Fix minor roadmap nits#6480

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michaelgsharp merged 1 commit into from
Apr 18, 2023
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Fix minor roadmap nits #6480

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8 changes: 4 additions & 4 deletions ROADMAP.md
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
- Object detection
- Named Entity Recognition (NER)
- Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Fix minor roadmap nits#6480

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michaelgsharp
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Apr 18, 2023
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Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix minor roadmap nits#6480

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 Merged

michaelgsharp
merged 1 commit into

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Apr 18, 2023
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Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix minor roadmap nits#6480

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michaelgsharp
merged 1 commit into

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Apr 18, 2023
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Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Fix minor roadmap nits#6480

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 Merged

michaelgsharp
merged 1 commit into

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Apr 18, 2023
 Merged


Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix minor roadmap nits#6480

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 Merged

michaelgsharp
merged 1 commit into

from

Apr 18, 2023
 Merged


Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix minor roadmap nits#6480

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 Merged

michaelgsharp
merged 1 commit into

from

Apr 18, 2023
 Merged


Fix minor roadmap nits
#6480
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Fix minor roadmap nits#6480

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 Merged

michaelgsharp
merged 1 commit into

from

Apr 18, 2023
 Merged


Fix minor roadmap nits
#6480
 Changes from all commits
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8 changes: 4 additions & 4 deletions
ROADMAP.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ As part of this plan, we will:

1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT)
1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:
 - Object detection
 - Named Entity Recognition (NER)
 - Question Answering
- Object detection
- Named Entity Recognition (NER)
- Question Answering
1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.
1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918).

Performance-related improvements are being tracked in this [issue](https://github.com/dotnet/machinelearning/issues/6422).

### LightBGM
### LightGBM

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

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