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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.

Click to expand
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())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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

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}}
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