--- library_name: sklearn tags: - sklearn - skops - text-classification model_file: middle_dutch_text-classification.pkl --- # 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 0x7fa10f2053a0>)),('vectorizer', CountVectorizer()),('classifier', PassiveAggressiveClassifier(random_state=42))])

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## 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}} ```