sklearn skops text-classification license:mit region:us
---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
model_format: pickle
model_file: skops-ngrzbpwh.pkl
---

# Model description

This is a `Support Vector Classifier` model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens)

## Intended uses & limitations

This model is not ready to be used in production.

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter                                          | Value                                                                                                         |
|---------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|
| memory                                                  |                                                                                                               |
| steps                                                   | [('columntransformer', ColumnTransformer(transformers=[('num',<br />                                 Pipeline(steps=[('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', StandardScaler()),<br />                                                 ('pca',<br />                                                  PCA(n_components=689))]),<br />                                 Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br />       'avg_9', 'avg_10',<br />       ...<br />       'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br />       'max_765', 'max_766', 'max_767', 'max_768'],<br />      dtype='object', length=2304))],<br />                  verbose_feature_names_out=False)), ('randomforestclassifier', RandomForestClassifier(max_depth=15, max_features=20, min_samples_split=10,<br />                       random_state=42))]                                                                                                               |
| verbose                                                 | False                                                                                                         |
| columntransformer                                       | ColumnTransformer(transformers=[('num',<br />                                 Pipeline(steps=[('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', StandardScaler()),<br />                                                 ('pca',<br />                                                  PCA(n_components=689))]),<br />                                 Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br />       'avg_9', 'avg_10',<br />       ...<br />       'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br />       'max_765', 'max_766', 'max_767', 'max_768'],<br />      dtype='object', length=2304))],<br />                  verbose_feature_names_out=False)                                                                                                               |
| randomforestclassifier                                  | RandomForestClassifier(max_depth=15, max_features=20, min_samples_split=10,<br />                       random_state=42)                                                                                                               |
| columntransformer__n_jobs                               |                                                                                                               |
| columntransformer__remainder                            | drop                                                                                                          |
| columntransformer__sparse_threshold                     | 0.3                                                                                                           |
| columntransformer__transformer_weights                  |                                                                                                               |
| columntransformer__transformers                         | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br />                ('scaler', StandardScaler()), ('pca', PCA(n_components=689))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br />       'avg_9', 'avg_10',<br />       ...<br />       'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br />       'max_765', 'max_766', 'max_767', 'max_768'],<br />      dtype='object', length=2304))]                                                                                                               |
| columntransformer__verbose                              | False                                                                                                         |
| columntransformer__verbose_feature_names_out            | False                                                                                                         |
| columntransformer__num                                  | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br />                ('scaler', StandardScaler()), ('pca', PCA(n_components=689))])                                                                                                               |
| columntransformer__num__memory                          |                                                                                                               |
| columntransformer__num__steps                           | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=689))] |
| columntransformer__num__verbose                         | False                                                                                                         |
| columntransformer__num__imputer                         | SimpleImputer(strategy='median')                                                                              |
| columntransformer__num__scaler                          | StandardScaler()                                                                                              |
| columntransformer__num__pca                             | PCA(n_components=689)                                                                                         |
| columntransformer__num__imputer__add_indicator          | False                                                                                                         |
| columntransformer__num__imputer__copy                   | True                                                                                                          |
| columntransformer__num__imputer__fill_value             |                                                                                                               |
| columntransformer__num__imputer__keep_empty_features    | False                                                                                                         |
| columntransformer__num__imputer__missing_values         | nan                                                                                                           |
| columntransformer__num__imputer__strategy               | median                                                                                                        |
| columntransformer__num__imputer__verbose                | deprecated                                                                                                    |
| columntransformer__num__scaler__copy                    | True                                                                                                          |
| columntransformer__num__scaler__with_mean               | True                                                                                                          |
| columntransformer__num__scaler__with_std                | True                                                                                                          |
| columntransformer__num__pca__copy                       | True                                                                                                          |
| columntransformer__num__pca__iterated_power             | auto                                                                                                          |
| columntransformer__num__pca__n_components               | 689                                                                                                           |
| columntransformer__num__pca__n_oversamples              | 10                                                                                                            |
| columntransformer__num__pca__power_iteration_normalizer | auto                                                                                                          |
| columntransformer__num__pca__random_state               |                                                                                                               |
| columntransformer__num__pca__svd_solver                 | auto                                                                                                          |
| columntransformer__num__pca__tol                        | 0.0                                                                                                           |
| columntransformer__num__pca__whiten                     | False                                                                                                         |
| randomforestclassifier__bootstrap                       | True                                                                                                          |
| randomforestclassifier__ccp_alpha                       | 0.0                                                                                                           |
| randomforestclassifier__class_weight                    |                                                                                                               |
| randomforestclassifier__criterion                       | gini                                                                                                          |
| randomforestclassifier__max_depth                       | 15                                                                                                            |
| randomforestclassifier__max_features                    | 20                                                                                                            |
| randomforestclassifier__max_leaf_nodes                  |                                                                                                               |
| randomforestclassifier__max_samples                     |                                                                                                               |
| randomforestclassifier__min_impurity_decrease           | 0.0                                                                                                           |
| randomforestclassifier__min_samples_leaf                | 1                                                                                                             |
| randomforestclassifier__min_samples_split               | 10                                                                                                            |
| randomforestclassifier__min_weight_fraction_leaf        | 0.0                                                                                                           |
| randomforestclassifier__n_estimators                    | 100                                                                                                           |
| randomforestclassifier__n_jobs                          |                                                                                                               |
| randomforestclassifier__oob_score                       | False                                                                                                         |
| randomforestclassifier__random_state                    | 42                                                                                                            |
| randomforestclassifier__verbose                         | 0                                                                                                             |
| randomforestclassifier__warm_start                      | False                                                                                                         |

</details>

### Model Plot

<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler()),(&#x27;pca&#x27;,PCA(n_components=689))]),Index([&#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;, &#x27;avg_8&#x27;,&#x27;avg_9&#x27;, &#x27;avg_10&#x27;,...&#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;, &#x27;max_764&#x27;,&#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;, &#x27;max_768&#x27;],dtype=&#x27;object&#x27;, length=2304))],verbose_feature_names_out=False)),(&#x27;randomforestclassifier&#x27;,RandomForestClassifier(max_depth=15, max_features=20,min_samples_split=10,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-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler()),(&#x27;pca&#x27;,PCA(n_components=689))]),Index([&#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;, &#x27;avg_8&#x27;,&#x27;avg_9&#x27;, &#x27;avg_10&#x27;,...&#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;, &#x27;max_764&#x27;,&#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;, &#x27;max_768&#x27;],dtype=&#x27;object&#x27;, length=2304))],verbose_feature_names_out=False)),(&#x27;randomforestclassifier&#x27;,RandomForestClassifier(max_depth=15, max_features=20,min_samples_split=10,random_state=42))])</pre></div></div></div><div class="sk-serial"><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-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, StandardScaler()),(&#x27;pca&#x27;,PCA(n_components=689))]),Index([&#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;, &#x27;avg_8&#x27;,&#x27;avg_9&#x27;, &#x27;avg_10&#x27;,...&#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;, &#x27;max_764&#x27;,&#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;, &#x27;max_768&#x27;],dtype=&#x27;object&#x27;, length=2304))],verbose_feature_names_out=False)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index([&#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;, &#x27;avg_8&#x27;,&#x27;avg_9&#x27;, &#x27;avg_10&#x27;,...&#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;, &#x27;max_764&#x27;,&#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;, &#x27;max_768&#x27;],dtype=&#x27;object&#x27;, length=2304)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><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-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</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-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</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-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">PCA</label><div class="sk-toggleable__content"><pre>PCA(n_components=689)</pre></div></div></div></div></div></div></div></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-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier(max_depth=15, max_features=20, min_samples_split=10,random_state=42)</pre></div></div></div></div></div></div></div>

## Evaluation Results

| Metric   |    Value |
|----------|----------|
| accuracy | 0.964661 |
| f1 score | 0.964637 |

### Confusion Matrix

![Confusion Matrix](confusion_matrix.png)

# How to Get Started with the Model

[More Information Needed]

# Model Card Authors

huynhdoo

# Model Card Contact

You can contact the model card authors through following channels:
[More Information Needed]

# Citation

**BibTeX**

```
@inproceedings{...,year={2023}}
```

# get_started_code

import pickle as pickle
with open(pkl_filename, 'rb') as file:
pipe = pickle.load(file)
