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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_lrate10b32 |
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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_lrate10b32 |
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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: 0.4435 |
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- Precisions: 0.8143 |
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- Recall: 0.8300 |
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- F-measure: 0.8201 |
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- Accuracy: 0.9162 |
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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: 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: 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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| 0.6187 | 1.0 | 118 | 0.3807 | 0.8761 | 0.6803 | 0.6943 | 0.8771 | |
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| 0.3045 | 2.0 | 236 | 0.3297 | 0.7915 | 0.7331 | 0.7475 | 0.8966 | |
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| 0.1748 | 3.0 | 354 | 0.3503 | 0.7831 | 0.7466 | 0.7553 | 0.9005 | |
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| 0.1059 | 4.0 | 472 | 0.3670 | 0.8133 | 0.7784 | 0.7893 | 0.9086 | |
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| 0.0649 | 5.0 | 590 | 0.3926 | 0.7875 | 0.7973 | 0.7908 | 0.9053 | |
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| 0.0376 | 6.0 | 708 | 0.4213 | 0.7906 | 0.7922 | 0.7906 | 0.9082 | |
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| 0.0221 | 7.0 | 826 | 0.4435 | 0.8143 | 0.8300 | 0.8201 | 0.9162 | |
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| 0.014 | 8.0 | 944 | 0.4521 | 0.8170 | 0.8047 | 0.8090 | 0.9142 | |
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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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