pushing model SVC with camember base embeddings

README.md

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+---
+library_name: sklearn
+license: mit
+tags:
+- sklearn
+- skops
+- text-classification
+model_format: pickle
+model_file: skops-0mf0eolw.pkl
+---
+
+# Model description
+
+This is a `Support Vector Classifier` model trained on SIRIUS 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=486))]),<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)), ('svc', SVC(probability=True, 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=486))]),<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) |
+| svc | SVC(probability=True, random_state=42) |
+| columntransformer__force_int_remainder_cols | True |
+| 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=486))]), 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=486))]) |
+| columntransformer__num__memory | |
+| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=486))] |
+| columntransformer__num__verbose | False |
+| columntransformer__num__imputer | SimpleImputer(strategy='median') |
+| columntransformer__num__scaler | StandardScaler() |
+| columntransformer__num__pca | PCA(n_components=486) |
+| 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__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 | 486 |
+| 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 |
+| svc__C | 1.0 |
+| svc__break_ties | False |
+| svc__cache_size | 200 |
+| svc__class_weight | |
+| svc__coef0 | 0.0 |
+| svc__decision_function_shape | ovr |
+| svc__degree | 3 |
+| svc__gamma | scale |
+| svc__kernel | rbf |
+| svc__max_iter | -1 |
+| svc__probability | True |
+| svc__random_state | 42 |
+| svc__shrinking | True |
+| svc__tol | 0.001 |
+| svc__verbose | False |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-3 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
+}#sk-container-id-3 {color: var(--sklearn-color-text);
+}#sk-container-id-3 pre {padding: 0;
+}#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-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
+}#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 thedefault 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;
+}div.sk-parallel-item,
+div.sk-serial,
+div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
+}/* Parallel-specific style estimator block */#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
+}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;
+}#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;
+}/* Serial-specific style estimator block */#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
+}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+clickable and can be expanded/collapsed.
+- Pipeline and ColumnTransformer use this feature and define the default style
+- Estimators will overwrite some part of the style using the `sk-estimator` class
+*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-3 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
+}/* Toggleable label */
+#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
+}#sk-container-id-3 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
+}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
+}/* Toggleable content - dropdown */#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
+}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
+}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
+}/* Estimator-specific style *//* Colorize estimator box */
+#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}#sk-container-id-3 div.sk-label label.sk-toggleable__label,
+#sk-container-id-3 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
+}/* On hover, darken the color of the background */
+#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+}/* Label box, darken color on hover, fitted */
+#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
+}/* Estimator label */#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;
+}/* Estimator-specific */
+#sk-container-id-3 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}/* on hover */
+#sk-container-id-3 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
+a:link.sk-estimator-doc-link,
+a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
+}.sk-estimator-doc-link.fitted,
+a:link.sk-estimator-doc-link.fitted,
+a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* On hover */
+div.sk-estimator:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover,
+div.sk-label-container:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover,
+div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}/* Span, style for the box shown on hovering the info icon */
+.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
+}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
+}.sk-estimator-doc-link:hover span {display: block;
+}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-3 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}#sk-container-id-3 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* On hover */
+#sk-container-id-3 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}#sk-container-id-3 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+}
+</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=486))]),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;svc&#x27;, SVC(probability=True, 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 fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><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=486))]),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;svc&#x27;, SVC(probability=True, 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 fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;columntransformer: ColumnTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></label><div class="sk-toggleable__content fitted"><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=486))]),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 fitted 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 fitted sk-toggleable__label-arrow fitted">num</label><div class="sk-toggleable__content fitted"><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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;PCA<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></label><div class="sk-toggleable__content fitted"><pre>PCA(n_components=486)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SVC<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></label><div class="sk-toggleable__content fitted"><pre>SVC(probability=True, random_state=42)</pre></div> </div></div></div></div></div></div>
+
+## Evaluation Results
+
+| Metric | Value |
+|----------|---------|
+| accuracy | 1 |
+| f1 score | 1 |
+
+### 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={2024}}
+```
+
+# get_started_code
+
+import pickle as pickle
+with open(pkl_filename, 'rb') as file:
+pipe = pickle.load(file)

config.json

@@ -0,0 +1,18 @@
+{
+ "sklearn": {
+ "environment": [
+ "scikit-learn=1.5.1"
+ ],
+ "example_input": {
+ "data": [
+ "",
+ ""
+ ]
+ },
+ "model": {
+ "file": "skops-0mf0eolw.pkl"
+ },
+ "model_format": "pickle",
+ "task": "text-classification"
+ }
+}

skops-0mf0eolw.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c10e2d61042c73b3430ecad489bf0b53d4871b6496b4639dd288eb17f6b0c58e
+size 12368852