| | --- |
| | 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 |
| | |
