---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- text-classification
model_format: pickle
model_file: multiskill-classifiert4_v38_0.pkl
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

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

| Hyperparameter               | Value                                                                                                   |
|------------------------------|---------------------------------------------------------------------------------------------------------|
| memory                       |                                                                                                         |
| steps                        | [('transformer', MultiSkillTransformer()), ('clf', SVC(C=1, class_weight='balanced', kernel='linear'))] |
| verbose                      | False                                                                                                   |
| transformer                  | MultiSkillTransformer()                                                                                 |
| clf                          | SVC(C=1, class_weight='balanced', kernel='linear')                                                      |
| clf__C                       | 1                                                                                                       |
| clf__break_ties              | False                                                                                                   |
| clf__cache_size              | 200                                                                                                     |
| clf__class_weight            | balanced                                                                                                |
| clf__coef0                   | 0.0                                                                                                     |
| clf__decision_function_shape | ovr                                                                                                     |
| clf__degree                  | 3                                                                                                       |
| clf__gamma                   | scale                                                                                                   |
| clf__kernel                  | linear                                                                                                  |
| clf__max_iter                | -1                                                                                                      |
| clf__probability             | False                                                                                                   |
| clf__random_state            |                                                                                                         |
| clf__shrinking               | True                                                                                                    |
| clf__tol                     | 0.001                                                                                                   |
| clf__verbose                 | False                                                                                                   |

</details>

### Model Plot

<style>#sk-container-id-1 {color: black;}#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=[(&#x27;transformer&#x27;, MultiSkillTransformer()),(&#x27;clf&#x27;, SVC(C=1, class_weight=&#x27;balanced&#x27;, kernel=&#x27;linear&#x27;))])</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=[(&#x27;transformer&#x27;, MultiSkillTransformer()),(&#x27;clf&#x27;, SVC(C=1, class_weight=&#x27;balanced&#x27;, kernel=&#x27;linear&#x27;))])</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">MultiSkillTransformer</label><div class="sk-toggleable__content"><pre>MultiSkillTransformer()</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">SVC</label><div class="sk-toggleable__content"><pre>SVC(C=1, class_weight=&#x27;balanced&#x27;, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div></div></div>

## Evaluation Results

[More Information Needed]

# How to Get Started with the Model

[More Information Needed]

# Model Card Authors

This model card is written by following authors:

[More Information Needed]

# Model Card Contact

You can contact the model card authors through following channels:
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# Citation

Below you can find information related to citation.

**BibTeX:**
```
[More Information Needed]
```

# model_description

Support Vector Machine (SVM) trained to predict if a skill span is a multiskill or not.

# Classification Report

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

| index        |   precision |   recall |   f1-score |    support |
|--------------|-------------|----------|------------|------------|
| SKILL        |    0.93617  | 0.807339 |   0.866995 | 109        |
| MULTISKILL   |    0.834646 | 0.946429 |   0.887029 | 112        |
| accuracy     |    0.877828 | 0.877828 |   0.877828 |   0.877828 |
| macro avg    |    0.885408 | 0.876884 |   0.877012 | 221        |
| weighted avg |    0.884719 | 0.877828 |   0.877148 | 221        |

</details>