README.md

---
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