Model description

Middle Dutch NER with PassiveAgressiveClassifier

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

TESTING

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameter	Value
memory
steps	[('vectorizer', CountVectorizer()), ('classifier', MultinomialNB())]
verbose	False
vectorizer	CountVectorizer()
classifier	MultinomialNB()
vectorizer__analyzer	word
vectorizer__binary	False
vectorizer__decode_error	strict
vectorizer__dtype	<class 'numpy.int64'>
vectorizer__encoding	utf-8
vectorizer__input	content
vectorizer__lowercase	True
vectorizer__max_df	1.0
vectorizer__max_features
vectorizer__min_df	1
vectorizer__ngram_range	(1, 1)
vectorizer__preprocessor
vectorizer__stop_words
vectorizer__strip_accents
vectorizer__token_pattern	(?u)\b\w\w+\b
vectorizer__tokenizer
vectorizer__vocabulary
classifier__alpha	1.0
classifier__class_prior
classifier__fit_prior	True

Model Plot

The model plot is below.

Pipeline(steps=[('vectorizer', CountVectorizer()),('classifier', MultinomialNB())])

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Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy including 'O'	0.905322
f1 score including 'O	0.905322
precision excluding 'O'	0.892857
recall excluding 'O'	0.404732
f1 excluding 'O'	0.556984

Confusion Matrix

How to Get Started with the Model

[More Information Needed]

Model Card Authors

Alassea TEST

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

BibTeX

@inproceedings{...,year={2022}}