metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - text-classification
model_file: middle_dutch_text-classification.pkl
widget:
  - text: >-
      Wje mayelin onghereit pieter van der beyke andries jnghel jehan de bleyer
      en jacob de bere Sceipenen mire vrauwen der Abbedessen van meessine jn
      nortscoten ende jn zuutscoten jn dien tyden doen te weitene tolle den
      gonen die deisen chaertre sullen zien of horen leisen Dat ledenaerd van
      den cole portre jn ypre heift ghecocht en ghecreighe ervelike te sine bouf
      en te sijns hoyrs bouf jeghen jacob cruken poortre jn ypre .vijf.
      vierendeel ymeits lants ligghende jn de proghye van zuutscote namelike
      binder vierscare mire vrauwen vors dat es te weitene oostwaert over dypre
      jeghe de woninghe marie sbuerechgrauen oostwaert over de strate streckende
      oost wart toter steenstrate moelne tussche martins broukers lande en johan
      covents lande an de zuutside ende clais onghereiden lande an de noortzide
      belast dit vors lant met twalef scellinghe en achte peninghe siaers
      erveliker rente.

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	[('trans', FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)), ('vectorizer', CountVectorizer()), ('classifier', PassiveAggressiveClassifier(random_state=42))]
verbose	False
trans	FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)
vectorizer	CountVectorizer()
classifier	PassiveAggressiveClassifier(random_state=42)
trans__accept_sparse	False
trans__check_inverse	True
trans__feature_names_out
trans__func	<function revert_data at 0x7f3fb95883a0>
trans__inv_kw_args
trans__inverse_func
trans__kw_args
trans__validate	False
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__C	1.0
classifier__average	False
classifier__class_weight
classifier__early_stopping	False
classifier__fit_intercept	True
classifier__loss	hinge
classifier__max_iter	1000
classifier__n_iter_no_change	5
classifier__n_jobs
classifier__random_state	42
classifier__shuffle	True
classifier__tol	0.001
classifier__validation_fraction	0.1
classifier__verbose	0
classifier__warm_start	False

Model Plot

The model plot is below.

Pipeline(steps=[('trans',FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)),('vectorizer', CountVectorizer()),('classifier', PassiveAggressiveClassifier(random_state=42))])

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'	0.903724
f1 score including 'O	0.903724
precision excluding 'O'	0.803184
recall excluding 'O'	0.525071
f1 excluding 'O'	0.635011

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}}