
# Model description

This is a `RandomForestClassifier` 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

### Hyperparameters

The model is trained with below hyperparameters.

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

| Hyperparameter                  | Value                                                                                                                                     |
|---------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|
| memory                          |                                                                                                                                           |
| steps                           | [('scaler', StandardScaler()), ('pca', PCA(n_components=374)), ('rfc', RandomForestClassifier(class_weight='balanced', random_state=42))] |
| verbose                         | False                                                                                                                                     |
| scaler                          | StandardScaler()                                                                                                                          |
| pca                             | PCA(n_components=374)                                                                                                                     |
| rfc                             | RandomForestClassifier(class_weight='balanced', random_state=42)                                                                          |
| scaler__copy                    | True                                                                                                                                      |
| scaler__with_mean               | True                                                                                                                                      |
| scaler__with_std                | True                                                                                                                                      |
| pca__copy                       | True                                                                                                                                      |
| pca__iterated_power             | auto                                                                                                                                      |
| pca__n_components               | 374                                                                                                                                       |
| pca__n_oversamples              | 10                                                                                                                                        |
| pca__power_iteration_normalizer | auto                                                                                                                                      |
| pca__random_state               |                                                                                                                                           |
| pca__svd_solver                 | auto                                                                                                                                      |
| pca__tol                        | 0.0                                                                                                                                       |
| pca__whiten                     | False                                                                                                                                     |
| rfc__bootstrap                  | True                                                                                                                                      |
| rfc__ccp_alpha                  | 0.0                                                                                                                                       |
| rfc__class_weight               | balanced                                                                                                                                  |
| rfc__criterion                  | gini                                                                                                                                      |
| rfc__max_depth                  |                                                                                                                                           |
| rfc__max_features               | sqrt                                                                                                                                      |
| rfc__max_leaf_nodes             |                                                                                                                                           |
| rfc__max_samples                |                                                                                                                                           |
| rfc__min_impurity_decrease      | 0.0                                                                                                                                       |
| rfc__min_samples_leaf           | 1                                                                                                                                         |
| rfc__min_samples_split          | 2                                                                                                                                         |
| rfc__min_weight_fraction_leaf   | 0.0                                                                                                                                       |
| rfc__n_estimators               | 100                                                                                                                                       |
| rfc__n_jobs                     |                                                                                                                                           |
| rfc__oob_score                  | False                                                                                                                                     |
| rfc__random_state               | 42                                                                                                                                        |
| rfc__verbose                    | 0                                                                                                                                         |
| rfc__warm_start                 | False                                                                                                                                     |

</details>

### Model Plot

The model plot is below.

<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=374)),(&#x27;rfc&#x27;,RandomForestClassifier(class_weight=&#x27;balanced&#x27;,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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=374)),(&#x27;rfc&#x27;,RandomForestClassifier(class_weight=&#x27;balanced&#x27;,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-6" type="checkbox" ><label for="sk-estimator-id-6" 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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">PCA</label><div class="sk-toggleable__content"><pre>PCA(n_components=374)</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-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, 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 | 0.962669 |
| f1 score | 0.945696 |

### 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)

{"license": "mit", "library_name": "sklearn", "tags": ["sklearn", "skops", "text-classification"], "model_format": "pickle", "model_file": "skops-4dusypwz.pkl"}
sklearn skops text-classification license:mit region:us
TAGS
#sklearn #skops #text-classification #license-mit #region-us 

Model description
=================


This is a 'RandomForestClassifier' 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
------------------


### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


### Model Plot


The model plot is below.


#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 URL-toggleable {background-color: white;}#sk-container-id-2 URL-toggleable\_\_label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-container-id-2 URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-container-id-2 URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-container-id-2 URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-2 URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-2 URL-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-2 URL-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-2 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-2 URL-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-2 URL-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-2 URL-item {position: relative;z-index: 1;}#sk-container-id-2 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 URL-item::before, #sk-container-id-2 URL-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-2 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-2 URL-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-2 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 URL-label-container {text-align: center;}#sk-container-id-2 URL-container {/\* jupyter's 'URL' sets '[hidden] { display: none; }' but URL 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 URL. See: URL \*/display: inline-block !important;position: relative;}#sk-container-id-2 URL-text-repr-fallback {display: none;}
```
Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=374)),('rfc',RandomForestClassifier(class_weight='balanced',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 URL.**Pipeline
```
Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=374)),('rfc',RandomForestClassifier(class_weight='balanced',random_state=42))])
```
StandardScaler
```
StandardScaler()
```
PCA
```
PCA(n_components=374)
```
RandomForestClassifier
```
RandomForestClassifier(class_weight='balanced', random_state=42)
```

Evaluation Results
------------------


You can find the details about evaluation process and the evaluation results.



### Confusion Matrix


!Confusion Matrix


How to Get Started with the Model
=================================


Model Card Authors
==================


huynhdoo


Model Card Contact
==================


You can contact the model card authors through following channels:


BibTeX


get\_started\_code
==================


import pickle as pickle
with open(pkl\_filename, 'rb') as file:
pipe = URL(file)



### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. ### Confusion Matrix


!Confusion Matrix


How to Get Started with the Model
=================================


Model Card Authors
==================


huynhdoo


Model Card Contact
==================


You can contact the model card authors through following channels:


BibTeX


get\_started\_code
==================


import pickle as pickle
with open(pkl\_filename, 'rb') as file:
pipe = URL(file)
TAGS
#sklearn #skops #text-classification #license-mit #region-us 
 ### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. ### Confusion Matrix


!Confusion Matrix


How to Get Started with the Model
=================================


Model Card Authors
==================


huynhdoo


Model Card Contact
==================


You can contact the model card authors through following channels:


BibTeX


get\_started\_code
==================


import pickle as pickle
with open(pkl\_filename, 'rb') as file:
pipe = URL(file)
TAGS
#sklearn #skops #text-classification #license-mit #region-us 
### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand### Model Plot


The model plot is below.### Confusion Matrix


!Confusion Matrix


How to Get Started with the Model
=================================


Model Card Authors
==================


huynhdoo


Model Card Contact
==================


You can contact the model card authors through following channels:


BibTeX


get\_started\_code
==================


import pickle as pickle
with open(pkl\_filename, 'rb') as file:
pipe = URL(file)