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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: robbert0210_lrate5b4 |
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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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# robbert0210_lrate5b4 |
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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: 0.3947 |
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- Precisions: 0.7931 |
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- Recall: 0.7419 |
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- F-measure: 0.7620 |
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- Accuracy: 0.8995 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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| 0.71 | 1.0 | 942 | 0.4675 | 0.8146 | 0.6895 | 0.6795 | 0.8713 | |
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| 0.3587 | 2.0 | 1884 | 0.3947 | 0.7931 | 0.7419 | 0.7620 | 0.8995 | |
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| 0.2221 | 3.0 | 2826 | 0.5259 | 0.7885 | 0.7682 | 0.7650 | 0.9021 | |
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| 0.1455 | 4.0 | 3768 | 0.5330 | 0.8071 | 0.7500 | 0.7698 | 0.9051 | |
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| 0.0775 | 5.0 | 4710 | 0.5904 | 0.7773 | 0.7806 | 0.7768 | 0.9035 | |
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| 0.0465 | 6.0 | 5652 | 0.6671 | 0.8375 | 0.7689 | 0.7890 | 0.9038 | |
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| 0.0329 | 7.0 | 6594 | 0.6634 | 0.8002 | 0.7764 | 0.7864 | 0.9073 | |
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| 0.0245 | 8.0 | 7536 | 0.6707 | 0.8325 | 0.7928 | 0.8087 | 0.9118 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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