|
--- |
|
license: mit |
|
base_model: pdelobelle/robbert-v2-dutch-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- recall |
|
- accuracy |
|
model-index: |
|
- name: robbert0410_lrate7.5b32 |
|
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. --> |
|
|
|
# robbert0410_lrate7.5b32 |
|
|
|
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: 0.4559 |
|
- Precisions: 0.8283 |
|
- Recall: 0.8004 |
|
- F-measure: 0.8131 |
|
- Accuracy: 0.9159 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 8 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
|
| 0.2719 | 1.0 | 118 | 0.4068 | 0.8731 | 0.7017 | 0.7281 | 0.8921 | |
|
| 0.2306 | 2.0 | 236 | 0.3380 | 0.7819 | 0.7690 | 0.7699 | 0.9017 | |
|
| 0.1268 | 3.0 | 354 | 0.3740 | 0.7908 | 0.7845 | 0.7790 | 0.9060 | |
|
| 0.0858 | 4.0 | 472 | 0.3834 | 0.7983 | 0.7651 | 0.7763 | 0.9092 | |
|
| 0.0586 | 5.0 | 590 | 0.4200 | 0.8225 | 0.7933 | 0.8045 | 0.9094 | |
|
| 0.0349 | 6.0 | 708 | 0.4474 | 0.8328 | 0.7926 | 0.8094 | 0.9107 | |
|
| 0.0209 | 7.0 | 826 | 0.4559 | 0.8283 | 0.8004 | 0.8131 | 0.9159 | |
|
| 0.013 | 8.0 | 944 | 0.4614 | 0.8175 | 0.8034 | 0.8097 | 0.9144 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.0 |
|
|
