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---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_keras_callback
model-index:
- name: manifesto-dutch-binary-relevance
results: []
language:
- nl
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# manifesto-dutch-binary-relevance
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base).
## Example usage
```python
from transformers import pipeline
pipe = pipeline("text-classification",
model="joris/manifesto-dutch-binary-relevance",
trust_remote_code=True)
print(pipe("De digitale versie lees je op d66.nl/verkiezingsprogramma"))
print(pipe("Duizenden studenten, net afgestudeerden en starters hebben op dit moment geen zicht op een (betaalbare) woning."))
## [{'label': 'LABEL_1', 'score': 0.9609444737434387}] # is 000
## [{'label': 'LABEL_0', 'score': 0.9993253946304321}] # some other code
```
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
| | Precision | Recall | F1-Score | Support |
|-----------|-----------|--------|----------|----------|
| 0 | 0.98 | 0.99 | 0.99 | 10043 |
| 1 | 0.88 | 0.76 | 0.82 | 714 |
| Accuracy | | | 0.98 | 10757 |
| Macro avg | 0.93 | 0.88 | 0.90 | 10757 |
| Weighted avg | 0.98 | 0.98 | 0.98 | 10757 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamW', 'weight_decay': 0.004, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Tokenizers 0.14.1 |