Your IP : 18.190.253.57
<!DOCTYPE html>
<html class="no-js" lang="nl-NL">
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title></title>
<style>
#wpadminbar #wp-admin-bar-vtrts_free_top_button .ab-icon:before {
content: "\f185";
color: #1DAE22;
top: 3px;
}
</style>
<style>img:is([sizes="auto" i], [sizes^="auto," i]) { contain-intrinsic-size: 3000px 1500px }</style>
<style id="classic-theme-styles-inline-css" type="text/css">
/*! This file is auto-generated */
.wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc( + 2px);font-size:}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none}
</style>
<style id="global-styles-inline-css" type="text/css">
:root{--wp--preset--aspect-ratio--square: 1;--wp--preset--aspect-ratio--4-3: 4/3;--wp--preset--aspect-ratio--3-4: 3/4;--wp--preset--aspect-ratio--3-2: 3/2;--wp--preset--aspect-ratio--2-3: 2/3;--wp--preset--aspect-ratio--16-9: 16/9;--wp--preset--aspect-ratio--9-16: 9/16;--wp--preset--color--black: #000000;--wp--preset--color--cyan-bluish-gray: #abb8c3;--wp--preset--color--white: #ffffff;--wp--preset--color--pale-pink: #f78da7;--wp--preset--color--vivid-red: #cf2e2e;--wp--preset--color--luminous-vivid-orange: #ff6900;--wp--preset--color--luminous-vivid-amber: #fcb900;--wp--preset--color--light-green-cyan: #7bdcb5;--wp--preset--color--vivid-green-cyan: #00d084;--wp--preset--color--pale-cyan-blue: #8ed1fc;--wp--preset--color--vivid-cyan-blue: #0693e3;--wp--preset--color--vivid-purple: #9b51e0;--wp--preset--gradient--vivid-cyan-blue-to-vivid-purple: linear-gradient(135deg,rgba(6,147,227,1) 0%,rgb(155,81,224) 100%);--wp--preset--gradient--light-green-cyan-to-vivid-green-cyan: linear-gradient(135deg,rgb(122,220,180) 0%,rgb(0,208,130) 100%);--wp--preset--gradient--luminous-vivid-amber-to-luminous-vivid-orange: linear-gradient(135deg,rgba(252,185,0,1) 0%,rgba(255,105,0,1) 100%);--wp--preset--gradient--luminous-vivid-orange-to-vivid-red: linear-gradient(135deg,rgba(255,105,0,1) 0%,rgb(207,46,46) 100%);--wp--preset--gradient--very-light-gray-to-cyan-bluish-gray: linear-gradient(135deg,rgb(238,238,238) 0%,rgb(169,184,195) 100%);--wp--preset--gradient--cool-to-warm-spectrum: linear-gradient(135deg,rgb(74,234,220) 0%,rgb(151,120,209) 20%,rgb(207,42,186) 40%,rgb(238,44,130) 60%,rgb(251,105,98) 80%,rgb(254,248,76) 100%);--wp--preset--gradient--blush-light-purple: linear-gradient(135deg,rgb(255,206,236) 0%,rgb(152,150,240) 100%);--wp--preset--gradient--blush-bordeaux: linear-gradient(135deg,rgb(254,205,165) 0%,rgb(254,45,45) 50%,rgb(107,0,62) 100%);--wp--preset--gradient--luminous-dusk: linear-gradient(135deg,rgb(255,203,112) 0%,rgb(199,81,192) 50%,rgb(65,88,208) 100%);--wp--preset--gradient--pale-ocean: linear-gradient(135deg,rgb(255,245,203) 0%,rgb(182,227,212) 50%,rgb(51,167,181) 100%);--wp--preset--gradient--electric-grass: linear-gradient(135deg,rgb(202,248,128) 0%,rgb(113,206,126) 100%);--wp--preset--gradient--midnight: linear-gradient(135deg,rgb(2,3,129) 0%,rgb(40,116,252) 100%);--wp--preset--font-size--small: 13px;--wp--preset--font-size--medium: 20px;--wp--preset--font-size--large: 36px;--wp--preset--font-size--x-large: 42px;--wp--preset--spacing--20: ;--wp--preset--spacing--30: ;--wp--preset--spacing--40: 1rem;--wp--preset--spacing--50: ;--wp--preset--spacing--60: ;--wp--preset--spacing--70: ;--wp--preset--spacing--80: ;--wp--preset--shadow--natural: 6px 6px 9px rgba(0, 0, 0, 0.2);--wp--preset--shadow--deep: 12px 12px 50px rgba(0, 0, 0, 0.4);--wp--preset--shadow--sharp: 6px 6px 0px rgba(0, 0, 0, 0.2);--wp--preset--shadow--outlined: 6px 6px 0px -3px rgba(255, 255, 255, 1), 6px 6px rgba(0, 0, 0, 1);--wp--preset--shadow--crisp: 6px 6px 0px rgba(0, 0, 0, 1);}:where(.is-layout-flex){gap: ;}:where(.is-layout-grid){gap: ;}body .is-layout-flex{display: flex;}.is-layout-flex{flex-wrap: wrap;align-items: center;}.is-layout-flex > :is(*, div){margin: 0;}body .is-layout-grid{display: grid;}.is-layout-grid > :is(*, div){margin: 0;}:where(.){gap: 2em;}:where(.){gap: 2em;}:where(.){gap: ;}:where(.){gap: ;}.has-black-color{color: var(--wp--preset--color--black) !important;}.has-cyan-bluish-gray-color{color: var(--wp--preset--color--cyan-bluish-gray) !important;}.has-white-color{color: var(--wp--preset--color--white) !important;}.has-pale-pink-color{color: var(--wp--preset--color--pale-pink) !important;}.has-vivid-red-color{color: var(--wp--preset--color--vivid-red) !important;}.has-luminous-vivid-orange-color{color: var(--wp--preset--color--luminous-vivid-orange) !important;}.has-luminous-vivid-amber-color{color: var(--wp--preset--color--luminous-vivid-amber) !important;}.has-light-green-cyan-color{color: var(--wp--preset--color--light-green-cyan) !important;}.has-vivid-green-cyan-color{color: var(--wp--preset--color--vivid-green-cyan) !important;}.has-pale-cyan-blue-color{color: var(--wp--preset--color--pale-cyan-blue) !important;}.has-vivid-cyan-blue-color{color: var(--wp--preset--color--vivid-cyan-blue) !important;}.has-vivid-purple-color{color: var(--wp--preset--color--vivid-purple) !important;}.has-black-background-color{background-color: var(--wp--preset--color--black) !important;}.has-cyan-bluish-gray-background-color{background-color: var(--wp--preset--color--cyan-bluish-gray) !important;}.has-white-background-color{background-color: var(--wp--preset--color--white) !important;}.has-pale-pink-background-color{background-color: var(--wp--preset--color--pale-pink) !important;}.has-vivid-red-background-color{background-color: var(--wp--preset--color--vivid-red) !important;}.has-luminous-vivid-orange-background-color{background-color: var(--wp--preset--color--luminous-vivid-orange) !important;}.has-luminous-vivid-amber-background-color{background-color: var(--wp--preset--color--luminous-vivid-amber) !important;}.has-light-green-cyan-background-color{background-color: var(--wp--preset--color--light-green-cyan) !important;}.has-vivid-green-cyan-background-color{background-color: var(--wp--preset--color--vivid-green-cyan) !important;}.has-pale-cyan-blue-background-color{background-color: var(--wp--preset--color--pale-cyan-blue) !important;}.has-vivid-cyan-blue-background-color{background-color: var(--wp--preset--color--vivid-cyan-blue) !important;}.has-vivid-purple-background-color{background-color: var(--wp--preset--color--vivid-purple) !important;}.has-black-border-color{border-color: var(--wp--preset--color--black) !important;}.has-cyan-bluish-gray-border-color{border-color: var(--wp--preset--color--cyan-bluish-gray) !important;}.has-white-border-color{border-color: var(--wp--preset--color--white) !important;}.has-pale-pink-border-color{border-color: var(--wp--preset--color--pale-pink) !important;}.has-vivid-red-border-color{border-color: var(--wp--preset--color--vivid-red) !important;}.has-luminous-vivid-orange-border-color{border-color: var(--wp--preset--color--luminous-vivid-orange) !important;}.has-luminous-vivid-amber-border-color{border-color: var(--wp--preset--color--luminous-vivid-amber) !important;}.has-light-green-cyan-border-color{border-color: var(--wp--preset--color--light-green-cyan) !important;}.has-vivid-green-cyan-border-color{border-color: var(--wp--preset--color--vivid-green-cyan) !important;}.has-pale-cyan-blue-border-color{border-color: var(--wp--preset--color--pale-cyan-blue) !important;}.has-vivid-cyan-blue-border-color{border-color: var(--wp--preset--color--vivid-cyan-blue) !important;}.has-vivid-purple-border-color{border-color: var(--wp--preset--color--vivid-purple) !important;}.has-vivid-cyan-blue-to-vivid-purple-gradient-background{background: var(--wp--preset--gradient--vivid-cyan-blue-to-vivid-purple) !important;}.has-light-green-cyan-to-vivid-green-cyan-gradient-background{background: var(--wp--preset--gradient--light-green-cyan-to-vivid-green-cyan) !important;}.has-luminous-vivid-amber-to-luminous-vivid-orange-gradient-background{background: var(--wp--preset--gradient--luminous-vivid-amber-to-luminous-vivid-orange) !important;}.has-luminous-vivid-orange-to-vivid-red-gradient-background{background: var(--wp--preset--gradient--luminous-vivid-orange-to-vivid-red) !important;}.has-very-light-gray-to-cyan-bluish-gray-gradient-background{background: var(--wp--preset--gradient--very-light-gray-to-cyan-bluish-gray) !important;}.has-cool-to-warm-spectrum-gradient-background{background: var(--wp--preset--gradient--cool-to-warm-spectrum) !important;}.has-blush-light-purple-gradient-background{background: var(--wp--preset--gradient--blush-light-purple) !important;}.has-blush-bordeaux-gradient-background{background: var(--wp--preset--gradient--blush-bordeaux) !important;}.has-luminous-dusk-gradient-background{background: var(--wp--preset--gradient--luminous-dusk) !important;}.has-pale-ocean-gradient-background{background: var(--wp--preset--gradient--pale-ocean) !important;}.has-electric-grass-gradient-background{background: var(--wp--preset--gradient--electric-grass) !important;}.has-midnight-gradient-background{background: var(--wp--preset--gradient--midnight) !important;}.has-small-font-size{font-size: var(--wp--preset--font-size--small) !important;}.has-medium-font-size{font-size: var(--wp--preset--font-size--medium) !important;}.has-large-font-size{font-size: var(--wp--preset--font-size--large) !important;}.has-x-large-font-size{font-size: var(--wp--preset--font-size--x-large) !important;}
:where(.){gap: ;}:where(.){gap: ;}
:where(.){gap: 2em;}:where(.){gap: 2em;}
:root :where(.wp-block-pullquote){font-size: ;line-height: 1.6;}
</style>
<style id="futurio-stylesheet-inline-css" type="text/css">
.full-head-img {
padding-bottom: 60px;
padding-top: 60px;
}., ., .futurio-woo-content {
padding-left: 0%;
padding-right: 0%;
}
</style>
<style>
.:nth-of-type(n+4):not(.e-lazyloaded):not(.e-no-lazyload),
.:nth-of-type(n+4):not(.e-lazyloaded):not(.e-no-lazyload) * {
background-image: none !important;
}
@media screen and (max-height: 1024px) {
.:nth-of-type(n+3):not(.e-lazyloaded):not(.e-no-lazyload),
.:nth-of-type(n+3):not(.e-lazyloaded):not(.e-no-lazyload) * {
background-image: none !important;
}
}
@media screen and (max-height: 640px) {
.:nth-of-type(n+2):not(.e-lazyloaded):not(.e-no-lazyload),
.:nth-of-type(n+2):not(.e-lazyloaded):not(.e-no-lazyload) * {
background-image: none !important;
}
}
</style>
<style type="text/css" id="futurio-header-css">
.site-title,
.site-description {
position: absolute;
clip: rect(1px, 1px, 1px, 1px);
}
</style>
<style id="kirki-inline-styles">body, a, .nav-subtitle{font-size:17px;font-weight:500;letter-spacing:0px;line-height:1.6;}.news-item a{font-size:26px;font-weight:300;letter-spacing:0px;line-height:1.6;}.news-item .post-excerpt{font-size:16px;font-weight:300;letter-spacing:0px;line-height:1.6;}.top-bar-section{font-size:15px;letter-spacing:0px;text-transform:none;}.site-branding-text a:hover, .site-branding-text .site-title a:hover, .site-branding-text , .site-branding-text .site-title, .site-branding-text a, .site-branding-text .site-title a{font-family:Roboto;font-size:28px;font-weight:900;letter-spacing:0px;line-height:32px;text-transform:uppercase;color:#000000;}{font-size:15px;letter-spacing:0px;line-height:22px;text-transform:none;}#site-navigation, #site-navigation .navbar-nav > li > a, #site-navigation .dropdown-menu > li > a{font-size:13px;letter-spacing:2px;text-transform:uppercase;}#sidebar .widget-title h3{font-size:20px;font-weight:400;letter-spacing:0px;line-height:1.6;}.widget{font-size:15px;font-weight:400;letter-spacing:0px;line-height:1.6;}#content-footer-section .widget{font-size:15px;font-weight:400;letter-spacing:0px;text-transform:none;}#content-footer-section .widget-title h3{font-size:15px;font-weight:400;letter-spacing:0px;line-height:1.6;}.heading-row .site-heading{padding-bottom:15px;padding-top:15px;}.{height:80px;}.site-branding-logo img{max-height:80px;}.heading-menu .site-branding-logo img{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.heading-menu .site-branding-text{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.shrink .{height:50px;}.shrink .site-branding-logo img{max-height:50px;}.shrink .heading-menu .site-branding-logo img{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.shrink .heading-menu .site-branding-text{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.navbar-nav .menu-button {-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}.futurio-content{padding-left:10%;padding-right:10%;}.full-head-img{padding-bottom:51px;padding-top:51px;}.full-head-img:after{background-color:rgba(41,152,249,);}.news-thumb img{-webkit-border-radius:46px;-moz-border-radius:46px;border-radius:46px;-webkit-box-shadow:0px 0px 11px 0px rgba(0,0,0,);-moz-box-shadow:0px 0px 11px 0px rgba(0,0,0,);box-shadow:0px 0px 11px 0px rgba(0,0,0,);}@media (max-width: 992px){.heading-row .site-heading{padding-bottom:15px;padding-top:15px;}}@media (max-width: 768px){.heading-row .site-heading{padding-bottom:15px;padding-top:15px;}}@media (min-width: 992px){.{width:25%;}}@media (min-width: 768px){.navbar-nav > li > a, .menu-cart, .menu-account, .top-search-icon, .menu-button, .offcanvas-sidebar-toggle{padding-top:30px;padding-right:10px;padding-bottom:30px;padding-left:10px;}.shrink .navbar-nav > li > a, .shrink .top-search-icon, .shrink .menu-cart, .shrink .menu-account, .shrink .menu-button, .shrink .offcanvas-sidebar-toggle{padding-top:15px;padding-right:10px;padding-bottom:15px;padding-left:10px;}}/* cyrillic-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
}
/* cyrillic */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
/* greek-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+1F00-1FFF;
}
/* greek */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
}
/* math */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0310, U+0312, U+0315, U+031A, U+0326-0327, U+032C, U+032F-0330, U+0332-0333, U+0338, U+033A, U+0346, U+034D, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2016-2017, U+2034-2038, U+203C, U+2040, U+2043, U+2047, U+2050, U+2057, U+205F, U+2070-2071, U+2074-208E, U+2090-209C, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2100-2112, U+2114-2115, U+2117-2121, U+2123-214F, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B7, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+3030, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
}
/* symbols */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8BB, U+1F8C0-1F8C1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA89, U+1FA8F-1FAC6, U+1FACE-1FADC, U+1FADF-1FAE9, U+1FAF0-1FAF8, U+1FB00-1FBFF;
}
/* vietnamese */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
}
/* latin-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
}
/* latin */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
}/* cyrillic-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
}
/* cyrillic */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
/* greek-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+1F00-1FFF;
}
/* greek */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
}
/* math */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0310, U+0312, U+0315, U+031A, U+0326-0327, U+032C, U+032F-0330, U+0332-0333, U+0338, U+033A, U+0346, U+034D, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2016-2017, U+2034-2038, U+203C, U+2040, U+2043, U+2047, U+2050, U+2057, U+205F, U+2070-2071, U+2074-208E, U+2090-209C, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2100-2112, U+2114-2115, U+2117-2121, U+2123-214F, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B7, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+3030, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
}
/* symbols */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8BB, U+1F8C0-1F8C1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA89, U+1FA8F-1FAC6, U+1FACE-1FADC, U+1FADF-1FAE9, U+1FAF0-1FAF8, U+1FB00-1FBFF;
}
/* vietnamese */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
}
/* latin-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
}
/* latin */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
}/* cyrillic-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
}
/* cyrillic */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
/* greek-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+1F00-1FFF;
}
/* greek */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
}
/* math */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0310, U+0312, U+0315, U+031A, U+0326-0327, U+032C, U+032F-0330, U+0332-0333, U+0338, U+033A, U+0346, U+034D, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2016-2017, U+2034-2038, U+203C, U+2040, U+2043, U+2047, U+2050, U+2057, U+205F, U+2070-2071, U+2074-208E, U+2090-209C, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2100-2112, U+2114-2115, U+2117-2121, U+2123-214F, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B7, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+3030, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
}
/* symbols */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8BB, U+1F8C0-1F8C1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA89, U+1FA8F-1FAC6, U+1FACE-1FADC, U+1FADF-1FAE9, U+1FAF0-1FAF8, U+1FB00-1FBFF;
}
/* vietnamese */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
}
/* latin-ext */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
}
/* latin */
@font-face {
font-family: 'Roboto';
font-style: normal;
font-weight: 900;
font-stretch: 100%;
font-display: swap;
src: url() format('woff2');
unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
}</style>
</head>
<body id="blog" class="home page-template-default page page-id-31 wp-custom-logo elementor-default elementor-kit-297 elementor-page elementor-page-31">
<span class="skip-link screen-reader-text"><br>
</span>
<div class="page-wrap">
<div class="container-fluid main-container page-builders" role="main">
<div class="page-area">
<div class="row">
<div class="post-31 page type-page status-publish hentry">
<div class="futurio-content main-content-page">
<div class="single-entry-summary">
<div data-elementor-type="wp-post" data-elementor-id="31" class="elementor elementor-31">
<div class="elementor-shape elementor-shape-bottom" data-negative="false">
<svg xmlns="" viewbox="0 0 1000 100" preserveaspectratio="none">
<path class="elementor-shape-fill" opacity="" d="M473,,,0C66,119.1,0,59.7,0, c0,0-62.1,,,,49.6,745.3,8.7,694.9,,59,473,">
<path class="elementor-shape-fill" opacity="" d="M734,,,39.1 ,0C115.7,118.3,0,39.8,0,,,18.1,775.7,67.3,734,">
<path class="elementor-shape-fill" d="M766.1,,,,1.8,242,5.4,184.8,,35.8,132.3,44.9,89.9,,63.7,0,0,0,0 h1000c0,0-9.9,,,47,766.1,">
</path> </path></path></svg></div>
<div class="elementor-container elementor-column-gap-default">
<div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-46e3c576" data-id="46e3c576" data-element_type="column">
<div class="elementor-widget-wrap elementor-element-populated">
<div class="elementor-element elementor-element-61b0303 text-center elementor-widget elementor-widget-advanced-text-block" data-id="61b0303" data-element_type="widget" data-widget_type="">
<div class="elementor-widget-container">
<div class="futurio_extra_adv_text_block animate-general" data-animate-type="" data-animate-delay="50">
<h2 class="text-content-block">
<p>Gpt2 python. ファインチューニング.</p>
</h2>
</div>
</div>
</div>
<section class="elementor-section elementor-inner-section elementor-element elementor-element-09369bb elementor-section-height-min-height elementor-section-content-middle elementor-section-boxed elementor-section-height-default" data-id="09369bb" data-element_type="section">
</section>
<div class="elementor-container elementor-column-gap-default">
<div class="elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-a6796ee" data-id="a6796ee" data-element_type="column">
<div class="elementor-widget-wrap elementor-element-populated">
<div class="elementor-element elementor-element-6580b53c elementor-widget elementor-widget-writing-effect-headline" data-id="6580b53c" data-element_type="widget" data-widget_type="">
<div class="elementor-widget-container">
<h2 class="futurio-extra-written-headline" data-speed="33" data-delay="2000" data-loop="1">
<span class="before-written">Gpt2 python. 0 Transformers version: 2.</span><span class="written-lines">
</span>
</h2>
</div>
</div>
</div>
</div>
</div>
<div class="elementor-element elementor-element-1e2e33c elementor-widget elementor-widget-spacer" data-id="1e2e33c" data-element_type="widget" data-widget_type="">
<div class="elementor-widget-container">
<div class="elementor-spacer">
<div class="elementor-spacer-inner"></div>
</div>
</div>
</div>
<section class="elementor-section elementor-inner-section elementor-element elementor-element-58fb294d elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="58fb294d" data-element_type="section">
</section>
<div class="elementor-container elementor-column-gap-no">
<div class="elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-5183a0df" data-id="5183a0df" data-element_type="column">
<div class="elementor-widget-wrap elementor-element-populated">
<div class="elementor-element elementor-element-4886141 text-center elementor-widget elementor-widget-advanced-text-block" data-id="4886141" data-element_type="widget" data-widget_type="">
<div class="elementor-widget-container">
<div class="futurio_extra_adv_text_block">
<div class="text-content-block">
<p>Gpt2 python run([var for var in tf. Parece que es un problema histórico y siempre van en Pytorch una versión por detrás de Python. It is also known as the "language of the Internet" because it is the most widely used language in the world. 3. It is based on the extremely awesome repository from HuggingFace team Transformers. ! Apr 27, 2023 · Setting up Python. Next, you will saw how could you easily set new options for simulation of HBM ImageNet. from transformers import pipeline generator = pipeline('text-generation', model='gpt2') generator("Hello world, continue Dec 23, 2020 · OpenAI’s GPT-2 is the world’s most advanced framework for NLP tasks in Python. written in the programming languages Python and Cython. If you are using a Jupyter Notebook solution (like DataLab), it's also helpful to import some functions from IPython. Before starting, set Runtime Type to GPU on the top menu bar. The four models trained as part of the research for GPT-2 are “approximately log-uniformly spaced sizes” starting with the smallest model having 124 million parameters and ending with the largest model having 1. A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions). from_pretrain All 376 Python 193 Jupyter Notebook 140 HTML 9 JavaScript 6 C++ 5 TypeScript 3 CSS GPT2 for Chinese chitchat/用于中文闲聊的GPT2模型(实现了DialoGPT的 Overview¶. Contribute to telunyang/python_web_scraping development by creating an account on GitHub. Apr 18, 2023 · 参考になれば幸い。GPT-3が流行ってるのにGPT-2やるの時代遅れとか言わないファインチューニング友達がたくさん入ってるグループがあって、そのグループっぽい会話を生成したかったのでファインチ… Dec 13, 2019 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand This repository provides code and instructions for fine-tuning GPT-2 to produce contextually relevant chatbot responses using PyTorch and transformers. When I type “Hi! My name is Isamu” the model outputted. I am using nsheppard's code for retraining it on my custom dataset. Note that it may take some time until the Aug 11, 2020 · I am trying to use gpt-2 for text generation. I've built the application using flask and docker, the service is deployed on Cloud Run (GCP). py 脚本来评估和测试你的 Objective: Develop a custom GPT-2 small model from scratch, closely following the original design but without utilizing any pre-existing transformer libraries. Sep 9, 2020 · Select the GPT2 environment in Anaconda and install Spyder, the Python IDE, in the environment. This course covers all the basics and advanced concepts necessary to We spent 6 months making GPT-4 safer and more aligned. 19. GPT2Model (config) [source] ¶. 10. Jul 2, 2023 · はじめに元AI女子高生「りんな」をご存知でしょうかLINEに突如現れたAI女子高生で話題となっていたと思いますので、ご存知の方も多いかとおもいます。先日「りんな」の開発元であるrinna社から… Sep 4, 2019 · Likewise, you can use the gpt2. The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top. It also encodes a user input prompt received via command line arguments or user input, and then uses it to predict the output sequence Nov 15, 2022 · 3. cd examples python training_gpt2_mydata. Please check your connection, disable any ad blockers, or try using a different browser. In the original colab Mar 14, 2021 · Text Generation With GPT-2 in Python # python # machinelearning # deeplearning # tutorial Language generation is one of those natural language tasks that can really produce an incredible feeling of awe at how far the fields of machine learning and artificial intelligence have come. 7. Dec 7, 2022 · """ Datafile is a text file with one sentence per line _DATASETS/data. aitextgen is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features. The implementation will be done in Python using PyTorch. Dec 4, 2022 · GPT2 for Chinese chitchat/用于中文闲聊的GPT2模型(实现了DialoGPT的MMI思想) Topics nlp text-generation transformer gpt-2 gpt2 dialogpt chichat dialogue-model Apr 28, 2022 · Para evitar este problema, lo ideal sería reentrenar este modelo GPT2 pero con textos en español. The training process is straightforward since GPT2 is capable of several tasks, including summarization, generation, and translation. Module sub-class. display. BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. 7 or earlier model. Jul 11, 2023 · To generate text using transformers and GPT2 model, if you're not particular about modifying different generation features you can use the pipeline function, e. trainable_variables]) to get trained parameters and save them as numpy array. threading. For a solid foundation in Python, consider the comprehensive Python Foundation Course offered by us. 0 模型： python train. GPT2 finetuning with transformers 🤗. To use GPT via the API, you need to import the os and openai Python packages. Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to Oct 17, 2021 · A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI GPT-2 text generation model (specifically the "small", 124M hyperparameter version). from_pretrained (" rinna/japanese-gpt2-medium ") tokenizer. Open Spyder and create a new project in the existing GPT2 directory (Projects > New Project). On startup the demo application reads command line parameters and loads a model to OpenVINO™ Runtime plugin. py model. py, but in even fewer lines of code. It includes setup, dataset preparation, and training examples for efficient model customization. 04) using float16 with gpt2-large, we saw the following speedups during training and inference. md Transformer-based Language Model - GPT2# This notebook runs on Google Colab. The dataset for the fine-tuning operation is available on the Huggingface Hub, and it’s a subset of a bigger dataset hosted on Kaggle. Train the model and output the data. 3. de. generate() function will generate as much text as possible (1,024 tokens) with a little bit of randomness. In today’s tutorial, I’ll walk you through how to get started with GPT-2. We’re on a journey to advance and democratize artificial intelligence through open source and open science. from_pretrained('gpt2') model = GPT2Model. A collection of projects involving generating text using a generative recurrent neural network and GPT-2. py is the same as gpt2. We’ll see what it will come up at the end :). py touch gpt2. Additionally, this package allows easier generation of text, generating to a file for easy Jan 24, 2023 · Python is a powerful and versatile programming language that is widely used in various industries. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. test. However, I cannot see how I can load the dataset. py Lección 16 del módulo “Redes Transformer” del curso Fundamentos de Deep Learning con Python. (NASDAQ:SUN). 0 code upgrade script. ioChannel membership Jan 6, 2021 · A tutorial to get started with GPT-2 on Google Colab. Then, we used these repository URLs to download all contents of each repository from GitHub. May 13, 2023 · PythonからOpenAI APIが提供するライブラリopenaiライブラリをつかって、GPT-3を呼び出してテキスト生成や質問応答などのタスクを行うことができます。 1. May 13, 2022 · Fine tuning GPT2. copy_checkpoint_from_gdrive() cell to retrieve a stored model and generate in the notebook. cpp. 5B model or all of them. config. Chatbot using GPT-2. 0. GPT-2 relies on TensorFlow for its underlying deep learning framework. exists("pgn"): os. The training process is configured using the TrainingArguments class. ファインチューニング. keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding. 7M+ scientific papers belonging to the STEM category. Sep 25, 2019 · I am trying to run gpt-2 on my local machine, since google restricted my resources, because I was training too long in colab. (the current version of TF as of this post) does not allow for training/fine-tuning of gpt2 without some creative modifications. 2M python-related repositories hosted by GitHub. It is free to use and easy to try. You can disable this in Notebook settings. Why? Because why not. GPT-2 models' robustness and worst case behaviors are not well-understood. With the power of Python and Generative AI, you can build a chatbot that not only responds to queries but also generates human-like conversations. Feb 5, 2020 · ️ Create a new Anaconda Environment named GPT2 and running Python 3. from transformers import T5Tokenizer, AutoModelForCausalLM tokenizer = T5Tokenizer. mkdir("pgn") Download PGN game files to the pgn folder. json gpt2-generate. To begin training and getting cool samples, run the following code. You switched accounts on another tab or window. The GPT_Model_Trainer project is designed to train GPT-2 models with support for multi-format data ingestion, real-time loss monitoring, and integration with the Hugging Face architecture. I trained my model on a custom dataset of conversations that I pulled from my facebook data. Oct 4, 2024 · python train_gpt2. 1 japanese-gpt2-medium. The model used is a GPT-2 large which has been trained on around 40 MBs worth of Python code scraped Mar 12, 2022 · はじめにLINEにも利用されていたAIチャットbotの「りんな」を開発する会社がGPT2の日本語事前学習済みモデルを公開していたので試しました。 https://huggingface. g. January 6th, 2021. We will explore the transformer architecture upon which GPT models are built, how transformer models encode natural language into embeddings, and how GPT predicts text. do_lower_case = True model = AutoModelForCausalLM. txt seinfeld. Key training parameters include: output_dir: The directory where the trained model will be saved. npz I was losing my mind after trying someone else's super complicated gpt2 tutorial for two days. $ python3 gpt2-generate. Introduction to Chatbots; 2. For summarization we only need to include the labels of our dataset as inputs. . 6. py --help Options: --num-layers INTEGER No. After that, we got 60M raw python files under 1MB with a total size of 330GB. py -m gpt2 --output gpt2. path. In this blog post, we’ll take a look at generating Text using GPT-2 and Python Generative… Feb 15, 2021 · IMPORTANT: USE PYTHON 3. Apr 13, 2021 · Using State-of-the-Art Pretrained Models (BERT, GPT2, XLNET) for summarizing text with their respective implementation. Meta for releasing Llama 2 and Code Llama under a permissive license. py README. Jan 4, 2020 · What I would do : sess. This project leverages PyTorch and the Hugging Face transformers library to provide a flexible and efficient BibTeX entry and citation info @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } Dec 13, 2022 · El notebook me ha ido perfecto pero he tenido problemas con Python en Visual Studio Code porque no me instalaba Pytorch. Nov 4, 2019 · Questions & Help SYSTEM OS: Linux pop-os 5. This model is a PyTorch torch. As fine-tune, data we are using the German Recipes Dataset, which consists of 12190 german recipes with metadata crawled from chefkoch. rebuild a numpy only version of gpt2. 2. py seinfeld. ️ Clone the GPT-2 repository to your computer: The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3. Nov 3, 2019 · A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI’s GPT-2 text generation model (specifically the “small” 124M and “medium” 355M Dec 28, 2020 · Learn how to build a high-quality natural language generation model in Python using OpenAI's GPT-2 model. 4. The original dataset, published by Cornell University, contains titles and abstracts of 1. py --dataset lyric. bin files and artifacts by running python dev/data/tinyshakespeare. ; num_train_epochs: The number of training epochs (0. bfloat16). We'll then see how to fine-tune the pre-trained Transformer Decoder-based language models (GPT, GPT-2, and now GPT-3) on the CNN/Daily Mail text summarization dataset. We've covered loading and fine-tuning a pre-trained GPT-2 model, creating a Flask API, integrating GPT-2 with the API, building a simple React frontend, and deploying the platform. 1. 96 datasets 2. A machine learning project that aims to test the extent to which GPT-2 is able to understand Python syntax. Feb 6, 2020 · sess = gpt2. GPT-2, short for Generative Pre-trained Transformer 2, has introduced a revolutionary approach to natural language understanding and text generation through innovative pre-training techniques on a vast corpus of internet text and transfer learning. Outputs will not be saved. 0 sentencepiece 0. nn. modeling_tf_utils import get_initializer import os # use 2 cores tf. float16 or torch. You should now see the GPT2 directories in your IDE. 言語モデルは、会話や文章の「人間が使う言葉」を確率としてモデル化したものです。 You signed in with another tab or window. If you intend to Aug 5, 2024 · Introduction. Contribute to SIC98/GPT2-python-code-generator development by creating an account on GitHub. finetune(sess, file_name, model_name=model_name, checkpoint_dir=checkpoint_dir, run_name=run_name, steps=25, ) This will automatically grab the latest checkpoint from your checkpoint/run-name folder, load its weights, and continue training where it left off. One example also uses the yfinance package to retrieve stock prices. py ja-bpe. In this article, we've guided you through building a ChatGPT-like platform using GPT-2, Python, and React. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx . The model is pretrained on a WebText dataset - text from 45 million website links. Programming languages supported: Python Introduced DL concepts: Few-shot learning, prompt engineering: GPT-3. pth --eval_corpus corpus. Primero, debemos instalar las librerías necesarias en nuestro entorno de Python: python convert_to_onnx. As a language model, we are using GPT-2 Large Pre-trained model and for the Text Generation pipeline, we are using Hugging Face Transformers Nov 27, 2019 · python encode. You signed in with another tab or window. Nov 10, 2019 · The best way to get input text to-be-trained into the Colaboratory VM, and to get the trained model out of Colaboratory, is to route it through Google Drive first. 12 transformers 4. 1, OS Ubuntu 22. nlp machine-learning text-classification named-entity-recognition seq2seq transfer-learning ner bert sequence-labeling nlp-framework bert-model text-labeling gpt-2 Apr 17, 2023 · For readers knowing Chinese, this part illustrates how to fine-tune GPT2 on Chinese poem dataset to teach our model to become a poet! Because GPT2 uses byte-pair encoder, and the original pretraining dataset contains some Chinese characters, we can use the original vocab to finetune on Chinese dataset. md encode_bpe. Recall that GPT-2 parses its input into tokens (not words): the last word in 'Joe flicked the grasshopper' is actually three tokens: ' grass', 'ho', and 'pper'. 5 billion parameters) on its release. In this post, we’ll walk you through the steps to create your own chatbot using Python and popular generative AI tools. Can write poems, news, novels, or train general language models. py --text " It was a bright cold day in April, and the clocks were striking thirteen. Contribute to CyberZHG/keras-gpt-2 development by creating an account on GitHub. Phind for fine-tuning the Code Llama 34B model. 8+ application. I want to have more then one model loaded to have multiple models Jul 5, 2024 · I have tried to make this as simpler as possible. 8 Torch version: 1. py contains the actual GPT model and generation code, which we can run as a python script. py and then python train_gpt2. Use the OpenAI GPT-2 language model (based on Transformers) to: Generate text sequences based on seed texts. We first crawled 1. - lutzroeder/gpt2 A movie plot generating python notebook. Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts - minimaxir/gpt-2-simple GPT-2 is a Transformer architecture that was notable for its size (1. Aug 23, 2021 · 個人が無料でここまでできる時代になったのですね。Pythonの知識さえあれば、日本語の文章を自動で作成できるのです。この記事では、日本語GPT-2モデルを用いて日本語文章を自動生成する方法をPython初心者でも理解できるように解説しています。 Mar 23, 2023 · Download gpt-2-simple for free. All 12 Python 7 Jupyter Notebook 4 PHP 1. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator - microsoft/onnxruntime This workshop offers a general introduction to the GPT (Generative Pretrained Transformers) model. of decoder layers [default: 8] --embedding-size INTEGER Embedding size [default: 768] --num-heads INTEGER Number of heads [default: 8] --dff INTEGER Filter Size [default: 3072] --max-seq-len INTEGER Seq length [default: 515] --vocab-size INTEGER Vocab size [default: 24512] --optimizer TEXT optimizer type [default: adam] --batch Nov 19, 2019 · Inside the docker container’s working directory, in the src folder, we will find two python files called generate_unconditional_samples. 0 です。 後続処理のtokenizerとして利用する GPT2 tokenizer がサブワードベースの分割器であり, 低頻度語をサブ Sep 4, 2020 · We are going to apply Python development, for instance, GPT 2 python chess, so you’ll need to install python-chess [4] and aitextgen [5] modules: In [ ]: !pip install python-chess !pip install aitextgen !pip install tqdm 1. 5 billion parameters. We'll be reimplementing gpt2. Jan 22, 2023 · rinna社の日本語GPT-2の特徴. 2. ipynb in https://api. py --do_train --do_predict --num_epochs 15 --output_dir outputs-gpt2 --model_name gpt2 PS: fine-tuned result model is GPT2-python: shibing624/code-autocomplete-gpt2-base , I spent about 24 hours with V100 to fine-tune it. Python は 3. co/ri… Kashgari is a production-level NLP Transfer learning framework built on top of tf. Live demo available (Please click "Restart Space" if it has timed out due to inactivity). 今回は、先程も紹介した記事の通りにファインチューニングしました。 こちらの記事が大変わかりやすいので、割愛させていただきます。 GPT2Model¶ class transformers. Reload to refresh your session. It uses the Hugging Face's Transformers library for the GPT-2 model and tokenizer, and the Datasets library for handling the dataset Feb 23, 2022 · 訓練済みモデルを使うときは，語彙を揃えるために対応するトークナイザーを使います．transformersにはGPT-2のためのトークナイザーとしてGPT2TokenizerFastがあるので，これを使うことにします．モデルのIDにはgpt2を指定します．他にも，パラメータ数がより多い Dec 3, 2022 · Transformersを使うと、GPTの事前学習モデルを使って簡単に文章生成ができます。モデル自体は同じでも色々なメソッドが用意されていて、用途に応じて適切なインターフェースを選ぶことでより便利に… GPT-2 chatbot for daily conversations trained on Daily Dialogue, Empathetic Dialogues, PERSONA-CHAT, Blended Skill Talk datasets. This Python script trains a GPT-2 language model for code generation. Codes from A Comprehensive Guide to Build Your Own Language Model in Python Use the OpenAI GPT-2 language model (based on Transformers) to: Generate text sequences based on seed texts Dec 10, 2021 · python interactive_conditional_samples --model_name=345M. 0 Transformers version: 2. Sort options. $ python main. Includes notebooks for fine-tuning and training from scratch GPT-2 and TextGenRNN as well a May 12, 2019 · python --restore_from path/to/checkpoint train. I wasn't able to find much information on how to use GPT2 for classification so I decided to make this tutorial using similar structure with other transformers models. 9. I get compatibility errors, even after running the Tensorflow 2. Anyone with any level of Python or machine learning can follow along and build the model. Tools and Libraries; 3. Its goal is to generate meaningful phrases and sentences in the form of human-written text. GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3. This section delves into how to effectively utilize GPT-2 for text generation tasks, particularly in Python. You can choose between the small 117M, medium 345M, large 774M model, xl 1. x (the version of Python you need to be running to work with GPT-2 at the moment): conda create -n GPT2 python=3. 0 Python version: 3. txt Visualize metrics. 5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models. While there have been larger language models released since August, we’ve continued with our original staged release plan in order to provide the community with a test case of a full ChatGPT helps you get answers, find inspiration and be more productive. npz. py --context= " 『GPT-2』は、入力された文章の続きを作成するAIです。 このデモンストレーションでは、このエリアに入力された文章の続きとなる文章を生成します。 Jun 4, 2019 · I am experimenting with the gpt-2 model's conditional text generation to tweak it for a good chatbot. txt report sampling. Resources This project will take you through all the steps for building a simple GPT-2 model and train on bunch of Taylor Swift and Ed Sheeran songs. The first one generates random samples using the trained model whereas the latter waits for user input and tries to complete the user’s text sequence. 2023年2月現在、OpenAI GPT-3をPythonから使うには、OpenAIが提供するopenaiパッケージを使用します。 Web scraping (網路爬蟲). It largely follows the previous GPT architecture with some modifications: Layer normalization is moved to the input of each sub-block, similar to a pre-activation residual network and an additional layer A minimal version of GPT-2 in 175 lines of PyTorch code. 🏋️‍♂️ 在这个过程中，你的模型将通过大量的文本数据学习如何生成文本。训练可能需要一些时间，但耐心是值得的！ 4. Mar 10, 2023 · A step-by-step guide to setup a runnable GPT-2 model on your PC or laptop, leverage GPU CUDA, and output the probability of words generated by GPT-2, all in Python Andrei for building the Python bindings for llama. torch. - realdarter/SimpleGPT Sep 29, 2023 · Understanding GPT-2. py Mar 28, 2022 · What is Python? Python is a general-purpose programming language that is used to write computer programs. Python code example for building a generative transformer chatbot with a GUI using the Tkinter library. onnx (2) Convert pretrained model 'distilgpt2' to ONNX, and use optimizer to get float16 model. For example, if you're using Conda, use the command conda create --name gpt2 python=3. A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions). 学習データ. com/repos/keras-team/keras-io/contents/examples/generative/ipynb?per_page=100&ref=master In this article I will describe an abstractive text summarization approach, first mentioned in $[1]$, to train a text summarizer. py. Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts - minimaxir/gpt-2-simple May 26, 2024 · In this article, we’ll walk through the process of fine-tuning a pre-trained GPT-2 model using the Hugging Face Transformers library, and then performing inference on the newly trained model. The training part includes building and uploading the GPT2 model to Layer. $ . Sort: Most stars. Dec 29, 2022 · Basically, we initialize from a GPT2 checkpoint with init_from and train as normal, except shorter and with a small learning rate. In the tutorial, we are going to fine-tune a German GPT-2 from the Huggingface model hub. May 22, 2021 · Training some some short tests of the newly-minted generative python transformer model :DNeural Networks from Scratch book: https://nnfs. 模型评估： 运行 demo. OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. Main idea: Since GPT2 is a decoder transformer, the last token of the input sequence is used to make predictions about the next token that should follow the input. Most stars Fewest python cli gpt-2 gpt2 gpt-2-text-generation gpt-2-chatbot gpt-2-model. gpt2 Colab Notebook and Dataset + Model to Generate Fake Trump Tweets. PGN files download import os if not os. py文件的模型加载部分进行替换。 你吃了吗？ 吃过了，吃的糖醋排骨 For the best speedups, we recommend loading the model in half-precision (e. Codes from A Comprehensive Guide to Build Your Own Language Model in Python. Python is written in C++, a high-level, object-oriented language developed by Sun Microsystems, Inc. Build your own GPT-2 AI text generator in Python. If you'd prefer to avoid running the starter pack script, then as mentioned in the previous section you can reproduce the exact same . py 脚本来训练你的 GPT-2. Table of Contents. 1 I am running this linux VM with the above software versions on a Windows 10 l python open-source numpy pytorch gpt paper-implementations attention-is-all-you-need pytorch-implementation gpt-2 gpt2 gpt-3 gpt-implementation gpt-using-pytorch. This chatbot is made based on GPT2 Model transformer with a language modeling head on top. AIに返答文作成のパターンを学習させるため、学習データを用意します。 これは自分でメモ帳に返答パターンをひたすら書いて作成しました。 Jan 30, 2023 · gpt2. py from scratch, so let's delete it and recreate it as an empty file: rm gpt2. $ python train_gpt2. from_pretrained (" rinna/japanese-gpt2-medium ") text = " こんな夢を見ていた。 Jul 8, 2022 · 実際にGPT2-japaneseを使って遊んでみましょう。 今回は文章生成にGPT-1bを、ファインチューニングにはGPT2-mediumを使用します。 ファインチューニングするにはGPT-1bだと学習時間がかかってしまうので、パラメータ数などがGPT-1bよりも低いGPT2-mediumを使用してい Feb 2, 2021 · Steps. 模型训练： 运行 train. To Could not find gpt2_text_generation_with_kerasnlp. Apr 11, 2024 · Chinese version of GPT2 training code, using BERT tokenizer or BPE tokenizer. Ask Questions: After the training is complete, run the question-asking script: python ask_gpt2. Python package to easily retrain OpenAI's GPT-2 text-generating model . 5 in this example). According to the README. Contribute to philip-bl/gpt2_chatbot development by creating an account on GitHub. Assemble the token embeddings, positional encodings, and transformer layers into a Apr 6, 2019 · I haven't worked with GPT2, but bpemb is a very good place to start for subword embeddings. The Hugging Face Transformers library and Tkinter are among the libraries that we first load into this code. Installation. GPT-2 is a large transformer-based A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions). 6 to create a new environment named "gpt2" with Python 3. Clone the repo, install dependencies, and download the model weights. py gpt2-transform. Jul 29, 2019 · GPT-2 gives State-of-the Art results as you might have surmised already (and will soon see when we get into Python). txt tf_gpt2_keras_lora is the name of the fine-tuned model """ import tensorflow as tf from transformers import GPT2Tokenizer, TFGPT2LMHeadModel from transformers. On a local benchmark (rtx3080ti-16GB, PyTorch 2. set_intra_op_parallelism A PyTorch re-implementation of GPT, both training and inference. Natural Language Generation (NLG) or Text Generation is a subfield of Natural Language Processing (NLP). Si que se instala bien con la 3. txt --vocab_path vocab. Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given How to add a pipeline to 🤗 Transformers? Testing Checks on a Pull Request. gpt2_pico. /bin/gpt-2 -h usage: . NousResearch for fine-tuning the Llama 2 7B and 13B models. txtが生成されます。 3. From here on out, follow the directions in DEVELOPERS. 5 ~175,000: 175: Vocabulary size: 175 billion words Number of languages: Over 100 Programming languages supported: Python Introduced DL concepts: Improved few-shot learning, cross-lingual transfer learning: GPT-4: Unknown: Unknown Load GPT-2 checkpoint and generate texts. Finally, we use the pipeline function to import the pre-trained GPT-2 model. ) Feb 16, 2019 · and then iterate your code all the way to it 's goal using the convolution frontend programs (RNG, Python and the hyperpanos philosophy used to model the cerebral cortex). The main objective of this project is to create a service for generating text using GPT-2 model. This notebook is open with private outputs. py Example Questions. Convert text sequences into numerical representations! Using the GPT-2 model we can generate some realistic and awesome text. /bin/gpt-2 [options] options: -h, --help show this help message and exit-s SEED, --seed SEED RNG seed (default: -1) -t N, --threads N number of threads to use during computation (default: 8) -p PROMPT, --prompt PROMPT prompt to start generation with (default: random) -n N, --n_predict N number of tokens to predict (default: 200) --top_k N top-k sampling (default: 40 A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture. Running this cell (which will only work in Colaboratory) will mount your personal Google Drive in the VM, which later cells can use to get data in/out. Moreover, you can also analyse training loss Jan 8, 2021 · Photo by Glenn Carstens-Peters on Unsplash. 4 Installing TensorFlow. Dec 11, 2020 · ％ cd gpt2-japanese gpt2-japanese % ls LICENSE emoji. Jan 31, 2020 · I'm working on a discord bot and one of the functions I want to implement responds with text generated by the gpt-2-simple library. Al final resulta que Pytorch no funciona con la última versión de Python. This means Apr 10, 2021 · For text generation, we are using two things in python. Jan 19, 2025 · GPT-2, developed by OpenAI, is a powerful language model that excels in generating coherent and contextually relevant text. $ python -m gpt2 evaluate --model_path model. Tom Jobbins for quantizing the Llama 2 models. Speaking of generation, once you have a finetuned model, you can now generate custom text from it! By default, the gpt2. Just ask and ChatGPT can help with writing, learning, brainstorming and more. py and interactive_conditional_samples. Steps I've followed: Clone repo. github. If you're running out of memory try decreasing the model size (they are {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}) or possibly decreasing the block_size (context length). Apr 28, 2021 · Using tutorials here , I wrote the following codes: from transformers import GPT2Tokenizer, GPT2Model import torch tokenizer = GPT2Tokenizer. 5 on our internal evaluations. Aug 7, 2024 · To learn more about ChatGPT and explore its applications, you might also be interested in deepening your understanding of Python, which is essential for working with APIs. You signed out in another tab or window. Nov 5, 2019 · As the final model release of GPT-2’s staged release, we’re releasing the largest version (1. 1. En la lección anterior vimos cómo GPT y usa el aprendizaje no supervisado para emular la forma como el ser humano interpreta y resuelve tareas propias del lenguaje natural, usando un enfoque de fuerza bruta. Importing Data. ️ Activate the Conda environment: conda activate GPT2 Getting and using GPT-2. In the console, the Model Prompt >>> pops up which then asks for input. machine-learning ai openai trump-tweets gpt-2 Aug 23, 2020 · I wrote a set of functions that can do precisely what you're looking for. Dec 13, 2021 · これにより、同ディレクトリ内にgpt2_train_data. The pre-trained model contains data from 8 million web pages collected from Jan 29, 2024 · Python 3. Control a Spooky Ghost Writer for Halloween with OpenAI's GPT-3 Engine, Python, and Twilio WhatsApp API; Generating Lyrics in the Style of your Favorite Artist with Python, OpenAI's GPT-3 and Twilio SMS; Automated Yugioh Deckbuilding in Python with OpenAI's GPT-3 and Twilio SMS; Build a Telephone Chatbot with GPT-3 and Twilio Autopilot python process_data. Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind, slipped quickly through the glass doors of Victory Mansions, though not quickly enough to prevent a swirl of gritty dust from entering along with him. start_tf_sess() gpt2. OpenAI introduced GPT-2 (Generative Pre-trained Transformer 2) in 2019 with the paper Better Language Models and Their Implications (2019). A straightforward guide to easy text generation. After this workshop, you will be Due to the small size of public released dataset, we proposed to collect data from GitHub from scratch. As with any machine-learned model, carefully evaluate GPT-2 for your use case, especially if used without fine-tuning or in safety-critical applications where reliability is important. You can now ask questions and get answers: 本项目是基于GPT2实现的一个中文闲聊模型，其中的模型代码是自己编写的，可能不是很适合，需要的小伙伴可以直接调用transformers库的GPT2Model来在trainer. <a href=https://mytrade.su/rnk7ku/agriculture-drone-sprayer-price-in-india.html>yngf</a> <a href=http://hobbyclick.ru/k4pojfc/rocker-sprayer.html>jutvu</a> <a href=https://lal.dk/9zpfnk/concert-radio-commercial-scripts.html>zwu</a> <a href=http://hobbyclick.ru/k4pojfc/big-4-background-check-reddit.html>fkpds</a> <a href=https://svcmutual.com/h8oez/calculus-iii-cheat-sheet-pdf.html>trzffm</a> <a href=https://www.potolki-mo.ru/qfrs/basf-nmp-sds.html>yopwpv</a> <a href=http://s891290.ha009.t.mydomain.zone/hczb/coupe-decale-alhaji-mp3-download.html>ksp</a> <a href=https://artemius-lab.ru/hzjpo/Delhi-Disawar-Satta-King.html>vdn</a> <a href=http://89168071728.ru/33hbaxkbk/how-to-say-ps5-in-spanish.html>pnbvd</a> <a href=https://jeffreymanassa.com/2k0yzt/southern-railway-passenger-train-consists.html>xrjnoj</a> </p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- end page-wrap -->
</body>
</html>