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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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metrics: |
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- recall |
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- accuracy |
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model-index: |
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- name: robbert_testrun |
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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_testrun |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5609 |
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- Precisions: 0.8558 |
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- Recall: 0.8234 |
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- F-measure: 0.8375 |
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- Accuracy: 0.9294 |
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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: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 14 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
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| 0.4449 | 1.0 | 285 | 0.3985 | 0.8120 | 0.6963 | 0.7240 | 0.8907 | |
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| 0.19 | 2.0 | 570 | 0.3572 | 0.8522 | 0.7715 | 0.8031 | 0.9118 | |
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| 0.0946 | 3.0 | 855 | 0.3966 | 0.8331 | 0.7816 | 0.8013 | 0.9168 | |
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| 0.0492 | 4.0 | 1140 | 0.4321 | 0.8295 | 0.8127 | 0.8189 | 0.9187 | |
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| 0.034 | 5.0 | 1425 | 0.4523 | 0.8123 | 0.8122 | 0.8097 | 0.9241 | |
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| 0.0221 | 6.0 | 1710 | 0.5082 | 0.8111 | 0.8109 | 0.8097 | 0.9222 | |
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| 0.015 | 7.0 | 1995 | 0.5375 | 0.8587 | 0.7934 | 0.8194 | 0.9212 | |
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| 0.0121 | 8.0 | 2280 | 0.5233 | 0.8542 | 0.8256 | 0.8373 | 0.9292 | |
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| 0.0077 | 9.0 | 2565 | 0.5259 | 0.8277 | 0.8235 | 0.8246 | 0.9286 | |
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| 0.0063 | 10.0 | 2850 | 0.5609 | 0.8558 | 0.8234 | 0.8375 | 0.9294 | |
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| 0.003 | 11.0 | 3135 | 0.5672 | 0.8176 | 0.8197 | 0.8169 | 0.9271 | |
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| 0.002 | 12.0 | 3420 | 0.5968 | 0.8555 | 0.8184 | 0.8347 | 0.9294 | |
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| 0.0021 | 13.0 | 3705 | 0.5846 | 0.8315 | 0.8222 | 0.8263 | 0.9269 | |
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| 0.0016 | 14.0 | 3990 | 0.5905 | 0.8352 | 0.8167 | 0.8251 | 0.9263 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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