--- 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.

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| Hyperparameter | Value | |---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('trans', FunctionTransformer(func=)), ('vectorizer', CountVectorizer()), ('classifier', PassiveAggressiveClassifier(random_state=42))] | | verbose | False | | trans | FunctionTransformer(func=) | | vectorizer | CountVectorizer() | | classifier | PassiveAggressiveClassifier(random_state=42) | | trans__accept_sparse | False | | trans__check_inverse | True | | trans__feature_names_out | | | trans__func | | | 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 | | | 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 ![Confusion Matrix](confusion_matrix.png) # 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}} ```