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license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert_dataaugmentation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robbert_dataaugmentation
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.
It achieves the following results on the evaluation set:
- Loss: 0.6304
- Precisions: 0.8565
- Recall: 0.7966
- F-measure: 0.8182
- Accuracy: 0.9056
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5972 | 1.0 | 284 | 0.4291 | 0.7808 | 0.7248 | 0.7363 | 0.8652 |
| 0.2753 | 2.0 | 568 | 0.4249 | 0.7837 | 0.7521 | 0.7570 | 0.8811 |
| 0.1379 | 3.0 | 852 | 0.5021 | 0.8379 | 0.7750 | 0.7955 | 0.8815 |
| 0.0776 | 4.0 | 1136 | 0.6344 | 0.8567 | 0.7657 | 0.7907 | 0.8842 |
| 0.0401 | 5.0 | 1420 | 0.6621 | 0.8442 | 0.7622 | 0.7856 | 0.8884 |
| 0.0319 | 6.0 | 1704 | 0.6013 | 0.8435 | 0.7870 | 0.8010 | 0.8969 |
| 0.0205 | 7.0 | 1988 | 0.6304 | 0.8565 | 0.7966 | 0.8182 | 0.9056 |
| 0.0138 | 8.0 | 2272 | 0.6804 | 0.8538 | 0.7732 | 0.7896 | 0.9030 |
| 0.0096 | 9.0 | 2556 | 0.7395 | 0.8274 | 0.7696 | 0.7862 | 0.8923 |
| 0.005 | 10.0 | 2840 | 0.7293 | 0.8531 | 0.7846 | 0.8054 | 0.8967 |
| 0.0034 | 11.0 | 3124 | 0.7385 | 0.8621 | 0.7929 | 0.8105 | 0.9022 |
| 0.0047 | 12.0 | 3408 | 0.7428 | 0.8575 | 0.7953 | 0.8155 | 0.9061 |
| 0.004 | 13.0 | 3692 | 0.7524 | 0.8617 | 0.7954 | 0.8152 | 0.9024 |
| 0.0023 | 14.0 | 3976 | 0.7515 | 0.8636 | 0.7957 | 0.8174 | 0.9041 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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