BranchesTags

Repository files navigation

detectLanguage

Purpose

The purpose of this repository is twofold:

  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).

Tatoeba

Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:

Tatoeba -- https://tatoeba.org/ -- CC-BY License

Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.

Components

Currently, there are three main components in this repository.

create_Tatoeba_train_test.py

A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).

The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.

The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.

The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.

You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.

usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb

A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.

Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.

plot_results.ipynb

A notebook for plotting the performance results by language and as a function of sentence length.

get_predictions.ipynb

A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.

About

To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.

Topics

langdetectlangidnlptatoeba

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

Contributors

Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
detectLanguage
Purpose
The purpose of this repository is twofold:
  1. Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
  2. Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).
Tatoeba
Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:
Tatoeba -- https://tatoeba.org/ -- CC-BY License
Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.
Components
Currently, there are three main components in this repository.
create_Tatoeba_train_test.py
A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).
The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.
The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.
The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.
You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.
usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]
optional arguments:
-h, --help show this help message and exit
--languages [LANGUAGES ...]
languages to include in output
--minimum_sentences MINIMUM_SENTENCES
minimum number of sentences found in corpus
--sample_type {random,stratify}
type of sample to take: "random" or "stratify"
--number_sets NUMBER_SETS
number of train-test sets to generate

evaluate.ipynb
A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.
Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.
plot_results.ipynb
A notebook for plotting the performance results by language and as a function of sentence length.
get_predictions.ipynb
A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.
About
To 1) create train/test samples of Tatoeba sentences for NLP-related tasks & 2) evaluate the performance of different solutions for detecting the language of a given text.
Topics
langdetectlangidnlptatoeba
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages