+ | 0.0087 | 22.0 | 550 | 0.0337 | 0.1835 |
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+ | 0.0079 | 23.0 | 575 | 0.0340 | 0.1845 |
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+ | 0.0074 | 24.0 | 600 | 0.0372 | 0.1928 |
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+ | 0.007 | 25.0 | 625 | 0.0345 | 0.1856 |
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+ | 0.0072 | 26.0 | 650 | 0.0333 | 0.1824 |
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+ | 0.0074 | 27.0 | 675 | 0.0308 | 0.1756 |
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+ | 0.0071 | 28.0 | 700 | 0.0314 | 0.1772 |
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+ | 0.0067 | 29.0 | 725 | 0.0314 | 0.1772 |
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+ | 0.0065 | 30.0 | 750 | 0.0333 | 0.1824 |
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+ | 0.0072 | 31.0 | 775 | 0.0337 | 0.1837 |
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+ | 0.0065 | 32.0 | 800 | 0.0351 | 0.1873 |
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+ | 0.0057 | 33.0 | 825 | 0.0330 | 0.1818 |
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+ | 0.0067 | 34.0 | 850 | 0.0367 | 0.1915 |
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+ | 0.0061 | 35.0 | 875 | 0.0358 | 0.1893 |
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+ | 0.0062 | 36.0 | 900 | 0.0353 | 0.1879 |
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+ | 0.006 | 37.0 | 925 | 0.0317 | 0.1779 |
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+ | 0.0059 | 38.0 | 950 | 0.0331 | 0.1819 |
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+ | 0.0058 | 39.0 | 975 | 0.0308 | 0.1755 |
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+ | 0.0057 | 40.0 | 1000 | 0.0338 | 0.1838 |
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+ | 0.0055 | 41.0 | 1025 | 0.0324 | 0.1800 |
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+ | 0.0056 | 42.0 | 1050 | 0.0333 | 0.1824 |
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+ | 0.0054 | 43.0 | 1075 | 0.0331 | 0.1819 |
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+ | 0.0062 | 44.0 | 1100 | 0.0329 | 0.1813 |
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+ | 0.0056 | 45.0 | 1125 | 0.0325 | 0.1802 |
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+ | 0.0051 | 46.0 | 1150 | 0.0324 | 0.1801 |
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+ | 0.0056 | 47.0 | 1175 | 0.0322 | 0.1795 |
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+ | 0.0056 | 48.0 | 1200 | 0.0331 | 0.1818 |
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+ | 0.0053 | 49.0 | 1225 | 0.0322 | 0.1794 |
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+ | 0.0056 | 50.0 | 1250 | 0.0327 | 0.1808 |
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+
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+
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+ ### Framework versions
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+
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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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