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