pushing model SVC with camember base embeddings
Browse files- README.md +203 -0
- config.json +18 -0
- confusion_matrix.png +0 -0
- skops-ik6yuleb.pkl +3 -0
README.md
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---
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library_name: sklearn
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license: mit
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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-ik6yuleb.pkl
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---
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# Model description
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This is a `Support Vector Classifier` model trained on SIRIUS 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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[More Information Needed]
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### 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 | [('columntransformer', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=560))]),<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))] |
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| verbose | False |
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| columntransformer | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=560))]),<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) |
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| svc | SVC(probability=True, random_state=42) |
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| columntransformer__force_int_remainder_cols | True |
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| columntransformer__n_jobs | |
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| columntransformer__remainder | drop |
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| columntransformer__sparse_threshold | 0.3 |
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| columntransformer__transformer_weights | |
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| columntransformer__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=560))]), 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))] |
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| columntransformer__verbose | False |
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| columntransformer__verbose_feature_names_out | False |
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| columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=560))]) |
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| columntransformer__num__memory | |
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| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=560))] |
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| columntransformer__num__verbose | False |
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| columntransformer__num__imputer | SimpleImputer(strategy='median') |
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| columntransformer__num__scaler | StandardScaler() |
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| columntransformer__num__pca | PCA(n_components=560) |
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| columntransformer__num__imputer__add_indicator | False |
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| columntransformer__num__imputer__copy | True |
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| columntransformer__num__imputer__fill_value | |
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| columntransformer__num__imputer__keep_empty_features | False |
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| columntransformer__num__imputer__missing_values | nan |
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| columntransformer__num__imputer__strategy | median |
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| columntransformer__num__scaler__copy | True |
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| columntransformer__num__scaler__with_mean | True |
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| columntransformer__num__scaler__with_std | True |
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| columntransformer__num__pca__copy | True |
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| columntransformer__num__pca__iterated_power | auto |
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| columntransformer__num__pca__n_components | 560 |
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| columntransformer__num__pca__n_oversamples | 10 |
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| columntransformer__num__pca__power_iteration_normalizer | auto |
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| columntransformer__num__pca__random_state | |
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| columntransformer__num__pca__svd_solver | auto |
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| columntransformer__num__pca__tol | 0.0 |
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| columntransformer__num__pca__whiten | False |
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| svc__C | 1.0 |
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| svc__break_ties | False |
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| svc__cache_size | 200 |
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| svc__class_weight | |
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| svc__coef0 | 0.0 |
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| svc__decision_function_shape | ovr |
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| svc__degree | 3 |
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| svc__gamma | scale |
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| svc__kernel | rbf |
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| svc__max_iter | -1 |
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| svc__probability | True |
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| svc__random_state | 42 |
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| svc__shrinking | True |
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| svc__tol | 0.001 |
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| svc__verbose | False |
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</details>
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### Model Plot
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<style>#sk-container-id-1 {/* 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;}
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}#sk-container-id-1 {color: var(--sklearn-color-text);
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}#sk-container-id-1 pre {padding: 0;
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}#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;
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}#sk-container-id-1 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);
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}#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 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;
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}#sk-container-id-1 div.sk-text-repr-fallback {display: none;
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}div.sk-parallel-item,
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div.sk-serial,
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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;
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}/* Parallel-specific style estimator block */#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
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}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
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}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;
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}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
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}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;
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}/* Serial-specific style estimator block */#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-1 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);
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}/* Toggleable label */
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#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
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}#sk-container-id-1 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);
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}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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}/* Toggleable content - dropdown */#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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}#sk-container-id-1 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-1 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);
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}#sk-container-id-1 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
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}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-1 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);
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}#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
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}/* Estimator-specific style *//* Colorize estimator box */
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#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}#sk-container-id-1 div.sk-label label.sk-toggleable__label,
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#sk-container-id-1 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
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}/* On hover, darken the color of the background */
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#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
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}/* Label box, darken color on hover, fitted */
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#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
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}/* Estimator label */#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
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}#sk-container-id-1 div.sk-label-container {text-align: center;
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}/* Estimator-specific */
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#sk-container-id-1 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);
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}#sk-container-id-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}/* on hover */
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#sk-container-id-1 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-1 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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a:link.sk-estimator-doc-link,
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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);
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}.sk-estimator-doc-link.fitted,
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| 144 |
+
a:link.sk-estimator-doc-link.fitted,
|
| 145 |
+
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 146 |
+
}/* On hover */
|
| 147 |
+
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 148 |
+
.sk-estimator-doc-link:hover,
|
| 149 |
+
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 150 |
+
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 151 |
+
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 152 |
+
.sk-estimator-doc-link.fitted:hover,
|
| 153 |
+
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 154 |
+
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 155 |
+
}/* Span, style for the box shown on hovering the info icon */
|
| 156 |
+
.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);
|
| 157 |
+
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
|
| 158 |
+
}.sk-estimator-doc-link:hover span {display: block;
|
| 159 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-1 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;
|
| 160 |
+
}#sk-container-id-1 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 161 |
+
}/* On hover */
|
| 162 |
+
#sk-container-id-1 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 163 |
+
}#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 164 |
+
}
|
| 165 |
+
</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=560))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', 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-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=560))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', 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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler()),('pca',PCA(n_components=560))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', 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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">num</label><div class="sk-toggleable__content fitted"><pre>Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', 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-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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='median')</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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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=560)</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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> 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>
|
| 166 |
+
|
| 167 |
+
## Evaluation Results
|
| 168 |
+
|
| 169 |
+
| Metric | Value |
|
| 170 |
+
|----------|----------|
|
| 171 |
+
| accuracy | 0.972222 |
|
| 172 |
+
| f1 score | 0.972214 |
|
| 173 |
+
|
| 174 |
+
### Confusion Matrix
|
| 175 |
+
|
| 176 |
+
|
| 177 |
+
|
| 178 |
+
# How to Get Started with the Model
|
| 179 |
+
|
| 180 |
+
[More Information Needed]
|
| 181 |
+
|
| 182 |
+
# Model Card Authors
|
| 183 |
+
|
| 184 |
+
huynhdoo
|
| 185 |
+
|
| 186 |
+
# Model Card Contact
|
| 187 |
+
|
| 188 |
+
You can contact the model card authors through following channels:
|
| 189 |
+
[More Information Needed]
|
| 190 |
+
|
| 191 |
+
# Citation
|
| 192 |
+
|
| 193 |
+
**BibTeX**
|
| 194 |
+
|
| 195 |
+
```
|
| 196 |
+
@inproceedings{...,year={2024}}
|
| 197 |
+
```
|
| 198 |
+
|
| 199 |
+
# get_started_code
|
| 200 |
+
|
| 201 |
+
import pickle as pickle
|
| 202 |
+
with open(pkl_filename, 'rb') as file:
|
| 203 |
+
pipe = pickle.load(file)
|
config.json
ADDED
|
@@ -0,0 +1,18 @@
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|
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|
|
|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"environment": [
|
| 4 |
+
"scikit-learn=1.5.1"
|
| 5 |
+
],
|
| 6 |
+
"example_input": {
|
| 7 |
+
"data": [
|
| 8 |
+
"",
|
| 9 |
+
""
|
| 10 |
+
]
|
| 11 |
+
},
|
| 12 |
+
"model": {
|
| 13 |
+
"file": "skops-ik6yuleb.pkl"
|
| 14 |
+
},
|
| 15 |
+
"model_format": "pickle",
|
| 16 |
+
"task": "text-classification"
|
| 17 |
+
}
|
| 18 |
+
}
|
confusion_matrix.png
ADDED
|
skops-ik6yuleb.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7ab2306a010fbb2eab1fe034266bded2cf9c89c3f1a0a21fcc70f2fa70860903
|
| 3 |
+
size 20087916
|