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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: robbert2809_lrate10 |
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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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# robbert2809_lrate10 |
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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: 0.3760 |
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- Precision: 0.7615 |
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- Recall: 0.7517 |
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- F1: 0.7566 |
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- Accuracy: 0.8990 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 118 | 0.4116 | 0.7166 | 0.7104 | 0.7135 | 0.8906 | |
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| No log | 2.0 | 236 | 0.3760 | 0.7615 | 0.7517 | 0.7566 | 0.8990 | |
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| No log | 3.0 | 354 | 0.4114 | 0.7428 | 0.7692 | 0.7558 | 0.9019 | |
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| No log | 4.0 | 472 | 0.4230 | 0.7881 | 0.7844 | 0.7862 | 0.9131 | |
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| 0.1527 | 5.0 | 590 | 0.4550 | 0.7858 | 0.7716 | 0.7786 | 0.9092 | |
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| 0.1527 | 6.0 | 708 | 0.4553 | 0.7876 | 0.8019 | 0.7947 | 0.9188 | |
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| 0.1527 | 7.0 | 826 | 0.4824 | 0.7864 | 0.8001 | 0.7932 | 0.9181 | |
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| 0.1527 | 8.0 | 944 | 0.4973 | 0.7922 | 0.7978 | 0.7950 | 0.9196 | |
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### Framework versions |
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- Transformers 4.33.2 |
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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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