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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: robbert0410_lrate10b16 |
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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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# robbert0410_lrate10b16 |
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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: 0.4783 |
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- Precisions: 0.8324 |
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- Recall: 0.8123 |
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- F-measure: 0.8208 |
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- Accuracy: 0.9164 |
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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: 0.0001 |
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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: 8 |
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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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| 0.6379 | 1.0 | 236 | 0.4156 | 0.8657 | 0.6790 | 0.6955 | 0.8798 | |
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| 0.3257 | 2.0 | 472 | 0.3378 | 0.7529 | 0.7397 | 0.7336 | 0.8932 | |
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| 0.1977 | 3.0 | 708 | 0.3737 | 0.7960 | 0.7383 | 0.7451 | 0.9003 | |
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| 0.1197 | 4.0 | 944 | 0.4060 | 0.8446 | 0.7503 | 0.7696 | 0.9025 | |
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| 0.0659 | 5.0 | 1180 | 0.4428 | 0.7851 | 0.7731 | 0.7779 | 0.9063 | |
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| 0.0447 | 6.0 | 1416 | 0.4972 | 0.8285 | 0.7991 | 0.8124 | 0.9127 | |
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| 0.0256 | 7.0 | 1652 | 0.4783 | 0.8324 | 0.8123 | 0.8208 | 0.9164 | |
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| 0.0173 | 8.0 | 1888 | 0.4918 | 0.8251 | 0.8082 | 0.8159 | 0.9169 | |
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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 0.14.0 |
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