pushing model RandomForestClassifier with camember base embeddings
Browse files- README.md +114 -0
- config.json +19 -0
- confusion_matrix.png +0 -0
- skops-4dusypwz.pkl +3 -0
README.md
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---
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license: mit
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library_name: sklearn
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tags:
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- sklearn
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- skops
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- text-classification
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model_format: pickle
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model_file: skops-4dusypwz.pkl
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---
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# Model description
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This is a `RandomForestClassifier` model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens)
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## Intended uses & limitations
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This model is not ready to be used in production.
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## Training Procedure
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### Hyperparameters
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The model is trained with below hyperparameters.
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<details>
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<summary> Click to expand </summary>
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| Hyperparameter | Value |
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|---------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|
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| memory | |
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| steps | [('scaler', StandardScaler()), ('pca', PCA(n_components=374)), ('rfc', RandomForestClassifier(class_weight='balanced', random_state=42))] |
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| verbose | False |
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| scaler | StandardScaler() |
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| pca | PCA(n_components=374) |
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| rfc | RandomForestClassifier(class_weight='balanced', random_state=42) |
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| scaler__copy | True |
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| scaler__with_mean | True |
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| scaler__with_std | True |
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| pca__copy | True |
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| pca__iterated_power | auto |
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| pca__n_components | 374 |
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| pca__n_oversamples | 10 |
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| pca__power_iteration_normalizer | auto |
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| pca__random_state | |
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| pca__svd_solver | auto |
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| pca__tol | 0.0 |
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| pca__whiten | False |
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| rfc__bootstrap | True |
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| rfc__ccp_alpha | 0.0 |
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| rfc__class_weight | balanced |
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| rfc__criterion | gini |
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| rfc__max_depth | |
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| rfc__max_features | sqrt |
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| rfc__max_leaf_nodes | |
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| rfc__max_samples | |
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| rfc__min_impurity_decrease | 0.0 |
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| rfc__min_samples_leaf | 1 |
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| rfc__min_samples_split | 2 |
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| rfc__min_weight_fraction_leaf | 0.0 |
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| rfc__n_estimators | 100 |
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| rfc__n_jobs | |
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| rfc__oob_score | False |
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| rfc__random_state | 42 |
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| rfc__verbose | 0 |
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| rfc__warm_start | False |
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</details>
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### Model Plot
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The model plot is below.
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<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=[('scaler', StandardScaler()), ('pca', PCA(n_components=374)),('rfc',RandomForestClassifier(class_weight='balanced',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=[('scaler', StandardScaler()), ('pca', PCA(n_components=374)),('rfc',RandomForestClassifier(class_weight='balanced',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='balanced', random_state=42)</pre></div></div></div></div></div></div></div>
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## Evaluation Results
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You can find the details about evaluation process and the evaluation results.
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| Metric | Value |
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|----------|----------|
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| accuracy | 0.962669 |
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| f1 score | 0.945696 |
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### Confusion Matrix
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# How to Get Started with the Model
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[More Information Needed]
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# Model Card Authors
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huynhdoo
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# Model Card Contact
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You can contact the model card authors through following channels:
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[More Information Needed]
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# Citation
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**BibTeX**
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```
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@inproceedings{...,year={2023}}
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```
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# get_started_code
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import pickle as pickle
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with open(pkl_filename, 'rb') as file:
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pipe = pickle.load(file)
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config.json
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{
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"sklearn": {
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"environment": [
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"scikit-learn=1.2.2"
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],
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"example_input": {
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"data": [
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"",
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""
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]
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},
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"model": {
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"file": "skops-4dusypwz.pkl"
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},
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"model_format": "pickle",
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"task": "text-classification",
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"use_intelex": false
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}
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}
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confusion_matrix.png
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skops-4dusypwz.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:248df8a44469d4ec20a6bd4fd61560b2864e52e4fbf3192808dae871f45ca272
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size 12861966
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