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multiskill-classifier
nestauk/multiskill-classifier
multiskill-classifier is a text classification model from nestauk. Use it when you need a label for a piece of text. It is set up for sklearn. The card lists the license as mit.
Classifier to predict whether a skill entity (as predicted by our skillNER model) is likely to contain mention of multiple skills or not.
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Public
Updated Jun 3, 2025
Repo size
28.5 KB
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1
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Repo makeup
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.pkl28.5 KB · 69%
At a glance
- Task
- Text Classification
- Library
- sklearn
- License
- mit
- Access
- Public
- Created
- May 3, 2024
- Updated
- Jun 3, 2025
- SHA
- 19eea6bf
Try a prompt
Model card
From the Hugging Face model README
Model description
Classifier to predict whether a skill entity (as predicted by our skillNER model) is likely to contain mention of multiple skills or not.
Future developments and maintainence of this model by Nesta have been stopped as of May 2025.
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 |
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=[('transformer', MultiSkillTransformer()),('clf', SVC(C=1, class_weight='balanced', kernel='linear'))])</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=[('transformer', MultiSkillTransformer()),('clf', SVC(C=1, class_weight='balanced', kernel='linear'))])</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='balanced', kernel='linear')</pre></div></div></div></div></div></div></div>Evaluation Results
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Model Card Authors
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Citation
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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 |
- Task
- Text Classification
- Library
- sklearn
- License
- mit
- Created
- May 3, 2024
- Updated
- Jun 3, 2025