joris/manifesto-dutch-binary-relevance

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manifesto-dutch-binary-relevance

This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base.

Example usage

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

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —optimizer: {'name': 'AdamW', 'weightdecay': 0.004, 'clipnorm': None, 'globalclipnorm': None, 'clipvalue': None, 'useema': False, 'emamomentum': 0.99, 'emaoverwritefrequency': None, 'jitcompile': True, 'islegacyoptimizer': False, 'learningrate': 2e-05, 'beta1': 0.9, 'beta2': 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