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.
<details> <summary> Click to expand </summary>
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 |
</details>
Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's normalize.less
sets [hidden] { display: none; }
but bootstrap.min.css set [hidden] { display: none !important; }
so we also need the !important
here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('trans',FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)),('vectorizer', CountVectorizer()),('classifier', PassiveAggressiveClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('trans',FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)),('vectorizer', CountVectorizer()),('classifier', PassiveAggressiveClassifier(random_state=42))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function revert_data at 0x7f3fb95883a0>)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">PassiveAggressiveClassifier</label><div class="sk-toggleable__content"><pre>PassiveAggressiveClassifier(random_state=42)</pre></div></div></div></div></div></div></div>
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}}