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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: model.pkl |
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--- |
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# Model description |
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[More Information Needed] |
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## Intended uses & limitations |
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[More Information Needed] |
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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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| memory | | |
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| steps | [('tfidf', TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7fa438e7a280>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...))] | |
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| verbose | True | |
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| tfidf | TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7fa438e7a280>) | |
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| classifier | XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...) | |
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| tfidf__analyzer | word | |
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| tfidf__binary | False | |
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| tfidf__decode_error | strict | |
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| tfidf__dtype | <class 'numpy.float64'> | |
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| tfidf__encoding | utf-8 | |
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| tfidf__input | content | |
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| tfidf__lowercase | True | |
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| tfidf__max_df | 1.0 | |
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| tfidf__max_features | | |
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| tfidf__min_df | 100 | |
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| tfidf__ngram_range | (1, 3) | |
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| tfidf__norm | l2 | |
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| tfidf__preprocessor | <function preprocessor at 0x7fa438e7a280> | |
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| tfidf__smooth_idf | True | |
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| tfidf__stop_words | | |
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| tfidf__strip_accents | | |
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| tfidf__sublinear_tf | False | |
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| tfidf__token_pattern | (?u)\b\w\w+\b | |
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| tfidf__tokenizer | | |
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| tfidf__use_idf | True | |
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| tfidf__vocabulary | | |
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| classifier__objective | binary:logistic | |
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| classifier__base_score | | |
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| classifier__booster | | |
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| classifier__callbacks | | |
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| classifier__colsample_bylevel | | |
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| classifier__colsample_bynode | | |
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| classifier__colsample_bytree | | |
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| classifier__device | | |
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| classifier__early_stopping_rounds | | |
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| classifier__enable_categorical | False | |
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| classifier__eval_metric | | |
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| classifier__feature_types | | |
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| classifier__gamma | | |
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| classifier__grow_policy | | |
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| classifier__importance_type | | |
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| classifier__interaction_constraints | | |
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| classifier__learning_rate | | |
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| classifier__max_bin | | |
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| classifier__max_cat_threshold | | |
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| classifier__max_cat_to_onehot | | |
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| classifier__max_delta_step | | |
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| classifier__max_depth | | |
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| classifier__max_leaves | | |
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| classifier__min_child_weight | | |
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| classifier__missing | nan | |
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| classifier__monotone_constraints | | |
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| classifier__multi_strategy | | |
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| classifier__n_estimators | | |
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| classifier__n_jobs | | |
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| classifier__num_parallel_tree | | |
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| classifier__random_state | | |
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| classifier__reg_alpha | | |
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| classifier__reg_lambda | | |
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| classifier__sampling_method | | |
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| classifier__scale_pos_weight | | |
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| classifier__subsample | | |
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| classifier__tree_method | | |
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| classifier__validate_parameters | | |
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| classifier__verbosity | | |
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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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a:link.sk-estimator-doc-link.fitted, |
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a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
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}/* On hover */ |
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div.sk-estimator:hover .sk-estimator-doc-link:hover, |
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.sk-estimator-doc-link:hover, |
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div.sk-label-container:hover .sk-estimator-doc-link:hover, |
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.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
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}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, |
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.sk-estimator-doc-link.fitted:hover, |
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div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, |
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.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
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}/* Span, style for the box shown on hovering the info icon */ |
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.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); |
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); |
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}.sk-estimator-doc-link:hover span {display: block; |
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}/* "?"-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; |
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}#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); |
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}/* On hover */ |
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#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; |
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}#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
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} |
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</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7fa438e7a280>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</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/1.4/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=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7fa438e7a280>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</pre></div> </div></div><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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> TfidfVectorizer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></label><div class="sk-toggleable__content fitted"><pre>TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7fa438e7a280>)</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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">XGBClassifier</label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div> |
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## Evaluation Results |
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| Metric | Value | |
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|----------|----------| |
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| accuracy | 0.910317 | |
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| f1 score | 0.910317 | |
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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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This model card is written by following authors: |
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[More Information Needed] |
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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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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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[More Information Needed] |
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``` |
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# get_started_code |
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import sklearn |
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import dill as pickle |
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from skops import hub_utils |
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from pathlib import Path |
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suicide_detector_repo = Path("./suicide-detector") |
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hub_utils.download( |
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repo_id="AndyJamesTurner/suicideDetector", |
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dst=suicide_detector_repo |
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) |
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with open(suicide_detector_repo / "model.pkl", 'rb') as file: |
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clf = pickle.load(file) |
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classification = clf.predict(["I want to kill myself"])[0] |
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# model_card_authors |
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Andy Turner |
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# model_description |
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Suicide Detection text classification model. |
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Trained on the Suicide and Depression Detection dataset (https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch) |
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The model vectorises each text using a trained tfidf vectorizer and then classifies using xgboost. |
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# eval_method |
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The model was evaluated on a 0.3 holdout split using f1 score, accuracy, confusion matrix and ROC curves. |
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# Confusion matrix |
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# ROC Curve |
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# Classification Report |
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<details> |
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<summary> Click to expand </summary> |
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| index | precision | recall | f1-score | support | |
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|--------------|-------------|----------|------------|-----------| |
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| not suicide | 0.891721 | 0.934126 | 0.912431 | 34824 | |
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| suicide | 0.930785 | 0.886491 | 0.908098 | 34799 | |
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| macro avg | 0.911253 | 0.910308 | 0.910265 | 69623 | |
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| weighted avg | 0.911246 | 0.910317 | 0.910265 | 69623 | |
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</details> |
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