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|
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|
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|
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|
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LargeFile
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Summarising scientific publications from a tech-transfer perspective
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|
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|
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Fine-tune SciBERT
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|
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|
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|
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|
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|
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Create an inference function
|
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|
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|
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<a href="../../examples/scibert/additional-files/" class="md-nav__link">
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<a href="#lets-continue-by-finishing-the-deployment-in-the-next-notebook" class="md-nav__link">
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Let's continue by finishing the deployment in the next notebook.
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<article class="md-content__inner md-typeset">
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<a href="train.ipynb" title="Download Notebook" class="md-content__button md-icon">
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(function (global, factory) {
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typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
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|
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|
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|
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|
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|
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|
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// $FlowFixMe Clipboard is not defined in Flow yet.
|
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function blurred(event) {
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class ClipboardCopyElement extends HTMLElement {
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constructor() {
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super();
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|
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|
||
.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
|
||
.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
|
||
.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
|
||
.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
|
||
.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
|
||
.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
|
||
.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
|
||
.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
|
||
.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
|
||
.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
|
||
.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
|
||
.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
|
||
.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
|
||
.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
|
||
.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
|
||
.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
|
||
.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
|
||
.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
|
||
</style>
|
||
|
||
|
||
|
||
|
||
<style type="text/css">
|
||
/*
|
||
This file is taken from the built JupyterLab theme.css
|
||
Found on share/nbconvert/templates/lab/static
|
||
Some changes have been made and marked with CHANGE
|
||
*/
|
||
|
||
.jupyter-wrapper {
|
||
/* Elevation
|
||
*
|
||
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
|
||
*
|
||
* https://github.com/material-components/material-components-web
|
||
* https://material-components-web.appspot.com/elevation.html
|
||
*/
|
||
|
||
--jp-shadow-base-lightness: 0;
|
||
--jp-shadow-umbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.2
|
||
);
|
||
--jp-shadow-penumbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.14
|
||
);
|
||
--jp-shadow-ambient-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.12
|
||
);
|
||
--jp-elevation-z0: none;
|
||
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
|
||
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 3px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
|
||
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 5px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
|
||
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 10px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
|
||
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 18px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
|
||
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
|
||
0px 3px 14px 2px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
|
||
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
|
||
0px 5px 22px 4px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
|
||
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
|
||
0px 6px 30px 5px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
|
||
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
|
||
0px 8px 38px 7px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
|
||
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
|
||
0px 9px 46px 8px var(--jp-shadow-ambient-color);
|
||
|
||
/* Borders
|
||
*
|
||
* The following variables, specify the visual styling of borders in JupyterLab.
|
||
*/
|
||
|
||
--jp-border-width: 1px;
|
||
--jp-border-color0: var(--md-grey-400);
|
||
--jp-border-color1: var(--md-grey-400);
|
||
--jp-border-color2: var(--md-grey-300);
|
||
--jp-border-color3: var(--md-grey-200);
|
||
--jp-border-radius: 2px;
|
||
|
||
/* UI Fonts
|
||
*
|
||
* The UI font CSS variables are used for the typography all of the JupyterLab
|
||
* user interface elements that are not directly user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-ui-font-scale-factor: 1.2;
|
||
--jp-ui-font-size0: 0.83333em;
|
||
--jp-ui-font-size1: 13px; /* Base font size */
|
||
--jp-ui-font-size2: 1.2em;
|
||
--jp-ui-font-size3: 1.44em;
|
||
|
||
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Use these font colors against the corresponding main layout colors.
|
||
* In a light theme, these go from dark to light.
|
||
*/
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-ui-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
|
||
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
|
||
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
|
||
|
||
/*
|
||
* Use these against the brand/accent/warn/error colors.
|
||
* These will typically go from light to darker, in both a dark and light theme.
|
||
*/
|
||
|
||
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
|
||
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
|
||
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
|
||
|
||
/* Content Fonts
|
||
*
|
||
* Content font variables are used for typography of user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-content-line-height: 1.6;
|
||
--jp-content-font-scale-factor: 1.2;
|
||
--jp-content-font-size0: 0.83333em;
|
||
--jp-content-font-size1: 14px; /* Base font size */
|
||
--jp-content-font-size2: 1.2em;
|
||
--jp-content-font-size3: 1.44em;
|
||
--jp-content-font-size4: 1.728em;
|
||
--jp-content-font-size5: 2.0736em;
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-content-presentation-font-size1: 17px;
|
||
|
||
--jp-content-heading-line-height: 1;
|
||
--jp-content-heading-margin-top: 1.2em;
|
||
--jp-content-heading-margin-bottom: 0.8em;
|
||
--jp-content-heading-font-weight: 500;
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-content-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
|
||
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
|
||
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
|
||
|
||
--jp-content-link-color: var(--md-blue-700);
|
||
|
||
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Code Fonts
|
||
*
|
||
* Code font variables are used for typography of code and other monospaces content.
|
||
*/
|
||
|
||
--jp-code-font-size: 13px;
|
||
--jp-code-line-height: 1.3077; /* 17px for 13px base */
|
||
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
|
||
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
|
||
monospace;
|
||
--jp-code-font-family: var(--jp-code-font-family-default);
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-code-presentation-font-size: 16px;
|
||
|
||
/* may need to tweak cursor width if you change font size */
|
||
--jp-code-cursor-width0: 1.4px;
|
||
--jp-code-cursor-width1: 2px;
|
||
--jp-code-cursor-width2: 4px;
|
||
|
||
/* Layout
|
||
*
|
||
* The following are the main layout colors use in JupyterLab. In a light
|
||
* theme these would go from light to dark.
|
||
*/
|
||
|
||
--jp-layout-color0: white;
|
||
--jp-layout-color1: white;
|
||
--jp-layout-color2: var(--md-grey-200);
|
||
--jp-layout-color3: var(--md-grey-400);
|
||
--jp-layout-color4: var(--md-grey-600);
|
||
|
||
/* Inverse Layout
|
||
*
|
||
* The following are the inverse layout colors use in JupyterLab. In a light
|
||
* theme these would go from dark to light.
|
||
*/
|
||
|
||
--jp-inverse-layout-color0: #111111;
|
||
--jp-inverse-layout-color1: var(--md-grey-900);
|
||
--jp-inverse-layout-color2: var(--md-grey-800);
|
||
--jp-inverse-layout-color3: var(--md-grey-700);
|
||
--jp-inverse-layout-color4: var(--md-grey-600);
|
||
|
||
/* Brand/accent */
|
||
|
||
--jp-brand-color0: var(--md-blue-900);
|
||
--jp-brand-color1: var(--md-blue-700);
|
||
--jp-brand-color2: var(--md-blue-300);
|
||
--jp-brand-color3: var(--md-blue-100);
|
||
--jp-brand-color4: var(--md-blue-50);
|
||
|
||
--jp-accent-color0: var(--md-green-900);
|
||
--jp-accent-color1: var(--md-green-700);
|
||
--jp-accent-color2: var(--md-green-300);
|
||
--jp-accent-color3: var(--md-green-100);
|
||
|
||
/* State colors (warn, error, success, info) */
|
||
|
||
--jp-warn-color0: var(--md-orange-900);
|
||
--jp-warn-color1: var(--md-orange-700);
|
||
--jp-warn-color2: var(--md-orange-300);
|
||
--jp-warn-color3: var(--md-orange-100);
|
||
|
||
--jp-error-color0: var(--md-red-900);
|
||
--jp-error-color1: var(--md-red-700);
|
||
--jp-error-color2: var(--md-red-300);
|
||
--jp-error-color3: var(--md-red-100);
|
||
|
||
--jp-success-color0: var(--md-green-900);
|
||
--jp-success-color1: var(--md-green-700);
|
||
--jp-success-color2: var(--md-green-300);
|
||
--jp-success-color3: var(--md-green-100);
|
||
|
||
--jp-info-color0: var(--md-cyan-900);
|
||
--jp-info-color1: var(--md-cyan-700);
|
||
--jp-info-color2: var(--md-cyan-300);
|
||
--jp-info-color3: var(--md-cyan-100);
|
||
|
||
/* Cell specific styles */
|
||
|
||
--jp-cell-padding: 5px;
|
||
|
||
--jp-cell-collapser-width: 8px;
|
||
--jp-cell-collapser-min-height: 20px;
|
||
--jp-cell-collapser-not-active-hover-opacity: 0.6;
|
||
|
||
--jp-cell-editor-background: var(--md-grey-100);
|
||
--jp-cell-editor-border-color: var(--md-grey-300);
|
||
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-cell-editor-active-background: var(--jp-layout-color0);
|
||
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
|
||
|
||
--jp-cell-prompt-width: 64px;
|
||
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
|
||
--jp-cell-prompt-letter-spacing: 0px;
|
||
--jp-cell-prompt-opacity: 1;
|
||
--jp-cell-prompt-not-active-opacity: 0.5;
|
||
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
|
||
/* A custom blend of MD grey and blue 600
|
||
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
|
||
--jp-cell-inprompt-font-color: #307fc1;
|
||
/* A custom blend of MD grey and orange 600
|
||
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
|
||
--jp-cell-outprompt-font-color: #bf5b3d;
|
||
|
||
/* Notebook specific styles */
|
||
|
||
--jp-notebook-padding: 10px;
|
||
--jp-notebook-select-background: var(--jp-layout-color1);
|
||
--jp-notebook-multiselected-color: var(--md-blue-50);
|
||
|
||
/* The scroll padding is calculated to fill enough space at the bottom of the
|
||
notebook to show one single-line cell (with appropriate padding) at the top
|
||
when the notebook is scrolled all the way to the bottom. We also subtract one
|
||
pixel so that no scrollbar appears if we have just one single-line cell in the
|
||
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
|
||
*/
|
||
--jp-notebook-scroll-padding: calc(
|
||
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
|
||
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
|
||
);
|
||
|
||
/* Rendermime styles */
|
||
|
||
--jp-rendermime-error-background: #fdd;
|
||
--jp-rendermime-table-row-background: var(--md-grey-100);
|
||
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
|
||
|
||
/* Dialog specific styles */
|
||
|
||
--jp-dialog-background: rgba(0, 0, 0, 0.25);
|
||
|
||
/* Console specific styles */
|
||
|
||
--jp-console-padding: 10px;
|
||
|
||
/* Toolbar specific styles */
|
||
|
||
--jp-toolbar-border-color: var(--jp-border-color1);
|
||
--jp-toolbar-micro-height: 8px;
|
||
--jp-toolbar-background: var(--jp-layout-color1);
|
||
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
|
||
--jp-toolbar-header-margin: 4px 4px 0px 4px;
|
||
--jp-toolbar-active-background: var(--md-grey-300);
|
||
|
||
/* Statusbar specific styles */
|
||
|
||
--jp-statusbar-height: 24px;
|
||
|
||
/* Input field styles */
|
||
|
||
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-input-active-background: var(--jp-layout-color1);
|
||
--jp-input-hover-background: var(--jp-layout-color1);
|
||
--jp-input-background: var(--md-grey-100);
|
||
--jp-input-border-color: var(--jp-border-color1);
|
||
--jp-input-active-border-color: var(--jp-brand-color1);
|
||
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
|
||
|
||
/* General editor styles */
|
||
|
||
--jp-editor-selected-background: #d9d9d9;
|
||
--jp-editor-selected-focused-background: #d7d4f0;
|
||
--jp-editor-cursor-color: var(--jp-ui-font-color0);
|
||
|
||
/* Code mirror specific styles */
|
||
|
||
--jp-mirror-editor-keyword-color: #008000;
|
||
--jp-mirror-editor-atom-color: #88f;
|
||
--jp-mirror-editor-number-color: #080;
|
||
--jp-mirror-editor-def-color: #00f;
|
||
--jp-mirror-editor-variable-color: var(--md-grey-900);
|
||
--jp-mirror-editor-variable-2-color: #05a;
|
||
--jp-mirror-editor-variable-3-color: #085;
|
||
--jp-mirror-editor-punctuation-color: #05a;
|
||
--jp-mirror-editor-property-color: #05a;
|
||
--jp-mirror-editor-operator-color: #aa22ff;
|
||
--jp-mirror-editor-comment-color: #408080;
|
||
--jp-mirror-editor-string-color: #ba2121;
|
||
--jp-mirror-editor-string-2-color: #708;
|
||
--jp-mirror-editor-meta-color: #aa22ff;
|
||
--jp-mirror-editor-qualifier-color: #555;
|
||
--jp-mirror-editor-builtin-color: #008000;
|
||
--jp-mirror-editor-bracket-color: #997;
|
||
--jp-mirror-editor-tag-color: #170;
|
||
--jp-mirror-editor-attribute-color: #00c;
|
||
--jp-mirror-editor-header-color: blue;
|
||
--jp-mirror-editor-quote-color: #090;
|
||
--jp-mirror-editor-link-color: #00c;
|
||
--jp-mirror-editor-error-color: #f00;
|
||
--jp-mirror-editor-hr-color: #999;
|
||
|
||
/* Vega extension styles */
|
||
|
||
--jp-vega-background: white;
|
||
|
||
/* Sidebar-related styles */
|
||
|
||
--jp-sidebar-min-width: 250px;
|
||
|
||
/* Search-related styles */
|
||
|
||
--jp-search-toggle-off-opacity: 0.5;
|
||
--jp-search-toggle-hover-opacity: 0.8;
|
||
--jp-search-toggle-on-opacity: 1;
|
||
--jp-search-selected-match-background-color: rgb(245, 200, 0);
|
||
--jp-search-selected-match-color: black;
|
||
--jp-search-unselected-match-background-color: var(
|
||
--jp-inverse-layout-color0
|
||
);
|
||
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
|
||
|
||
/* Icon colors that work well with light or dark backgrounds */
|
||
--jp-icon-contrast-color0: var(--md-purple-600);
|
||
--jp-icon-contrast-color1: var(--md-green-600);
|
||
--jp-icon-contrast-color2: var(--md-pink-600);
|
||
--jp-icon-contrast-color3: var(--md-blue-600);
|
||
}
|
||
|
||
[data-md-color-scheme="slate"] .jupyter-wrapper {
|
||
/* Elevation
|
||
*
|
||
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
|
||
*
|
||
* https://github.com/material-components/material-components-web
|
||
* https://material-components-web.appspot.com/elevation.html
|
||
*/
|
||
|
||
/* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
|
||
* colors used in the theme as well. */
|
||
--jp-shadow-base-lightness: 32;
|
||
--jp-shadow-umbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.2
|
||
);
|
||
--jp-shadow-penumbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.14
|
||
);
|
||
--jp-shadow-ambient-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.12
|
||
);
|
||
--jp-elevation-z0: none;
|
||
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
|
||
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 3px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
|
||
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 5px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
|
||
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 10px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
|
||
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 18px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
|
||
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
|
||
0px 3px 14px 2px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
|
||
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
|
||
0px 5px 22px 4px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
|
||
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
|
||
0px 6px 30px 5px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
|
||
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
|
||
0px 8px 38px 7px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
|
||
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
|
||
0px 9px 46px 8px var(--jp-shadow-ambient-color);
|
||
|
||
/* Borders
|
||
*
|
||
* The following variables, specify the visual styling of borders in JupyterLab.
|
||
*/
|
||
|
||
--jp-border-width: 1px;
|
||
--jp-border-color0: var(--md-grey-700);
|
||
--jp-border-color1: var(--md-grey-700);
|
||
--jp-border-color2: var(--md-grey-800);
|
||
--jp-border-color3: var(--md-grey-900);
|
||
--jp-border-radius: 2px;
|
||
|
||
/* UI Fonts
|
||
*
|
||
* The UI font CSS variables are used for the typography all of the JupyterLab
|
||
* user interface elements that are not directly user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-ui-font-scale-factor: 1.2;
|
||
--jp-ui-font-size0: 0.83333em;
|
||
--jp-ui-font-size1: 13px; /* Base font size */
|
||
--jp-ui-font-size2: 1.2em;
|
||
--jp-ui-font-size3: 1.44em;
|
||
|
||
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Use these font colors against the corresponding main layout colors.
|
||
* In a light theme, these go from dark to light.
|
||
*/
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-ui-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-ui-font-color1: rgba(255, 255, 255, 0.87);
|
||
--jp-ui-font-color2: rgba(255, 255, 255, 0.54);
|
||
--jp-ui-font-color3: rgba(255, 255, 255, 0.38);
|
||
|
||
/*
|
||
* Use these against the brand/accent/warn/error colors.
|
||
* These will typically go from light to darker, in both a dark and light theme.
|
||
*/
|
||
|
||
--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
|
||
--jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
|
||
--jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
|
||
|
||
/* Content Fonts
|
||
*
|
||
* Content font variables are used for typography of user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-content-line-height: 1.6;
|
||
--jp-content-font-scale-factor: 1.2;
|
||
--jp-content-font-size0: 0.83333em;
|
||
--jp-content-font-size1: 14px; /* Base font size */
|
||
--jp-content-font-size2: 1.2em;
|
||
--jp-content-font-size3: 1.44em;
|
||
--jp-content-font-size4: 1.728em;
|
||
--jp-content-font-size5: 2.0736em;
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-content-presentation-font-size1: 17px;
|
||
|
||
--jp-content-heading-line-height: 1;
|
||
--jp-content-heading-margin-top: 1.2em;
|
||
--jp-content-heading-margin-bottom: 0.8em;
|
||
--jp-content-heading-font-weight: 500;
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-content-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-content-font-color1: rgba(255, 255, 255, 1);
|
||
--jp-content-font-color2: rgba(255, 255, 255, 0.7);
|
||
--jp-content-font-color3: rgba(255, 255, 255, 0.5);
|
||
|
||
--jp-content-link-color: var(--md-blue-300);
|
||
|
||
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Code Fonts
|
||
*
|
||
* Code font variables are used for typography of code and other monospaces content.
|
||
*/
|
||
|
||
--jp-code-font-size: 13px;
|
||
--jp-code-line-height: 1.3077; /* 17px for 13px base */
|
||
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
|
||
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
|
||
monospace;
|
||
--jp-code-font-family: var(--jp-code-font-family-default);
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-code-presentation-font-size: 16px;
|
||
|
||
/* may need to tweak cursor width if you change font size */
|
||
--jp-code-cursor-width0: 1.4px;
|
||
--jp-code-cursor-width1: 2px;
|
||
--jp-code-cursor-width2: 4px;
|
||
|
||
/* Layout
|
||
*
|
||
* The following are the main layout colors use in JupyterLab. In a light
|
||
* theme these would go from light to dark.
|
||
*/
|
||
|
||
--jp-layout-color0: #111111;
|
||
--jp-layout-color1: var(--md-grey-900);
|
||
--jp-layout-color2: var(--md-grey-800);
|
||
--jp-layout-color3: var(--md-grey-700);
|
||
--jp-layout-color4: var(--md-grey-600);
|
||
|
||
/* Inverse Layout
|
||
*
|
||
* The following are the inverse layout colors use in JupyterLab. In a light
|
||
* theme these would go from dark to light.
|
||
*/
|
||
|
||
--jp-inverse-layout-color0: white;
|
||
--jp-inverse-layout-color1: white;
|
||
--jp-inverse-layout-color2: var(--md-grey-200);
|
||
--jp-inverse-layout-color3: var(--md-grey-400);
|
||
--jp-inverse-layout-color4: var(--md-grey-600);
|
||
|
||
/* Brand/accent */
|
||
|
||
--jp-brand-color0: var(--md-blue-700);
|
||
--jp-brand-color1: var(--md-blue-500);
|
||
--jp-brand-color2: var(--md-blue-300);
|
||
--jp-brand-color3: var(--md-blue-100);
|
||
--jp-brand-color4: var(--md-blue-50);
|
||
|
||
--jp-accent-color0: var(--md-green-700);
|
||
--jp-accent-color1: var(--md-green-500);
|
||
--jp-accent-color2: var(--md-green-300);
|
||
--jp-accent-color3: var(--md-green-100);
|
||
|
||
/* State colors (warn, error, success, info) */
|
||
|
||
--jp-warn-color0: var(--md-orange-700);
|
||
--jp-warn-color1: var(--md-orange-500);
|
||
--jp-warn-color2: var(--md-orange-300);
|
||
--jp-warn-color3: var(--md-orange-100);
|
||
|
||
--jp-error-color0: var(--md-red-700);
|
||
--jp-error-color1: var(--md-red-500);
|
||
--jp-error-color2: var(--md-red-300);
|
||
--jp-error-color3: var(--md-red-100);
|
||
|
||
--jp-success-color0: var(--md-green-700);
|
||
--jp-success-color1: var(--md-green-500);
|
||
--jp-success-color2: var(--md-green-300);
|
||
--jp-success-color3: var(--md-green-100);
|
||
|
||
--jp-info-color0: var(--md-cyan-700);
|
||
--jp-info-color1: var(--md-cyan-500);
|
||
--jp-info-color2: var(--md-cyan-300);
|
||
--jp-info-color3: var(--md-cyan-100);
|
||
|
||
/* Cell specific styles */
|
||
|
||
--jp-cell-padding: 5px;
|
||
|
||
--jp-cell-collapser-width: 8px;
|
||
--jp-cell-collapser-min-height: 20px;
|
||
--jp-cell-collapser-not-active-hover-opacity: 0.6;
|
||
|
||
--jp-cell-editor-background: var(--jp-layout-color1);
|
||
--jp-cell-editor-border-color: var(--md-grey-700);
|
||
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-cell-editor-active-background: var(--jp-layout-color0);
|
||
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
|
||
|
||
--jp-cell-prompt-width: 64px;
|
||
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
|
||
--jp-cell-prompt-letter-spacing: 0px;
|
||
--jp-cell-prompt-opacity: 1;
|
||
--jp-cell-prompt-not-active-opacity: 1;
|
||
--jp-cell-prompt-not-active-font-color: var(--md-grey-300);
|
||
|
||
/* A custom blend of MD grey and blue 600
|
||
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
|
||
--jp-cell-inprompt-font-color: #307fc1;
|
||
/* A custom blend of MD grey and orange 600
|
||
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
|
||
--jp-cell-outprompt-font-color: #bf5b3d;
|
||
|
||
/* Notebook specific styles */
|
||
|
||
--jp-notebook-padding: 10px;
|
||
--jp-notebook-select-background: var(--jp-layout-color1);
|
||
--jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
|
||
|
||
/* The scroll padding is calculated to fill enough space at the bottom of the
|
||
notebook to show one single-line cell (with appropriate padding) at the top
|
||
when the notebook is scrolled all the way to the bottom. We also subtract one
|
||
pixel so that no scrollbar appears if we have just one single-line cell in the
|
||
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
|
||
*/
|
||
--jp-notebook-scroll-padding: calc(
|
||
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
|
||
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
|
||
);
|
||
|
||
/* Rendermime styles */
|
||
|
||
--jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
|
||
--jp-rendermime-table-row-background: var(--md-grey-900);
|
||
--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
|
||
|
||
/* Dialog specific styles */
|
||
|
||
--jp-dialog-background: rgba(0, 0, 0, 0.6);
|
||
|
||
/* Console specific styles */
|
||
|
||
--jp-console-padding: 10px;
|
||
|
||
/* Toolbar specific styles */
|
||
|
||
--jp-toolbar-border-color: var(--jp-border-color2);
|
||
--jp-toolbar-micro-height: 8px;
|
||
--jp-toolbar-background: var(--jp-layout-color1);
|
||
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
|
||
--jp-toolbar-header-margin: 4px 4px 0px 4px;
|
||
--jp-toolbar-active-background: var(--jp-layout-color0);
|
||
|
||
/* Statusbar specific styles */
|
||
|
||
--jp-statusbar-height: 24px;
|
||
|
||
/* Input field styles */
|
||
|
||
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-input-active-background: var(--jp-layout-color0);
|
||
--jp-input-hover-background: var(--jp-layout-color2);
|
||
--jp-input-background: var(--md-grey-800);
|
||
--jp-input-border-color: var(--jp-border-color1);
|
||
--jp-input-active-border-color: var(--jp-brand-color1);
|
||
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
|
||
|
||
/* General editor styles */
|
||
|
||
--jp-editor-selected-background: var(--jp-layout-color2);
|
||
--jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
|
||
--jp-editor-cursor-color: var(--jp-ui-font-color0);
|
||
|
||
/* Code mirror specific styles */
|
||
|
||
--jp-mirror-editor-keyword-color: var(--md-green-500);
|
||
--jp-mirror-editor-atom-color: var(--md-blue-300);
|
||
--jp-mirror-editor-number-color: var(--md-green-400);
|
||
--jp-mirror-editor-def-color: var(--md-blue-600);
|
||
--jp-mirror-editor-variable-color: var(--md-grey-300);
|
||
--jp-mirror-editor-variable-2-color: var(--md-blue-400);
|
||
--jp-mirror-editor-variable-3-color: var(--md-green-600);
|
||
--jp-mirror-editor-punctuation-color: var(--md-blue-400);
|
||
--jp-mirror-editor-property-color: var(--md-blue-400);
|
||
--jp-mirror-editor-operator-color: #aa22ff;
|
||
--jp-mirror-editor-comment-color: #408080;
|
||
--jp-mirror-editor-string-color: #ff7070;
|
||
--jp-mirror-editor-string-2-color: var(--md-purple-300);
|
||
--jp-mirror-editor-meta-color: #aa22ff;
|
||
--jp-mirror-editor-qualifier-color: #555;
|
||
--jp-mirror-editor-builtin-color: var(--md-green-600);
|
||
--jp-mirror-editor-bracket-color: #997;
|
||
--jp-mirror-editor-tag-color: var(--md-green-700);
|
||
--jp-mirror-editor-attribute-color: var(--md-blue-700);
|
||
--jp-mirror-editor-header-color: var(--md-blue-500);
|
||
--jp-mirror-editor-quote-color: var(--md-green-300);
|
||
--jp-mirror-editor-link-color: var(--md-blue-700);
|
||
--jp-mirror-editor-error-color: #f00;
|
||
--jp-mirror-editor-hr-color: #999;
|
||
|
||
/* Vega extension styles */
|
||
|
||
--jp-vega-background: var(--md-grey-400);
|
||
|
||
/* Sidebar-related styles */
|
||
|
||
--jp-sidebar-min-width: 250px;
|
||
|
||
/* Search-related styles */
|
||
|
||
--jp-search-toggle-off-opacity: 0.6;
|
||
--jp-search-toggle-hover-opacity: 0.8;
|
||
--jp-search-toggle-on-opacity: 1;
|
||
--jp-search-selected-match-background-color: rgb(255, 225, 0);
|
||
--jp-search-selected-match-color: black;
|
||
--jp-search-unselected-match-background-color: var(
|
||
--jp-inverse-layout-color0
|
||
);
|
||
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
|
||
|
||
/* scrollbar related styles. Supports every browser except Edge. */
|
||
|
||
/* colors based on JetBrain's Darcula theme */
|
||
|
||
--jp-scrollbar-background-color: #3f4244;
|
||
--jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
|
||
|
||
--jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
|
||
|
||
/* hacks for setting the thumb shape. These do nothing in Firefox */
|
||
|
||
--jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
|
||
--jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
|
||
|
||
/* Icon colors that work well with light or dark backgrounds */
|
||
--jp-icon-contrast-color0: var(--md-purple-600);
|
||
--jp-icon-contrast-color1: var(--md-green-600);
|
||
--jp-icon-contrast-color2: var(--md-pink-600);
|
||
--jp-icon-contrast-color3: var(--md-blue-600);
|
||
}
|
||
|
||
:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: 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#3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
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Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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*//*!
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Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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<h1 id="train-your-model">Train your model<a class="anchor-link" href="#train-your-model">¶</a></h1><p>The first step is, of course, to install <code>great-ai</code> in your Python environment.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">great</span><span class="o">-</span><span class="n">ai</span> <span class="o">></span> <span class="o">/</span><span class="n">dev</span><span class="o">/</span><span class="n">null</span>
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</pre></div>
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<div id="cell-1" class="clipboard-copy-txt">%pip install great-ai > /dev/null</div>
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<pre>Note: you may need to restart the kernel to use updated packages.
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<p>First, we have to get some data. After downloading it <a href="https://github.com/allenai/scibert/tree/master/data/text_classification/mag">from here</a>, we might notice that the dataset is in <a href="https://jsonlines.org/">JSON Lines</a> format (each line is a separate JSON document).</p>
|
||
<p>Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also <a href="/reference/utilities/#great_ai.utilities.clean.clean">cleaned</a> to remove any LaTeX, XML, unicode, PDF-extraction artifacts.</p>
|
||
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">json</span>
|
||
<span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">clean</span>
|
||
|
||
|
||
<span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
|
||
<span class="n">data_point</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
|
||
|
||
<span class="n">sentence</span> <span class="o">=</span> <span class="n">data_point</span><span class="p">[</span><span class="s2">"text"</span><span class="p">]</span>
|
||
<span class="n">label</span> <span class="o">=</span> <span class="n">data_point</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]</span>
|
||
|
||
<span class="k">return</span> <span class="n">clean</span><span class="p">(</span><span class="n">sentence</span><span class="p">),</span> <span class="n">label</span>
|
||
</pre></div>
|
||
<div id="cell-2" class="clipboard-copy-txt">import json
|
||
from great_ai.utilities import clean
|
||
|
||
|
||
def preprocess(line):
|
||
data_point = json.loads(line)
|
||
|
||
sentence = data_point["text"]
|
||
label = data_point["label"]
|
||
|
||
return clean(sentence), label</div>
|
||
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<p>Now, we can load the dataset and extract the <em>training</em> samples from it. Since we're impatient, we can do it in parallel using the <a href="/reference/utilities/#great_ai.utilities.simple_parallel_map"><code>simple_parallel_map</code></a> function.</p>
|
||
<blockquote><p>Open files in Python are iterable: in text mode, each iteration returns the next line.</p>
|
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">simple_parallel_map</span>
|
||
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"data/train.txt"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">training_data</span> <span class="o">=</span> <span class="n">simple_parallel_map</span><span class="p">(</span><span class="n">preprocess</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
|
||
|
||
<span class="n">X_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">]</span>
|
||
<span class="n">y_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">]</span>
|
||
</pre></div>
|
||
<div id="cell-3" class="clipboard-copy-txt">from great_ai.utilities import simple_parallel_map
|
||
|
||
with open("data/train.txt", encoding="utf-8") as f:
|
||
training_data = simple_parallel_map(preprocess, f)
|
||
|
||
X_train = [d[0] for d in training_data]
|
||
y_train = [d[1] for d in training_data]</div>
|
||
|
||
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|
||
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|
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||
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|
||
<pre>100%|██████████| 84000/84000 [00:09<00:00, 8960.63it/s]
|
||
</pre>
|
||
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|
||
</div>
|
||
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||
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||
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||
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|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
|
||
<p>Let's do the same for the <em>test</em> data.</p>
|
||
|
||
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|
||
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|
||
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|
||
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||
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||
<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
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||
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
<svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1" data-view-component="true" class="clipboard-copy-icon">
|
||
<path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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||
<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
|
||
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|
||
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|
||
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|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"data/test.txt"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">test_data</span> <span class="o">=</span> <span class="n">simple_parallel_map</span><span class="p">(</span><span class="n">preprocess</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
|
||
|
||
<span class="n">X_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">]</span>
|
||
<span class="n">y_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">]</span>
|
||
</pre></div>
|
||
<div id="cell-4" class="clipboard-copy-txt">with open("data/test.txt", encoding="utf-8") as f:
|
||
test_data = simple_parallel_map(preprocess, f)
|
||
|
||
X_test = [d[0] for d in test_data]
|
||
y_test = [d[1] for d in test_data]</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
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|
||
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|
||
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||
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||
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|
||
|
||
|
||
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|
||
<pre>100%|██████████| 22399/22399 [00:03<00:00, 6078.02it/s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
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|
||
|
||
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|
||
|
||
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|
||
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||
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|
||
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|
||
<p>After obtaining some clean data, it's time to create and train a model on it. We are going to use <a href="https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html">scikit-learn</a> for this purpose.</p>
|
||
<p>In this case, we opted for a <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html">linear Support Vector Machine</a> because it's known to be an adequate baseline for simple classification tasks.</p>
|
||
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||
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||
<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
|
||
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|
||
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|
||
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|
||
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|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
|
||
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">TfidfVectorizer</span><span class="p">(),</span> <span class="n">LinearSVC</span><span class="p">())</span>
|
||
<span class="c1"># todo: hyperparameter-optimisation</span>
|
||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-5" class="clipboard-copy-txt">from sklearn.pipeline import make_pipeline
|
||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||
from sklearn.svm import LinearSVC
|
||
|
||
model = make_pipeline(TfidfVectorizer(), LinearSVC())
|
||
# todo: hyperparameter-optimisation
|
||
model.fit(X_train, y_train)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
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|
||
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|
||
|
||
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|
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|
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|
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|
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||
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|
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|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
|
||
|
||
|
||
|
||
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),
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('linearsvc', LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),
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('linearsvc', LinearSVC())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfVectorizer</label><div class="sk-toggleable__content"><pre>TfidfVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">LinearSVC</label><div class="sk-toggleable__content"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>
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<p>It's already finished. Let's see how well it performs. But first, we need to <a href="https://matplotlib.org/stable/tutorials/introductory/customizing.html">configure matplotlib</a> to make the charts more legible.</p>
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<blockquote><p><code>ConfusionMatrixDisplay</code> relies on <code>matplotlib</code> for rendering.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.figsize"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.facecolor"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"white"</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"font.size"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">12</span>
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</pre></div>
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<div id="cell-6" class="clipboard-copy-txt">import matplotlib.pyplot as plt
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%matplotlib inline
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plt.rcParams["figure.figsize"] = (30, 15)
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plt.rcParams["figure.facecolor"] = "white"
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plt.rcParams["font.size"] = 12</div>
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<p>Next, we check the quality of the model on the <code>test</code> split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
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<span class="n">y_predicted</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">))</span>
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<span class="n">metrics</span><span class="o">.</span><span class="n">ConfusionMatrixDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">)</span>
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<span class="kc">None</span>
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||
</pre></div>
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<div id="cell-7" class="clipboard-copy-txt">from sklearn import metrics
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y_predicted = model.predict(X_test)
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print(metrics.classification_report(y_test, y_predicted))
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metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted)
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None</div>
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<pre> precision recall f1-score support
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business 0.67 0.69 0.68 3198
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economics 0.69 0.71 0.70 3189
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geography 0.70 0.73 0.72 3207
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medicine 0.90 0.91 0.90 3187
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politics 0.63 0.57 0.60 3169
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psychology 0.73 0.73 0.73 3252
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sociology 0.51 0.51 0.51 3197
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accuracy 0.69 22399
|
||
macro avg 0.69 0.69 0.69 22399
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weighted avg 0.69 0.69 0.69 22399
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</pre>
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<img 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cbiVVR2AAAAAAAASyHZAQAAAAAALIU2FgAAAAAAzEYbi1dR2QEAAAAAACyFZAcAAAAAALAU2lgAAAAAADCTwWws3kZlBwAAAAAAsBSSHQAAAAAAwFJoYwEAAAAAwGy0sXgVlR0AAAAAAMBSSHYAAAAAAABLIdkBAAAAAAAshTE7AAAAAAAwG2N2eBWVHQAAAAAAwFJIdgAAAAAAAEuhjQUAAAAAAJPZaGPxKio7AAAAAACApZDsAAAAAAAAlkIbCwAAAAAAZqONxauo7AAAAAAAAJZCsgMAAAAAAFgKbSwAAAAAAJjJEG0sXkZlBwAAAAAAsBSSHQAAAAAAwFJoYwEAAAAAwGQ22li8isoOAAAAAABgKSQ7AAAAAACApdDGAgAAAACA2Whj8SoqOwAAAAAAgKWQ7AAAAAAAAJZCsgMAAAAAAFgKY3YAAAAAAGAypp71Lio7AAAAAACApZDsAAAAAAAAlkIbCwAAAAAAZqONxauo7AAAAAAAAJZCsgMAAAAAAFgKbSwAAAAAAJjJEG0sXkZlBwAAAAAAsBSSHQAAAAAAwFJoYwEAAAAAwES2/97gPVR2AAAAAAAASyHZAQAAAAAALIU2FgAAAAAAzMZsLF5FZQcAAAAAALAUkh0AAAAAAMBSSHYAAAAAAABLYcwOSTabTdu3b1eDBg10++23q1atWpowYcJxt508ebJ27dql6dOnn+Uoqz8fp1tjHtmklu2zFBxarkP7AzXj1YZavSxaPj5uPfj0OjVsclgx8SV6ZFRbrV9dw7Pvky+vVtNWOVWe60BKkMYMvMyMQ8F/9Rq4V937pCqpQYGWfBWrqU8086y7qu9+9b91jyKiyrTxt3C99GQTZWf4S5KeevVXNW2V69nWx+nWgT1BunPAJWf7EHCUXjfuqzyfDQu0ZGGspj7e1LOu45VpGnzHTkXFlCrzkL/ee6W+ln9f85jnmDxttVq2z1Gv1l3ldpEvr0763Jqh7gOyldS4REs+D9eUexM96zr1ztGQ+w8pKq5cGalOvftsnJYvCjcvWEiSghdmKej7HDn3lqqoQ5iyx9b2rAtYdlhhH6bLkVUuV5RTh2+KUXG70CP7zstU6OeZspW6VXRxqHJGxUvOqn+TfhsLVfOJ3Tp8fbTyBsWctePCEb0Gp6p73zQlXVCoJV9Ga+rfGkmSGrXI0y3jUtSgaYHcbmndL+F64+n6ysnwlSQ1b5+rQXfuVYMmBSrI89FtV7Qz8zDwO13a7dTQa39TzchCZR8O0P9N76QKl13D+q3WBYmZcht2rdkSq1dmXaLsw4GSJKePS2NvWqEOrffIx+HWhh0xmvreZcrMDTL5aFCd2Bizw6tIdvzOG2+84bm/ZMkS3Xzzzdq/f79n2aOPPmpGWOcEh8OtjDR/PTyynTIO+Su5Q4YeeXatxgy8TNkZftq0Jlyfz07U3/5vzTH7PjGuTZXHz7z5i9atqnHMdji7sjP8NOetemp9aZZ8/Vye5Re1ydbQsTv0yMhkpe4N1OiHturhZ9br4RFtJUmPj21d5XmefWuV1q6MOKux41iV57Puf8+n27M8smaJHpi8QX+/u4VW/RSpth2z9Lfn1+m2azrocLavZ7vLrzkoHx/ehaurrDSnPvhHjJIvz5ev/1HnN7ZMD728VxOH1dWq70PU7oo8jX9zj25pH6TDWU4TI4Yrwkd5N9SU/5oC2cqOnDNHVrkiX96vzIfqqKRVsPx/LVDklL06+HojucN85L8mX6GfZSh9Yl25IpyKei5FYR+m6/DNsUeevMJQ+LsHVdowwIQjw/9kp/tqzusJat0hp8rfZUhohRZ+FKvVSyPkdtl0x4SdunfyNj0+svKiQkmRQ9/MjdEPX0Zr4Oh9ZoWP42jT5IBG9V+pp17vqi27oxUZViRJqpeQrflLGmvlhlpyue26++Zlenj4f/Twiz0kSdd336im9dM14vF+Kihy6oFbf9JdNy/XE692M/NwAEvjshy8prTERx9Ma6D0gwEyDJtW/lhTaakBanDhYVVU2PX57CRtWlP5pn4yNeOK1bRVjhbPr3WWIseJLPsuRsuX1FR+btUvRO06ZWrpNzHauytYFRV2zX6rri5qk6vY2kXHPIfnfM6LP1th4wSWLa6p5d8fez6jYkpVmO+jVT9FSbJp5Y9RKi12KO6o8xkYXKHBt+/W21MbnuWocbp+Whiu5YvClZdT9TpGVFy5CvMcWvV9qCSbflkcppIiu+KTyswJFB7FF4epuF2oXCGOKssdWeVyB9pV0jpEstlU0iZEhr9dPocqz1ngklwVdo1QRYK/jGCH8m6oqaAluVWeI2RepkpaBKuilt/ZOhwcx7JvorR8cdQx/3dX/VhDSxdFq7jQR6UlDs2bFa8mrfM867etD9F3X8To0D7/sx0yTuHWvr/q/S9aafOumjIMmzJzg5SZG6Rf1ifoh1V1VVTiq9IyH326uImaNUj37Bcbla+VG2opJy9A5RU++v6XukqKzzXvQIDzgKWSHUlJSXrmmWfUpEkTRURE6LbbblNJSYkk6a233lKDBg1Uo0YN9enTR6mpqcd9jltvvVWPPfaYCgsLdfXVVys1NVXBwcEKDg5WamqqJk6cqJtvvtmz/dKlS3XppZcqPDxcCQkJmjFjhiRpwYIFatKkiUJCQlSrVi298MILZ/z4q5vwGqWqVadIe3cF/6H9ruh1QBt/i1D6Qa5GVWu2Y+8mNSg4ZrMreqdyPqu57RtDtW9XkNp3zpDdbuiSLukqL7dr9/YQzza33rVDX35USzlZvid5JlRH29cGau92P13c/XDl+b0qV+Vldu3axJeo6qqsfoDKa/vJf2We5DIU8EueDB+byhMrz5lzX6nKko78Ty1P8pcjt0L2/ApJkiOjTEHf5SjvhmhT4scf1yz5sPZuDzQ7DJyC3eZWo6RMhYWUaOazH+mjKbM17uZl8nVWHLNt8wsOaU9quOfxgh8vULOGaYoML5Sfb4W6XbJTv6yvfcx+OM8Z1fx2jrFcG8usWbO0aNEiBQUFqXfv3po0aZK6du2qv/3tb/r666/VtGlTPfDAA7rxxhv1n//854TPExQUpIULFx7TxnK0lJQUXX311Zo2bZpuuOEG5eXlad++ylLD4cOH66OPPlLHjh2Vk5Oj3bt3n5Hjra4cPm49OGmdFs+P1/49fyzZ0bVnqj58u/4ZigzesHpZpB5+Zr0WfFxbqXsDNWjULrndkp+/65htr+h5UHOm1zMhSpwut9umxfPj9NCzG+Tr61Z5uU3PPNhcpcWVV5sbNsnThS1z9cZzFygqptTkaPFHud02fftxDT3yWop8/SrP79OjkzznF9WQw6aizuGK/Md+2crcMnxsyrq/jgz/ymtU9hKXjMAj16vcgZXn0lbslkKkiLcP6vCNMTICOMfngqQLCnXTnXv11JgmZoeCU4gIK5bTx63OyXs07pleqnDZ9fS4bzSk9xq9/UmyZ7t6tbN1S5/f9NjL3T3LDqSFKT07SB9PnSOXy6Zd+yP0j5mXmnEYwHnDcsmOsWPHKiEhQZI0fvx43XXXXTp48KCGDRum1q0rxxF45plnFBERoT179igpKelP/6wPPvhA3bp106BBgyRJkZGRioyMlCQ5nU5t2rRJLVq0UEREhCIijj9ewbRp0zRt2jRJUpm7+E/HUp3YbIbuf2q9ysvtev25C//Qvk1a5igiskxLv2Ugtepszc+RmvVGfY1/Ya0Cg1z6/IM6Ki70UWZa1SvFTVrmKCKqTEu/PXagS1QfLdtnadg9O/TI8DbasTlEDZrk6Yl/rNXjY1pp97Zg3Tl+i958rhEDkp6jWnXM14jHUvXgDQ20Y32AGjYv1sR3d+mxIU7t2siV5OrIb12Bwt5PU/rEuiqv5y/fXcWKenavMsYnqrxugNz+jsrExn/ZiysTzUaAXf6r8mQrcav4sjCzwscfEFenWE+9tUFvTq6njas5Z9VdaVnlV6dPFzfxDDz670XNdPNRyY74mnl69r5FevWDi7V++5FxdO4eskxOH7f6jL1ZJaU+uvHqdfq/exfpzkl9zv6BAOcJy31y/V+iQ5ISExOVmpqq1NRUJSYeGZU+ODhYkZGROnDgwF/6Wfv27VP9+sevQJg7d64WLFigxMREde7cWcuXLz/udqNGjdKqVau0atUq+dqtUOZv6O7HNygislSTH2opV8Uf+xW7otcBLfsuRiXFlsvDWc78jxI08toOGtyts376tqYcPm6l7KhaxdOt90Et+64m57Oaq9eoQBt+Ddf2TaEyDJu2bwzT1vVhatk+W4HBFWrYJE+PPLdeMxf/R/+Y9Ysk6V9fL60ygxKqr/pNirV+RbC2rwuUYdi0bW2gtv4WqNYdjm07Q/Xgu7tEpU0CVd4gQLLbVNYgUKUNA+S/rlCSVJ7gJ989JZ7tnXtK5Ar3kTvER/7rC+W7s1jxI7YofsQWBSw7rJAvsxT1bIpZh4MTqBlfosnvrtecf9bRd19wkedcUFDkp/TsIBlHlfMbR/X1xkTma8qDC/X+Fy31zfKqY1w1SMjSop8aKr/QT+UVDn3ybRNdWD9DocElAjzMblOxWBuL5ZId/2sjkaS9e/cqPj5e8fHxSkk58iZfWFiorKws1ap18gEwbbaTD6SZkJCgnTt3Hndd27Zt9fnnnys9PV3XXXedBgwY8AeO4tw15m+blFC3UE/e01plpVXLZ32cbjl9Xb+7f+SvxtfPpY7d0/TtfAayrC7sjsrzZHcYctj13/uVyxLrF0gyFB1brLsmbNbnH9RRQf6RAdg85/MLzmd1ceR8Sg6H4Tmf2zaGqmmrXNVrlC9Jqtc4T01b52r39mAV5vtoSLeOumtAe901oL0eH9tSknT3oHbaup6rkNWJ3WHI6eeW3W7I7lDlfYehrWsD1ax9geo1rRxwtn7TIjVrX6hdmxmzw3QuQypzy+Y2JHflfbkMlTUIkN/mIjl3V1Z8OncVy29zkcoSKwcbLeocrqDvcuSzr0S2QpdC52ao8PJwSdLhG2vq4MsNdej5+jr0fH2VJIeo8IoIZY9h0G8z2B2GnL7uo95HK+9H1izVMzPWa96seC34MO6Y/Wy2yv18fCq/Sjt93fJxuo/9ATjrvvqxofp226TwkGIFB5bqhis3aPnaOooKL9SUhxbq08VNNG/JsZXNW3ZH68pLdygooEwOh1vXdd2sjJxA5RXwvxg4Uyx3ufW1115Tr169FBgYqKeffloDBw5U165dNWjQIN1000268MIL9eijj6p9+/anbGGJiYlRVlaWDh8+rP9v777DoyjXPo7/No2QXgkJCQmEEkEBlSJIUYyKgoiA9CJYQMSuRwVEVEQBUd8jKIIckaJY8IiAgIqC9CLSe02jJhDS2z7vHzksxoQirmxYvp/rynUlM8/M3jOzszO5936e8fcvfVPfs2dPjRo1Sl9++aU6duyo9PR0JSYmqk6dOvrqq6/Url07+fv7y8/PTy4uTpdXKiW0co7u7pyk/DwXzfhhiW36+FF1tGRBhCZ9s0xhEcXZ65ETfpMk9WvX0jZwZdNbjikrw02b1/HI2fKi+0MH1HPgftvfrdsd1syJ1fXtzKr616gtCo/KVnaWm376LkLTP6hRYtmmtx5TVqYbj5wtR7o/fEA9Hz07flDrdkc088NqmjkxVjMnVteQtzcrIDhf6Sc99MWUGP2+qrhb3snUs09zcP/fI2tPpnrQraWc6fHkEfV+9qjt7/hOJzV9XJhmvBOuGeMq6+WPDiogtFDpqW6a9X6YNvzq58BoIUl+Xx+T/1fHbX97/5qu9PtDdbprmE53qaTgcYlyPVUoq5+rMjqGKq9B8aDBudf76vS9Iao04oAs+UY5N/kpvWtxd0FT0bXEWB3Gw0VWTxdZfZ3ulu+K0P3RBPUcnGD7u/W9xzRzfFUZI4VXzVXPxw6p52Nnv5DrdOPNkqRrG6Vr9LQttulzNq/Q5rX+erFPvcsXPMo0be718vfN1fS3vlZ+gauWrK2mGXPrq3vbzapSKUMP3LtBD9y7wdb+7kf7SpImftFYj/dcrelvfSV3N6sOJAVq+Ps8dhb4J1mMMVdgQUrZYmJiNGDAAE2fPl0pKSm699579eGHH8rLy0sTJ07U2LFjdfLkSTVr1kwTJ05UZGTxCMgWi0V79uxRjRo19MADDygyMlIjR46UJPXv319z5sxRUVGRtm/frkmTJmnv3r2aMWOGJGnZsmV67rnntGPHDvn7+2vkyJHq3r272rdvrzVr1qioqEi1a9fWu+++q+bNm583fn+PSmoW2vWf3Um4rIyVb2GcitVpPi7xP0UnTjg6BNhR4ld1HR0C7Cymf8KFG+GKkdO0lqNDgB1tWPm+MtLLfpAD/jqvSlGq1fUZR4dxXm6rZmr9+vWODuOiOV2y4+OPP1Z8/JWZJSXZ4XxIdjgZkh1Oh2SHcyHZ4XxIdjgXkh3OhWSHfZHssD9qkAEAAAAAgFMh2QEAAAAAgKM5+mkrf/NpLOPHj1fDhg1VoUIFPfDAAyXmLV68WHFxcfLy8tKtt95a4gEieXl56t+/v/z8/FS5cmW98847F73s+ThVsuPgwYNXbBcWAAAAAACuVBERERo2bJj69+9fYvqJEyfUsWNHvf7660pLS1PDhg3VtevZ4RtGjBihPXv26NChQ/rll180ZswYLVy48KKWPR+nSnYAAAAAAIDLr2PHjurQoYOCg4NLTP/mm29Ut25d3X///fL09NSIESO0adMm7dy5U5L06aef6uWXX1ZgYKCuueYaPfzww5o6depFLXs+JDsAAAAAAMB5HT9+XA0bNrT9TJo06aKW27Ztm+rXr2/729vbW7Gxsdq2bZtOnjypw4cPl5hfv359bdu27YLLXggPXQcAAAAAwMEs5fzBf6GhoZf0NJbMzEyFhoaWmObv76+MjAxlZmba/v7zvAsteyFUdgAAAAAAgH+Ej4+PTp8+XWLa6dOn5evrKx8fH9vff553oWUvhGQHAAAAAAD4R9StW1ebNm2y/Z2VlaV9+/apbt26CgwMVHh4eIn5mzZtUt26dS+47IWQ7AAAAAAAwNEc/WjZv/no2cLCQuXm5qqoqEhFRUXKzc1VYWGh7rvvPm3dulWzZ89Wbm6uXnvtNdWrV09xcXGSpD59+mjkyJE6efKkdu7cqcmTJ9seXXuhZc+HZAcAAAAAAPhbRo4cqYoVK+qtt97SjBkzVLFiRY0cOVKhoaGaPXu2hg4dqsDAQK1Zs0azZs2yLffqq68qNjZW0dHRatWqlZ5//nm1adNGki647PlYjDHlfBiUq4e/RyU1C724ZwbjymCsVkeHAHuy8nHpbIpOnHB0CLCjxK8uXNKKK0tM/wRHhwA7ymlay9EhwI42rHxfGelJjg7DaXhVilLtzs84Oozzcl0785IGKHUUnsYCAAAAAICDlfensVxp6MYCAAAAAACcCskOAAAAAADgVOjGAgAAAACAI13kE09w8ajsAAAAAAAAToVkBwAAAAAAcCp0YwEAAAAAwNHoxmJXVHYAAAAAAACnQrIDAAAAAAA4FbqxAAAAAADgQBZJFrqx2BWVHQAAAAAAwKmQ7AAAAAAAAE6FZAcAAAAAAHAqjNkBAAAAAICjMWaHXVHZAQAAAAAAnArJDgAAAAAA4FToxgIAAAAAgINZDP1Y7InKDgAAAAAA4FRIdgAAAAAAAKdCNxYAAAAAABzJiKex2BmVHQAAAAAAwKmQ7AAAAAAAAE6FbiwAAAAAADiYpZx3Yynn4ZVCZQcAAAAAAHAqJDsAAAAAAIBToRsLAAAAAACOdqX1EynnqOwAAAAAAABOhWQHAAAAAABwKiQ7AAAAAACAU2HMDgAAAAAAHIxHz9oXlR0AAAAAAMCpkOwAAAAAAABOhW4sAAAAAAA42pXWT6Sco7IDAAAAAAA4FZIdAAAAAADAqdCNBQAAAAAARzLl/2ksVxoqOwAAAAAAgFMh2QEAAAAAAJwK3VgAAAAAAHA0urHYFZUdAAAAAADAqZDsAAAAAAAAToVuLOWJ1cjk5jk6CtiRNTPL0SHAjlwqejo6BNiZq6+vo0OAHUX32e/oEGBnrdYcd3QIsKOlnSs5OgTYkUt+kaNDcCoW8TQWe6OyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAEcz9GOxJyo7AAAAAACAUyHZAQAAAAAAnArJDgAAAAAA4FQYswMAAAAAAAfj0bP2RWUHAAAAAABwKiQ7AAAAAACAU6EbCwAAAAAAjmT+9wO7obIDAAAAAAA4FZIdAAAAAADAqdCNBQAAAAAAB7NYHR2Bc6GyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAEfjaSx2RWUHAAAAAABwKiQ7AAAAAACAU6EbCwAAAAAADmahG4tdUdkBAAAAAACcCskOAAAAAADgVEh2AAAAAAAAp8KYHQAAAAAAOJKRZBi0w56o7AAAAAAAAE6FZAcAAAAAAHAqdGMBAAAAAMDBePSsfVHZAQAAAAAAnArJDgAAAAAA4FToxgIAAAAAgKPRjcWuqOwAAAAAAABOhWQHAAAAAABwKnRjAQAAAADAgSziaSz2RmUHAAAAAABwKiQ7AAAAAACAU6EbCwAAAAAAjmRM8Q/shsoOAAAAAADgVEh2AAAAAAAAp0KyAwAAAAAAOBXG7AAAAAAAwMF49Kx9UdkBAAAAAACcCskOAAAAAADgVOjGAgAAAACAo9GNxa6o7AAAAAAAAE6FZAcAAAAAAHAqdGMBAAAAAMDBeBqLfVHZAQAAAAAAnArJDgAAAAAA4FToxgIAAAAAgCMZSVb6sdgTlR0AAAAAAMCpkOwAAAAAAABOhW4sAAAAAAA4Gr1Y7IrKDgAAAAAA4FSo7IDdtOuRpNvvPaKYWlla8n0lvTv0mlJtuj96UL0HH9SQB+tp4+ogSZKPf4EGv7xbDZqelDHShhVBGv9aLeVk8fYsb8bM2qm46zNVVGSRJKUe8dBDra9T18dS1O2xw7Z2Lq5G7h5G3W5ooNMn3R0VLv6kXc8U3X7f0eJzdH6o3n2ptiSpdv3T6vPEIdWomymrVdq8NkAT34jVyeMetmVj62RqwEv7FFsnU7k5rvryoyjNmV7FUZuC/7nUY1qvySl1H5SgGnUylXnaTf1ua+zIzcA5VKqSq8GvHlDc9RkqyHfR8oVB+mhkNV1zfYZen7KjRNuK3laNfKyWViwKdlC0sOZLu0Z6KG21qwrTLaoYZVXskwUKblGkrH0WbR9SQTmJxd8z+taxqtZLefKOLf4ad+PACkrf4Hp2XQWSV4xRk//mSJI29PdU1l4XWfOlilWsqvZYgUJbF13+jYSeG7JWDW44Lk/PQp1M89TXs2pp0ffVSrTp3meHevfbriHPNtfGDWGSJDf3Ig1++nc1b5ms3DxXzZ5VS//9qpYjNgG4ajjVf5NLlixRr169lJSU9JeXnTp1qj7++GMtX778H4js6pB2rIJmfRStG24+KQ/P0hfgylE5anHHcaUe8ygxvc8TB+TjV6h+d9wki0Ua+t5W9XrsoCaPqXG5Qsdf8MEr0Vo4K7TEtC8mROiLCRG2v3s9laxrm2SQ6Chn0o55aNaHUbqh+Ul5eFpt0339CrXgy8r6bXmgrEUWPfryPj09areGP3ytJMkvoECvT96qSW9W1/JFIXL3sCokLN9Rm4E/uNRjmpvtqh9nh2np/FB1HZDoqPBxAYNfPaBTqe7q2bShfPwK9can29Wu5xF9Ny1cHes3sbW7rkm6Rny0U+t/DXBcsJAplCpUNrrhk1x5hhulLnPV1ucqqPE3OfIINbr2nTx5RhjJKiXNctPW5z3V5JviZEaDiXkl1rWhn6cCG5+9l6r1Qp68Yo1c3KT0zS7a+LCnbpqXowqh1Lxfbl9+Fqf3xt6owgJXRUad1lvv/ap9ewO0d3egJKlyRKZatEpS6gnPEsv16rtDEVUy9UC3uxQYlKs33/1VCQf99Nu6yo7YDJRTFk5pu6IbC+xm5U+hWvVzqDLSy86hDRq2W/95p7oKCywlpleukqtVP4coJ8tN2ZluWrU4VFVrZF2OkPGPMLqtU6p++jrE0YHgT1b+GKJVi0OUcapkEmr9siAtXxSqnCw35eW6au7MCNW54bRt/n39krVheaCWzKukwgIX5WS5KXG/1+UOH2W41GO6e4uvfv4uTEcSPf+8SpQjYZF5WvZ9sAryXXTyhId++zVA0TWzS7WLv++4li8MVl6OaxlrweXi6iVVH1SgilWMLC5SSKsieVYxytjuInc/FU+3SMZIFhcpJ9FS5npyki06tcFFldsX2qb51C5OdEgqXkehlHek7OXxz0o46KfCguJzzcgiGYvCI87etw56cqP+M+laFRaW/DfrtjsP6fPp1ygz00OJCX5aNK+a4tscuqyxA1cbp6rsQPnV/I5jKsh30fplpctr530eobbdUrT0+0qSpJtvP67Vv1CGW1498K8k9XshSUn7PfXp2CravNqvxPxrG2cqILhAyxcEOihC/F3XNkxXwp6zyYy4+qd1cLe33v58oyKq5mrXZl998Fqsjh/mH+UrxZ+PKa4M304NV8t2J7R5jZ98/AvVsNUpTX83qkSbChWL1LxNqkYMiHNQlDiX/BNSziGLvGPPVl392sxLRdmSsUrVHisoc7kj37kp4AarKlYp+RXvpscq6ORqV1nzLQq6uVC+da1lLo9/3qCnflf8nYfk6VmkvbsDtG51cXVG81ZJKihw0fo14ZI22tr7+OQrOCRXB/b526bt3+evps1TLnPkwNXlslR2xMTE6M0331SdOnUUGBiofv36KTc3VydOnFC7du0UEBCgoKAgtWjRQlarVWPHjlWnTp1KrOOJJ57Qk08+KUlKS0tTv379FBERocDAQHXo0KFE23HjxqlSpUoKDw/XJ598Ypuenp6uPn36KDQ0VNHR0Ro5cqSs1rIvFCtXrlSjRo3k7++vRo0aaeXKlbZ5Bw4cUMuWLeXr66v4+Hg99thj6tWrlySpbdu2ev/990usq169evrvf/97yfvvSlfRq1B9nzqgj94su1vK3u2+cnO3ataKFZq1YoWsRRbNn8VYAOXRlLci1a9FPfVqUl8LPgvViCl7FF41t0Sb2zuf0PLvA5WbzTeMV6KYWlnqMShBU8ae7X8cUjlft3U4qo/eiFXfWxvrSJKnXhi3y4FR4q8o65jiyrB1na+ia+Zo9sa1mrFig/Zs8dHKH4NKtLn5zjSdPumuLWv8zrEWOIK1QNr2oqcqty+Ud/WzSYuWK7PVcmW2ag3Jl+81Zd+DHpnrpvB7C0tNrz8hTy1XZ6v+B7kKalokC/XZDvPBe9erc9t79dzjrbRyWYQKClxUsWKB+j60VR+9X79Ue8+KxcczK/NsFV52lrsqepU+zgDs57J9TM6cOVOLFi3Svn37tHv3bo0cOVLjxo1TZGSkjh8/rqNHj2rUqFGyWCzq1auXFi5cqFOnTkmSCgsLNWvWLPXp00eS1Lt3b2VnZ2vbtm06duyYnn76advrHDlyROnp6UpOTtaUKVP02GOP6eTJk5Kkxx9/XOnp6dq/f7+WLl2qadOmlUiGnJGWlqa2bdvqiSeeUGpqqp555hm1bdtWqampkqQePXqocePGSk1N1YgRIzR9+nTbsn379tWMGTNsf2/atEnJyclq27Ztmftl0qRJatiwoRo2bKh8k1tmmytdz8cO6ue5YTqWUrHM+S+9s03Jh7zUqXELdW7SXIcTPfX8WzvKbAvH2rXRRzlZrirId9FPs0O0fb2PGrVOt82v4Fmk5nen6cfZdGG5EoVXzdFrk7fqo1HVte23s98+5eW6aNVPIdqz1VcF+S76bEJV1bnhtLx8uEkr7851TFH+WSxGr/9nh1YuCtJ99ZqoS8OG8vEvVP9/JZRoF3/fcS3+NlQSXRrKC2OVtg+pIBd3o1pDSo9v5OolVelSqO1DKig/teS8UxtclH/CotA7yv58dXGXglsUKW2Vq47/wpcKjmS1WrR9a4hCQnPU9t796vnADv38Y7SOHfUu1TY3p7iY3sv7bDWPl3ehcrIpssefGFO+f64wly3ZMXjwYEVFRSkoKEhDhw7V559/Lnd3dx0+fFiHDh2Su7u7WrRoIYvFovDwcLVs2VJfffWVJGnhwoUKCQnRjTfeqMOHD2vBggWaOHGiAgMD5e7urlatWtlex93dXcOHD5e7u7vuvvtu+fj4aNeuXSoqKtKsWbP05ptvytfXVzExMXr22WdLJCrOmD9/vmrWrKnevXvLzc1N3bt3V1xcnObOnauEhAStW7dOr732mjw8PNS8eXO1b9/etmz79u21e/du7dmzR5I0ffp0de3aVR4eHqVeR5IeeeQRrV+/XuvXr5eHxTlLwuvfdFLteyZpxtIVmrF0hUIq5+mld7ar84PFN2zV4zK14MsI5eW4KjfbTd9/GaGGLVMvsFaUB0aWErfXzdqcUuYpN21e5euwmHBpKkXkatQnWzTrg6r6+buwEvMO7vIucX27Aq91V6XzHVOUf74BhQqrkq/vpldWQb6LMk6568evK6nRLSdtbULC81SvSboW/zf0PGvC5WSMtGO4h/JTLbr23Ty5nGOcbmOVrLlS3rGSt+KHv3NTaHyh3C7Q68wUWs455gcuL1dXo/CITNW/4Zjad9yrGbPnacbseQoJzdZLr6xR5267lJnpodQTnqoee/YLomqxp3ToIBVZwD/psiU7oqLO9jGNjo5WSkqKnn/+edWoUUN33HGHqlevrrfeesvW5o8VEjNmzFDv3r0lSYmJiQoKClJgYNnjAQQHB8vN7WyW1MvLS5mZmTpx4oQKCgoUHR1dIo7k5ORS60hJSSnR7o9tU1JSFBQUJC+vs1ehP26bp6enunbtqhkzZshqterzzz+3xe7sXFytcvcokouL5Oqi4t9drRrSv4EGdWikxzs11OOdGirtWAW9P6KW5n1e/PSO3Vv9dGenw/KoUCSPCkVqc/9hHdjl4+CtwZ95+xXqxpbpcq9glYur0a0dUnVd4wytX3r22+L4Tif00zch4hvG8qn4kcDFx6/4HC3+PbhSnt6cukVzZ0bo+y/CSy334zdhahqfqupxmXJ1s6r7ownaut5P2Zl8I+Vol3pMLZbi5dzcjCwqXs7Nnf7/5cnpk+46nFBBbXselYurkbdvoeI7HtOBnWfvP27rcFzbN/jqcIJzfllyJdr1uoeyD7io3vhcuf7hsKStdFHGDheZIqkwU9o71kNufkZe1c+ed0W50rFFbqr8py4sWfstSl3mqqLc4u4xR+a66tRvLgpsyDl7ufkH5KrlrYny9CyUi4vRDY2OqFXrRG3cUElDnm2hQf1u1+MPxevxh+KVllpR779zg+Z9GytJWvxDVXXrvVM+PvmKjDqtNm0P6qeF0Rd4RQB/x2W7U01MPPtou4SEBEVERMjX11fjxo3TuHHjtHXrVrVu3VqNGjXSbbfdpg4dOujRRx/V1q1bNW/ePI0ZM0ZScWIhLS1Np06dUkBAwEW/fkhIiNzd3XXo0CHVqVPHFkeVKqXHhoiIiNChQyVHR05ISFCbNm0UHh6utLQ0ZWdn2xIef9w2qThR07t3bzVv3lxeXl5q2rTpRcd5Jes+4JB6PnZ2v7Vuf1QzJ0Rr5gcl+4lbrVLmaTfl/q90771htTVwyF5N+3mVLBZp1xZfvTOEgdbKGzc3o77PJSkyNlfWIosS93nq1YdrKPlA8d1ccFi+GjQ7rfHDuHCXV90fTVDPwWdL4Fvfe0wzx1eVMVJ41Vz1fKzkOdzpxpslSZvWBOjTd6M14qNtquBp1fYNfhrzHOdoeXCpx/TaRukaPW2LbfqczSu0ea2/XuxT7/IFjwsa+VhtDRh2UPc/kixrkUWbVvtr0hsxtvm3dTiurz+OOPcKcFnlpFiU8pW7XDyMVtxyNilVe3hxhcfuNz2Ud9QiF0/J79oiNfgwV64Vzi5//GdXufkaBTYuncQ48KG7sp6rIIurVLGqVde+nSffOiQ7LjdjLGp7734NfuZ3uViMjh310kcT6mvNytLnodVqUWaGu3Jzi+93Z0yto8FP/66psxYoL89VX8+qxWNnUQqPnrUvizH/fEFyTEyMfH19tWDBAnl5eal9+/Zq2bKlmjVrpri4OMXGxiopKUmNGzfWZ599pltvvVWS9PDDD2vNmjUKCQnRzz//bFtf27Zt5e/vrwkTJsjHx0erVq1Sy5YttWTJEvXq1UtJSUklXvvjjz9WfHy8evXqpaysLE2bNk1paWm688479dxzz+mhhx7S1KlT9fHHH2v58uVKTU1VbGysPvjgA3Xp0kWzZ8/WgAEDtHfvXoWEhOimm25Sy5YtNXLkSP32229q06aN7rnnnhJjddSqVUuenp7q3Lmzhg8fflH7yd8tVE3977PTXkd5YM3kEbrOxKUi354C5ZkpZBwZZ3PLmuOODgF2tLRz6cE7ceVadXCq0nMOOzoMp+HrH6mGNz3u6DDOKyP1K61fv97RYVy0y9aNpUePHrbuKrGxsRo2bJj27Nmj+Ph4+fj4qGnTpho0aJAt0SEVV0hs2bKlVDeQ6dOny93dXXFxcapUqZLee++9i4rh/fffl7e3t6pXr67mzZurR48e6t+/f6l2wcHBmjdvnsaNG6fg4GCNGTNG8+bNU0hI8aCLM2fO1KpVqxQcHKxhw4apa9euqlChQol19OnTR1u2bLE9pQUAAAAAAFwel62y40x1xV+RkJCguLg4HTlyRH5+5XcAn65duyouLk6vvvqqbdq0adM0adIkLV++/KLXQ2WH86Gyw7lQ2QGUb1R2OB8qO5wLlR3OhcoO+/L1uwIqO9Ko7LALq9Wqd955R926dSt3iY5169Zp3759slqtWrhwoebMmaMOHTrY5mdnZ+uDDz7QI4884rggAQAAAAC4SpXLofSzsrIUFham6OhoLVy40NHhlHLkyBF17NhRqampioyM1Icffqjrr79ekrRo0SJ17NhR8fHx6tGjh4MjBQAAAADg6nNZkh0HDx78S+29vb2VmZn5zwRjB/fcc4/uueeeMufdeeedysqi6wIAAAAA4OJYJFn++REmrirlthsLAAAAAADApSDZAQAAAAAAnEq5HLMDAAAAAICritXRATgXKjsAAAAAAIBTIdkBAAAAAACcCskOAAAAAADgVBizAwAAAAAAB+PRs/ZFZQcAAAAAAHAqJDsAAAAAAIBToRsLAAAAAACOZP73A7uhsgMAAAAAADgVkh0AAAAAAMCp0I0FAAAAAACHMhJPY7ErKjsAAAAAAIBTIdkBAAAAAACcCt1YAAAAAABwMAu9WOyKyg4AAAAAAOBUSHYAAAAAAACnQjcWAAAAAAAcjaex2BWVHQAAAAAAwKmQ7AAAAAAAAE6FbiwAAAAAADiSkSxWRwfhXKjsAAAAAAAAf9stt9wiT09P+fj4yMfHR7Vr17bN++yzzxQdHS1vb2916NBBaWlptnlpaWm677775O3trejoaH322Wd/OxaSHQAAAAAAwC7Gjx+vzMxMZWZmateuXZKkbdu2acCAAZo+fbqOHj0qLy8vDRo0yLbMY489Jg8PDx09elQzZ87Uo48+qm3btv2tOOjGAgAAAAAA/jEzZ87UPffco5YtW0qSXn/9dV1zzTXKyMiQi4uLZs+era1bt8rHx0fNmzdX+/btNX36dL311luX/JpUdgAAAAAA4GjGlOuf48ePq2HDhrafSZMmlbkZL730kkJCQnTzzTdryZIlkoorO+rXr29rExsbKw8PD+3evVu7d++Wm5ubatWqZZtfv359KjsAAAAAAMA/KzQ0VOvXrz9vm9GjR6tOnTry8PDQrFmzdM8992jjxo3KzMyUv79/ibb+/v7KyMiQq6ur/Pz8ypz3d1DZAQAAAAAA/rYmTZrI19dXFSpUUN++fXXzzTfr+++/l4+Pj06fPl2i7enTp+Xr63veeX8HlR0AAAAAADiacXQA9mexWGSMUd26dbVp0ybb9P379ysvL0+1atWSi4uLCgsLtWfPHtWsWVOStGnTJtWtW/dvvTaVHQAAAAAA4G85deqUFi1apNzcXBUWFmrmzJn69ddf1aZNG/Xs2VNz587VsmXLlJWVpeHDh6tjx47y9fWVt7e3OnbsqOHDhysrK0srVqzQnDlz1Lt3778VD5UdAAAAAADgbykoKNCwYcO0c+dOubq6Ki4uTt9++61t4NGJEyeqZ8+eSk1NVXx8vD755BPbsh988IH69++vSpUqKTg4WB9++OHfruwg2QEAAAAAgINZzJXdjyU0NFTr1q075/wePXqoR48eZc4LCgrSt99+a9d46MYCAAAAAACcCskOAAAAAADgVOjGAgAAAACAo13h3VjKGyo7AAAAAACAUyHZAQAAAAAAnArdWAAAAAAAcCQjyeroIJwLlR0AAAAAAMCpkOwAAAAAAABOhWQHAAAAAABwKozZAQAAAACAA1lkZOHRs3ZFZQcAAAAAAHAqJDsAAAAAAIBToRsLAAAAAACORjcWu6KyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAEcr791YLI4O4K8h2VGeWCySG4fEmbj4+Tg6BNiRxc/X0SHAzgoPJjg6BNhRwe03OjoE2NnSjmmODgF2tOPFAEeHADvKfdXV0SEA50U3FgAAAAAA4FQoIwAAAAAAwJGMJKujg7iAK6yYh8oOAAAAAADgVEh2AAAAAAAAp0I3FgAAAAAAHMxS3p/GcoWhsgMAAAAAADgVkh0AAAAAAMCpkOwAAAAAAABOhTE7AAAAAABwNMbssCsqOwAAAAAAgFMh2QEAAAAAAJwK3VgAAAAAAHAoQzcWO6OyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAEcyohuLnVHZAQAAAAAAnArJDgAAAAAA4FToxgIAAAAAgKNZHR2Ac6GyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAAez8DQWu6KyAwAAAAAAOBWSHQAAAAAAwKnQjQUAAAAAAEejG4tdUdkBAAAAAACcCskOAAAAAADgVEh2AAAAAAAAp8KYHQAAAAAAOJKRZGXMDnuisgMAAAAAADgVkh0AAAAAAMCp0I0FAAAAAACHMjx61s6o7AAAAAAAAE6FZAcAAAAAAHAqdGMBAAAAAMDR6MZiV1R2AAAAAAAAp0KyAwAAAAAAOBW6sQAAAAAA4Gh0Y7ErKjsAAAAAAIBTIdkBAAAAAACcCt1YAAAAAABwJCPJSjcWe6KyAwAAAAAAOBWSHQAAAAAAwKmQ7AAAAAAAAE6FMTsAAAAAAHAoIxmro4NwKlR2AAAAAAAAp0KyAwAAAAAAOBW6sQAAAAAA4GiGR8/aE5UdAAAAAADAqZDsAAAAAAAAToVuLAAAAAAAOJKRZKUbiz1R2QEAAAAAAJwKyQ4AAAAAAOBU6MYCAAAAAICj8TQWu7oqkh133XWXunXrpr59+5633cGDB1WtWjUVFBTIze2q2DV21a5bom5vn6KYmplasqCy3h1eV5J0y92H9fjLO23tLBYjz4pWPdGtsfbu8NNrE35X3RtO2ea7uVuVfNBLgzo3vdybgD9p1z1Jt9975H/HNEzvDrtGklQpIkdTF61WTrarre3X/6mqzz+KKbG8j1+BJs9do6SDXnq+7w2XM3SUwc29SI89u0UNGh2Xj1++jiR7a+rEa/Tb6jDdckeSBj+/ydbW4iJ5ehbpyf4ttXdXgNzcizTgqa1q2vKw3NyMtm8O0oSx9ZR6oqIDtwh/5O5h1eBRSbq+RYZ8A4p0+JCH/vNmhNb/4qeqNXP1/P8dUnh0viRp75aK+uDlSCXs8XRw1CjLrU32qc+9v6tScJZOplfU6I9bqrDIRf3u+021Yk7IanXRpl2V9f6MpkpL95Ik9e2wQT3bbVRB4dnP5Ydevk+Hj/s5ajMg6bmh69XgxmPy9CzSybQK+vrzWlo0P0Zublb96+V1qln7lMLCs/XCk821ZWOobTlvn3wNeHyzGjY5Kkma/211zZx6jaM246oVsPiY/FackEdyjjIaB+nog9Vs8yx5RQr9Mkm+609KRUZ5kRWV9GKcJClw4RH5rTgh99R8Ffm66dStlXSyTWXbssH/TZbP76fkcThHae3ClXpvlcu+bcDV4Kr4j37BggWODuGqkHa8gmZNrqYbmqXKo4LVNn3J9+Fa8n247e/49inq/sgB7d3hK0ka/tj1Jdbz1sfrtWld0OUJGueVdqyCZk2K1g3N0uThaS01//5mzWUtOndvuP5P71PiAS9ZLP9klLhYrq5Gx4956oXHbtbxoxXVsOlRvfj6ej3W+1Yt+SFSS36ItLWNvztB3R7Yrb27/CVJ996/X3F1T2pwn1uUleWux/+1SQOf2aI3hjR21ObgT1xcjY6nuOv5TjV0LNlDjW87raETD2rgbbWVetRNIx+J0dEkD7m4SPc8cEIvfXBQj94e5+iw8Sc31k3WI/ev02sfttbO/aEK9s+WJFWPStP8pXEaMb6KiqwueqLXSv3roV/14rg2tmV/WVtdb066xUGRoyxfzqyl98Zcr8ICV0VWzdBb7y3Tvj3+OrjfX9u2BOvbr2M15NW1pZZ7ZPAWVfAsUr+ud8o/ME9vvrNCx4566ccF0Q7YiqtXYYC7UtuFy3vbaVnyS94HhU07JBUZHRxZV0XebqqQkH12pjE68lA15UV6yf14niLH7VZhoIcymhTf3xZUqqDj90cqYMnxy7k5wFWHMTtgNysXV9KqXyop45T7edvd1v6wFs8Nl1T6P+BKETmqe8Op/82Ho61cHKpVP4cqI/38x7Qs19RPV3TNLP34LceyvMjLddNn/4nTsSNeMsaidSsr62iKl2rEnSrV9ra7ErV4QZTOnKdhEdnasDZUp056qiDfVcsWV1HVahmXdwNwXnk5rprxTriOJlWQMRat+clfRxI8VLNejrJOu+loUgVJFskiWYukiGp5jg4ZZXigwwZN++567dhXScZYdOKUt06c8tbaLVFauq6asnM9lJfvpm8X19G1NY45OlxcQMJBPxUWFFfbnKlOD6+SpcJCF835uoa2bwmRtaj0/VDjpkf09ee1lJfnpmNHvLXo+2jdfvehyxk6JGXeGKisGwJV5F3y+2H3wzny3nhKx/rGqMjXXXKxKC/G2zb/5F3hyov2llwtKqjsqczrA+S5N9M2//TNIcq+zl9WT/4Vw58YU75/rjDl9gwbPXq0qlSpIl9fX9WuXVuLFy9WXl6ennrqKUVERCgiIkJPPfWU8vLO3qzNmTNHDRo0kJ+fn2JjY7Vw4UJJ0i233KKPP/5YkmS1WjVy5EhFR0erUqVK6tOnj9LT08uMISUlRe3bt1dQUJBq1KihyZMn2+bl5OSob9++CgwM1DXXXKMxY8YoMrL4W9GxY8eqU6dOJdb1xBNP6Mknn7TrProSVQrP0bU3nNTieWX/A3zbPYe1bUOAjqVQGn8lmLpolab9tFJPv75DfgH5tukuLkaPDtmtD0fVuhI/F68aAYG5qhKVpYT9viWmh4Zlq279VP288Gylxw/zolXnujQFheSqQoVC3XJHktavDrvcIeMvCAgpUGT1PB3adbaryuztmzVv/yYNGpmsWe9z/MobF4tVtaqdUIBvrqaP/lJfvPO5nui1Uh7uhaXa1qt9RAdTAkpMa9ogQd+On67/vDFb7W/dcZmixoUMenqjvln0nSbP+ElpqZ5at7ryhReSZJH5w+9SdLXT/1CE+Ks8D2SpMLiCguckK/bJjYoevk0+60+W3dgYVdydofwqdBsELrdymezYtWuXxo8fr3Xr1ikjI0OLFi1STEyM3njjDa1evVobN27Upk2btHbtWo0cOVKStHbtWvXp00djx47VqVOn9OuvvyomJqbUuqdOnaqpU6fql19+0f79+5WZmanBgweXGUe3bt0UGRmplJQUff311xoyZIh+/vlnSdKrr76qgwcPav/+/frxxx81Y8YM23K9evXSwoULderUKUlSYWGhZs2apT59+th3R12BziQzjiaXncy4rd1h/fRdxGWOCn/V6ZPuerLrjXrgzqZ6omtDVfQq0vNvbbfNb98zSbu2+Gnvdt/zrAWO5Opq1fOvbNDiBVFKSih5nG67K1HbNgXr6OGz31KlJHrr+LGKmj7nB331wwJFxWTo8//Uutxh4yK5uhm9OP6Qfvw6SIn7zt5gd6pTT/fFXacJwyK1bytJ5fIm0D9H7m5WtWx4UE+OaqeHh9+nGtGp6tV+Y4l21SPT1Lv97/roi7PdyJasraZ+Qzqp4+M9Ne6T5up97+9q3WTfZd4ClOWDdxuo81336LnBLbTy1wgV5F/49vu3tWG6v+duVaxYoPAqmbrj7kPyrFB0GaLFxXA/WaAKyTmyVnTVvnH1dKxnVVX+zwF5pOSUahs8J0UWU1zNAeDyKpfJDldXV+Xl5Wn79u0qKChQTEyMYmNjNXPmTA0fPlyVKlVSaGioXnnlFU2fPl2SNGXKFPXv31+33367XFxcVKVKFcXFle6LPHPmTD3zzDOqXr26fHx89Oabb2rWrFkqLCz5rUliYqJWrFih0aNHy9PTUw0aNNBDDz2kadOmSZK+/PJLDRkyRIGBgYqMjNQTTzxhWzY8PFwtW7bUV199JUlauHChQkJCdOONN5aKZ9KkSWrYsKEaNmyofGvpD0hn07rdYS2eW3Yyo871pxQYkq/lP1a6zFHhr8rNcdOe7X6yFrnoVKqHPhxVUzfefFIVvQoVFJqn9j2S9Om/qzs6TJyDxWL07PANKih00YfvXFdqfus2Sf/rwnLWoGc3y93dqq5t2qhj/N1auTRcr41bfblCxl9gsRj969+HVJBv0YShkaXm5+W4av60YD3/fwnyDy5wQIQ4l7z84lL5//5UR2npXjqd6amvFl2rJvUSbW0iKp3WW88u0oTPbtKW3WcrBA6lBCr1lLesxkXb9obpmx/rqmWjg5d7E3AOVqtF27eEKCQ0R207HLhg+4n/rqf8PFdNnvmjhr+xWksXR+rEcRKU5YXV3SLjalFquwjJzUU5tX2VHecrr20lq28CFh+T36pUJT9ZU8a9XP7bhXKlHHRToRvLP69GjRp67733NGLECFWqVEndunVTSkqKUlJSFB19dmCm6OhopaSkSCpOTsTGxl5w3WWto7CwUEePHi3VLigoSL6+viXaJicn2+ZHRZ39Z+CPv0tS3759bdUeM2bMUO/evcuM55FHHtH69eu1fv16ebg490WsToNTCq6Ud85kRvw9KVq5OFS5OVfFuLlOxfxvXAeLi1T7utMKCs3XxDlrNeOXFRrwwh7Vuu60ZvyyQi4uV96HpPMxevKljQoMytOoIY1U9KcBZq+5LlXBIblasaRkUrJazdNa/H2UMjM8VFjgqrlfV1ftuqfk58+4D+WL0TPjEhUYWqjXH6mmosKyRwe2uEgVPK0KqUyyozzJzK6gY6neJe8nzdljGBacobf/tUDTv2ugH1fWPO+6jCnZDQLlg6urUXhE1gXbZWZ4aOzIRurV8W49+kC8LC5Gu3YGXoYIcTHyIr0u2MZv2QkFLjispOdqqzDI4zJEBeDPymWyQ5J69Oih5cuX69ChQ7JYLHrhhRcUERGhQ4fODs6UkJCgiIjiG/KoqCjt23fhcs2y1uHm5qawsLBS7dLS0pSRkVGibZUqxY+GCg8PV1JSkm1eYmJiieU7dOigzZs3a+vWrZo3b5569uz5F7b+yuTiapW7R5FcXIsv5sW/nx25+rZ7DmvFT5WUk106meFRoUgt7jhKF5ZyxnZMXYxcXc4e09rXpatKTLYsFiNf/wINfHGPNq0NUHamm9YtC1a/O2/S450b6vHODTVjQjXt3+Gjxzs3lNXKY1kc7bHnNysqJlOv/quJ8vNdS82PvytJK5aElzpP9+wIUOu7kuTlXSBXV6vadjygE8c9dTq9wuUKHRfhibeSFFUzV8P7VlN+7tlL/A0tMhRbN1suLkZePkUa8EqyMtNdlbCXPuTlzcLlNXVf/HYF+ObIxytPne7YqtWbqiokIEvjXligb3+qo7m/lH4EabPrD8nHK0+SUVy14+p4+3at+J0ndziSf0CeWrZOkmfFQrm4GN3Q6Kha3Zakjb8VP2LWzb1I7h7FXVPc3Kz/+704QVU5IlO+fnlycTFq2OSI2rQ7qFnTajtqU65eRUaWAqssxshiLf5dRUY5tXxUEOyhoO8PS0VGnnsy5LUzQ1nXFj/q2Xd1qkK+SVLyM7VUEFrGdbLQWrwuI6lIxb9bSU4C9lYuv0LftWuXkpOTdfPNN8vT01MVK1ZUUVGRunfvrpEjR6pRo0ayWCx67bXX1KtXL0nSgw8+qDvuuEPt2rXTrbfeqsOHDysjI6NUV5bu3btr9OjRuuuuuxQaGqohQ4aoa9eucnMruSuioqLUrFkzvfTSS3r77be1e/duTZkyRTNnzpQkdenSRW+++aYaNWqk7OxsjR8/vsTynp6e6ty5s3r06KHGjRuratWq/+AeKx+6P3xAPR89W5rZut0RzfywmmZOjJW7R3Ey441n65W5bNNbjysrw12b1vKtRXnS/ZFD6jnooO3v1vcc1cwPYpR00Et9n9iugKB8ZWe56fdVgRrzrzqSpMICF51MPXthz8p0U2FhyWlwjNCwbN3d4ZDy81w047tFtunjx9bXkh8i5e5RpOatkzVqaKNSy04ZX1cDnt6iyV8slpubVYf2++mNl0q3g+NUqpKvtr1TlZ9r0ayN22zT/++FSBUWuGjQyCSFhBcoL9eiXRu9NbRXrAryyu13Hlet6d9dL3+fXE0b/bXyC1y1ZG01zZhbX93v3qyIShnq22GD+nbYYGvfdmBfSVLrJvv1/IPL5OFWpOMnvfX5/Hr6YcX5qz/wzzJGanvvfg1+ZqNcXIyOHfXSR+Ov05qVxYO0T57+k8LCix9X+sa4lZKkB7reoWNHvFWz9ik9MniLvH0KlJzoo7EjGyrhoJ/DtuVqFTwvRcHfHbb97bc6Tantw5V6bxWlDK6hsKkHFfT9ERUEe+jIQ9VUEF5cpR3y32S5ZhWp6sizAwWfvilYx/oUJyDDPj0k/5WpZ19n/mEd6Rej080Z1wOwJ4sx5a/zzebNm/XQQw9px44dcnd3V7NmzTRp0iQFBQXpX//6l20sjPvvv19jxoyRp2fxN1P//e9/9corr+jAgQMKCwvThAkTdOedd+qWW25Rr1699NBDD9mexjJ58mTl5ubqzjvv1Pvvv6/AwEAdPHhQ1apVU0FBgdzc3JSUlKSBAwdq5cqVCgwM1PPPP6+BAwdKkrKysjRw4EDNnTtX4eHh6tmzpz755JMS1SXLly9XixYt9J///Ef9+vW74Hb7u1dS06DO/8AehcNYGUzMmVj8GHDV2RQeTHB0CLCjgttLj42FK5vngTRHhwA72vFikKNDgB0defV95R1MunBDXBR/90pqFnK/o8M4r+NV1mj9+vWODuOilctkx5Xoww8/1KxZs7R06VLbtISEBMXFxenIkSPy87twNp5khxMi2eFUSHY4H5IdzoVkh/Mh2eFcSHY4F5Id9kWyw/6oX71Ehw8f1ooVK2S1WrVr1y6NGzdO9913n22+1WrVO++8o27dul1UogMAAAAAANhHuRyz40qQn5+vAQMG6MCBAwoICFC3bt00aNAgScVdXMLCwhQdHa2FCxc6OFIAAAAAQLlHpwu7ItlxiaKjo7V169Yy53l7eyszM/MyRwQAAAAAACS6sQAAAAAAACdDZQcAAAAAAI5GNxa7orIDAAAAAAA4FZIdAAAAAADAqdCNBQAAAAAAhzKSlW4s9kRlBwAAAAAAcCokOwAAAAAAgFOhGwsAAAAAAI5kJGOsjo7CqVDZAQAAAAAAnArJDgAAAAAA4FRIdgAAAAAAAKfCmB0AAAAAADgaj561Kyo7AAAAAACAUyHZAQAAAAAAnArdWAAAAAAAcDRDNxZ7orIDAAAAAAA4FZIdAAAAAADAqdCNBQAAAAAARzJGslodHYVTobIDAAAAAAA4FZIdAAAAAADAqdCNBQAAAAAAR+NpLHZFZQcAAAAAAHAqJDsAAAAAAIBToRsLAAAAAAAOZngai11R2QEAAAAAAJwKyQ4AAAAAAOBUSHYAAAAAAACnwpgdAAAAAAA4lOHRs3ZGZQcAAAAAAHAqJDsAAAAAAIBToRsLAAAAAACOZCRZ6cZiT1R2AAAAAAAAp0KyAwAAAAAAOBW6sQAAAAAA4GjG6ugInAqVHQAAAAAAwKmQ7AAAAAAAAE6FbiwAAAAAADiQkWR4GotdUdkBAAAAAACcCskOAAAAAADgVOjGAgAAAACAIxnD01jsjMoOAAAAAADgVEh2AAAAAAAAp0I3FgAAAAAAHIynsdgXlR0AAAAAAMCpkOwAAAAAAABOhWQHAAAAAABwKozZAQAAAACAo/HoWbuisgMAAAAAADgVkh0AAAAAAMCp0I2lHHH3t+pE1FpHh/GPO378uEJDQx0dBuyIY+pcrqrjGejoAC6Pq+aYntjv6Agui6vmeErK9HV0BJfH1XJMQyc4OoLL42o5nlmZuY4Owak0u7OxTpw44OgwziskJMTRIfwlFmMMD/PFZdWwYUOtX7/e0WHAjjimzoXj6Xw4ps6F4+l8OKbOheMJlA90YwEAAAAAAE6FZAcAAAAAAHAqJDtw2T3yyCOODgF2xjF1LhxP58MxdS4cT+fDMXUuHE+gfGDMDgAAAAAA4FSo7AAAAAAAAE6FZAcAAAAAAHAqJDtQQkxMjH766Se7rW/ZsmWqXbu23dYH55OQkCAfHx8VFRU5OhT8A+z9mYILGzFihHr16iXp4s8vPqvLJ4vFor1790qSBg4cqNdff/2cbUeNGqWHHnrocoWGS7BkyRJFRkZe0rJTp05V8+bN7RwR/o677rpLn3766QXbHTx4UBaLRYWFhZchKgB/5OboAODcWrRooV27djk6DJRjVatWVWZmpqPDAJzSxZ5ffFaXfxMnTrT9vmTJEvXq1UtJSUm2aUOGDHFEWMBVa8GCBY4OAcAFUNkBACgT30IBAADgSkWyA6WsW7dOderUUWBgoPr166fc3Nwyyyf/WF77/fffq06dOvL19VWVKlX09ttvSypdshkTE6O3335b9erVk7+/v7p27arc3Fzb/Hnz5qlBgwYKCAhQs2bNtHnzZtu80aNHq0qVKvL19VXt2rW1ePFiSdLatWvVsGFD+fn5KSwsTM8888w/tm/Ks5SUFHXq1EmhoaGqVq2a/v3vf0uSioqKNGrUKMXGxsrX11c33nijEhMTJUkrV65Uo0aN5O/vr0aNGmnlypW29d1yyy16+eWXdfPNN8vX11d33HGHTpw4YZv/3XffqW7dugoICNAtt9yiHTt22ObFxMRo7Nixqlevnry9vfXggw/q6NGjuuuuu+Tr66v4+HidPHlSUunyzrS0NPXr108REREKDAxUhw4dJEknTpxQu3btFBAQoKCgILVo0UJWq/Uf3aeOtmHDBl1//fXy9fXV/fffr65du2rYsGGSzn+u7NixQ7fccosCAgJUt25dfffdd7Z5qampuueee+Tn56dGjRpp2LBhJc5ti8WiCRMmqGbNmqpZs6Yk6cknn1RUVJT8/Px04403atmyZbb2I0aMUOfOndW1a1f5+vrqhhtu0KZNm0psx8aNG8s856+99lrNnTvX1q6goEAhISH6/fff7bgXy6+/cp6sXr1azZo1U0BAgOrXr68lS5bY1nPgwAG1atVKvr6+uv3220ucpxd7ftnzsxqlxcTE6M033yx1bZWkyZMnq0aNGgoKClL79u2VkpJS5joeeOABDRs2TFlZWbrrrruUkpIiHx8f+fj4KCUlpUT3JUlavny57T0TFRWlqVOnSjr39fpqdq7jc67rztixY9WpU6cS63jiiSf05JNPSjr3eXbGuHHjVKlSJYWHh+uTTz6xTU9PT1efPn0UGhqq6OhojRw58pzXufNdvw8cOKCWLVvaPkcee+wx23ujbdu2ev/990usq169evrvf/97yfvvSlLWvWReXp6eeuopRUREKCIiQk899ZTy8vJsy8yZM0cNGjSQn5+fYmNjtXDhQknF90kff/yxJMlqtWrkyJGKjo5WpUqV1KdPH6Wnp5cZQ0pKitq3b6+goCDVqFFDkydPts3LyclR3759FRgYqGuuuUZjxoyxfTZf6H0HoAwG+IPo6GhTt25dk5CQYFJTU02zZs3M0KFDzSeffGJuvvnmEm0lmT179hhjjKlcubL59ddfjTHGpKWlmd9++80YY8wvv/xiqlSpUmL9jRo1MsnJySY1NdXExcWZDz/80BhjzIYNG0xoaKhZvXq1KSwsNFOnTjXR0dEmNzfX7Ny500RGRprk5GRjjDEHDhwwe/fuNcYYc9NNN5lp06YZY4zJyMgwq1at+gf3UPlUVFRkbrjhBvPqq6+avLw8s2/fPlOtWjWzcOFCM2bMGHPttdeanTt3GqvVajZu3GhOnDhhUlNTTUBAgJk2bZopKCgwn332mQkICDAnTpwwxhjTqlUrU716dbNr1y6TnZ1tWrVqZV544QVjjDG7du0yXl5e5ocffjD5+flm9OjRJjY21uTl5Rljio9zkyZNzJEjR0xSUpIJDQ01119/vdmwYYPJyckxt956qxkxYoQxpvhYSjIFBQXGGGPuvvtu06VLF5OWlmby8/PNkiVLjDHGvPjii2bAgAEmPz/f5Ofnm19//dVYrdbLvasvm7y8PFO1alXz3nvvmfz8fDN79mzj7u5uhg4det5zJT8/38TGxpo33njD5OXlmcWLFxsfHx+zc+dOY4wxXbt2NV27djVZWVlm27ZtJjIyssS5LcnEx8eb1NRUk52dbYwxZvr06ebEiROmoKDAvP322yYsLMzk5OQYY4x55ZVXjJubm/nqq69Mfn6+GTt2rImJiTH5+fnGmPOf86NHjzZdunSxvfa3335rrr322suyf8uDiz1PkpKSTFBQkJk/f74pKioyP/zwgwkKCjLHjh0zxhR/Bj799NMmNzfXLF261Pj4+JiePXsaYy7+/LLXZzXKdq5r6+LFi01wcLD57bffTG5urhk8eLBp0aKFbbk/Xmf79u1rhg4daowpfbyMKT4Xzxz3gwcPGh8fH/PZZ5+Z/Px8c+LECfP7778bY859vb6anev4nOu6k5KSYry8vMzJkyeNMcYUFBSY0NBQs379emPM+c8zV1dX8/LLL5v8/Hwzf/58U7FiRZOWlmaMMaZ3796mffv25vTp0+bAgQOmZs2a5uOPPzbGmBL3YRe6ft90003m2WefNXl5eWbZsmXG19fX9t744osvTOPGjW3bvnHjRhMUFGS7fjuzc91Lvvzyy6ZJkybm6NGj5tixY6Zp06Zm2LBhxhhj1qxZY/z8/MwPP/xgioqKTFJSktmxY4cxpvg+afLkycYYY6ZMmWJiY2PNvn37TEZGhrnvvvtMr169bK/zx8/hFi1amEcffdTk5OSY33//3YSEhJjFixcbY4x54YUXTMuWLU1aWppJTEw01113ne1cv9D7DkBpJDtQQnR0tO2G1hhj5s+fb6pXr37BZEdUVJSZOHGiSU9PL9GmrBvo6dOn2/5+/vnnzYABA4wxxgwcONB2cTmjVq1aZsmSJWbPnj0mNDTU/Pjjj7Z/os5o0aKFGT58uDl+/Pjf2PIr2+rVq01UVFSJaaNGjTIPPPCAqVWrlvn2229LLTNt2jTTqFGjEtNuuukm88knnxhjii/ir7/+um3ehAkTzJ133mmMMea1114z999/v21eUVGRiYiIML/88osxpvg4z5gxwza/Y8eOZuDAgba///3vf5t7773XGFPyJiAlJcVYLBbbjd8fvfzyy6Z9+/a295yzW7p0qYmIiCiR0Ln55pvN0KFDz3uu/PrrryYsLMwUFRXZ5nXr1s288sorprCw0Li5udkSH8YYM3To0FLJjjM3XecSEBBgNm7caIwp/gerSZMmtnlFRUUl/pk63zmfnJxsfHx8bJ8bnTp1MqNHj764HeQELvY8eeutt2w3zWfccccdZurUqebQoUPG1dXVZGZm2uZ17969zGTH+c4ve31Wo2znurb279/fPP/887bpGRkZxs3NzRw4cMAYc+nJjlGjRpkOHTqUGcu5rtdXs3Mdn/Ndd9q0aWMmTZpkjDFm7ty55pprrjHGmAueZ56enrZ/eo0xJjQ01KxatcoUFhYad3d3s23bNtu8iRMnmlatWhljSiY7znf9PvOZkJWVZZvXs2dP23sjJyfHBAQEmN27dxtjjHn22WfNo48+evE76wp2rnvJ6tWrm/nz59v+XrhwoYmOjjbGGPPII4+Yp556qsz1/THZ0bp1azNhwgTbvJ07dxo3NzdTUFBQ4nM4ISHBuLi4mNOnT9vavvjii6Zv377GGGP7ouqMyZMnlzjXz/W+A1A2urGglKioKNvv0dHR5yyp/aPZs2fr+++/V3R0tFq1aqVVq1ads23lypVtv3t5edkGzzt06JDGjRungIAA209iYqJSUlJUo0YNvffeexoxYoQqVaqkbt262eKaMmWKdu/erbi4ODVq1Ejz5s271E2/Yh06dEgpKSkl9t2oUaN09OhRJSYmKjY2ttQyKSkpio6OLjEtOjpaycnJtr/Pdaz+vKyLi4uioqJKLBsWFmb7vWLFiqX+LmvQxMTERAUFBSkwMLDUvOeff141atTQHXfcoerVq+utt9467z650qWkpKhKlSqyWCy2aWfOzfOdKykpKYqKipKLy9mP9zPH9fjx4yosLCxxjv/x93NNe/vtt3XNNdfI399fAQEBSk9PL9FV4o/tXVxcFBkZWeJz41zvo4iICN18882aPXu2Tp06pQULFqhnz55/eV9dyS7mPDl06JC++uqrEsd7+fLlOnz4sFJSUhQYGChvb2/bcn8+r8843/lVlkv5rMa5lXVt/fNnqY+Pj4KDg0t8ll6Kc33uS3/ten01Kev4nO+607dvX82YMUOSNGPGDPXu3VvShc+z4OBgubmdfT7AmXPrxIkTKigoKPF++PM1+YzzXb9TUlIUFBQkLy+vMrfN09NTXbt21YwZM2S1WvX555/bYnd257qX/PP+/OO97/nOpT8qax2FhYU6evRoqXZBQUHy9fUt0fbMcT5zDT/jz9fjc73vAJSNZAdKOTOeg1T82MKIiAh5e3srOzvbNv3IkSMllmnUqJHmzJmjY8eOqUOHDurSpctfft2oqCgNHTpUp06dsv1kZ2ere/fukqQePXpo+fLlOnTokCwWi1544QVJUs2aNfX555/r2LFjeuGFF9S5c2dlZWVdyqZfsaKiolStWrUS+y4jI0Pff/+9oqKitG/fvlLLRERE6NChQyWmJSQkqEqVKhd8vT8va4xRYmLiRS17oe1IS0vTqVOnSs3z9fXVuHHjtH//fn333Xd65513bOO2OKPw8HAlJyfLGGObdubcPN+5EhERocTExBL9vM8c19DQULm5uZV4gsMfz/cz/phgWbZsmcaMGaMvv/xSJ0+e1KlTp+Tv719mXFJxv+WkpCRFRERc1HaeuXH76quv1LRp07/9HnJGUVFR6t27d4njnZWVpRdffFHh4eE6efJkic+8hISEc67nXOfXX43nfJ/VKFtZ19Y/f5ZmZWUpNTX1gufBH8/Rspzrc1+yz/XaGZV1fM533enQoYM2b96srVu3at68ebZE7aWeZyEhIXJ3dy/xfjjXNfl81+/w8HClpaWVuGf78+d83759NXPmTC1evFheXl5q2rTpX4r1SlbWveSf9+eZ4y+d/1z6o7LW4ebmViKBfaZdWlqaMjIySrQ9c5zDw8PPe40+1/sOQNlIdqCUCRMmKCkpSWlpaXrjjTfUtWtX1a9fX9u2bdPGjRuVm5urESNG2Nrn5+dr5syZSk9Pl7u7u/z8/Ep8q3yxHn74YU2cOFFr1qyRMUZZWVmaP3++MjIytGvXLv3888/Ky8uTp6enKlasaHuNGTNm6Pjx43JxcVFAQIAkXdLrX8kaN24sX19fjR49Wjk5OSoqKtLWrVu1bt06P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<p>Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about <strong>5% better than SciBERT's</strong> 0.6571!</p>
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<p>You might wonder that <em>"this is great, but besides some utility functions (<code>clean</code>, <code>simple_parallel_map</code>, ...) what more value does GreatAI add?"</em>. This would be a valid argument because the scope of GreatAI actually only starts here.</p>
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<blockquote><p>Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering.</p>
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<p>In order to use this model in production, we have to make it available on some possibly shared infrastructure.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<span class="notice" hidden>Copied!</span>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">save_model</span>
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<span class="n">save_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s2">"my-domain-predictor"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-8" class="clipboard-copy-txt">from great_ai import save_model
|
||
|
||
save_model(model, key="my-domain-predictor")</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
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<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
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</div>
|
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<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre><span style="color: rgb(255,255,0)">Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(255,255,0)">The selected tracing database (ParallelTinyDbDriver) is not recommended for production</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)">GreatAI (v0.1.4): configured ✅</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 tracing_database: ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 large_file_implementation: LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 is_production: False</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 should_log_exception_stack: True</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 prediction_cache_size: 512</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 dashboard_table_size: 50</span>
|
||
<span style="color: rgb(255,255,0)">You still need to check whether you follow all best practices before trusting your deployment.</span>
|
||
<span style="color: rgb(255,255,0)">> Find out more at https://se-ml.github.io/practices</span>
|
||
<span style="color: rgb(0,175,255)">Fetching cached versions of my-domain-predictor</span>
|
||
<span style="color: rgb(0,175,255)">Copying file for my-domain-predictor-9</span>
|
||
<span style="color: rgb(0,175,255)">Compressing my-domain-predictor-9</span>
|
||
<span style="color: rgb(0,175,255)">Model my-domain-predictor uploaded with version 9</span>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
|
||
|
||
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
|
||
<pre>'my-domain-predictor:9'</pre>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
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|
||
|
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|
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|
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|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
|
||
<h3 id="lets-continue-by-finishing-the-deployment-in-the-next-notebook"><a href="/tutorial/deploy">Let's continue by finishing the deployment in the next notebook.</a><a class="anchor-link" href="#lets-continue-by-finishing-the-deployment-in-the-next-notebook">¶</a></h3>
|
||
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|
||
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|
||
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|
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|
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Last update:
|
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<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 15, 2022</span>
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|
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|
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|
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