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Data Selection

Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.

I. Requirements

TBA

II. Inputs

  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab

Data Corpus

  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.

Reference text

  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.

Vocab

  • An exisiting vocabulary of the LM. One word per line.

III. Usage

Go to the project directory and run:

./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data

Example with sbatch

sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819

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, '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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Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
IV. Author
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, '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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
IV. Author
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, '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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Repository files navigation
Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
IV. Author
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, '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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Repository files navigation
Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
IV. Author
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, '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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Repository files navigation
Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
IV. Author
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, '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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Data Selection
Data selection is one of the technique used to extract relevant data from the big text corpus for the given input text data. In our scenario, the input text data is the output/transcript of ASR, built from the LM that is not domain-specific enough. Hence, the data selection is applied to extract relevant domain-specific data to re-train LM and ultimately re-build ASR to produce a better transcript.
I. Requirements
TBA
II. Inputs
  • Data Corpus (e.g. Gigaword)
  • Reference text
  • Vocab
Data Corpus
  • A large text corpus with one sentence per line. 200 Million sentenes.
  • Used to extract relevant setences correponsding to ASR transcript.
  • Placed inside "./input".
  • Contact ztkyaw@ntu.edu.sg for download link.
Reference text
  • A transcript(s) obtained from 1st pass decoding or manual transcript.
  • One sentence per line. Normalized w/ no punctuation and capitalization.
  • Will be used as a reference of relevant data extraction.
Vocab
  • An exisiting vocabulary of the LM. One word per line.
III. Usage
Go to the project directory and run:
./run.sh --steps 1-4 <path to data corpus><path to ref transcript><path to vocab><output-dir>#step 1 - produce in-domain LM from given input text file (ref transcript)#step 2 - produce out-domain LM from the 1% of data corpus (randomly selected)#step 3 - perform data-selection technique and produced the output data corpus with sentences ranked by perplexity scores ( lower = more relevant )#step 4 - prepare the output for re-training LM ( final output = ./output/$output-dir/selected_data
Example with sbatch
sbatch --nodelist=node04 -o ./log/data_select.log run.sh --steps 1 ./input/data_pool_RR_1 ./input/judge-sg.txt ./input/vocabs.txt ./output/judge-data-110819
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