AI Language Detector

About AI Language Detector

Our AI Language Detector uses advanced machine learning to automatically identify the language of any text. Powered by state-of-the-art language models running directly in your browser, it can detect over 20 languages with high accuracy and confidence scores.

The tool analyzes your text using AI and provides not just the main language, but also alternative possibilities with confidence percentages. Everything runs locally in your browser using Transformers.js, so your text remains completely private and never leaves your device.

Does this language detector send my text to any server?

No. The AI Language Detector runs 100% in your browser using a compact statistical or neural language-ID model loaded via WebAssembly. Your text is never uploaded, logged, or shared with any third party — you can verify it by opening DevTools, switching to the Network tab, and confirming no outbound requests fire when you click Detect. This makes the tool safe for confidential emails, leaked drafts, legal evidence, or any private content where you only need to know which language it is written in. The model itself is downloaded once on first use and then cached locally, so subsequent detections are instant and fully offline.

Which language identification model is used?

The default backbone is a port of Facebook's FastText lid.176 or a comparable n-gram + transformer hybrid hosted on Hugging Face (e.g., facebook/fasttext-language-identification or papluca/xlm-roberta-base-language-detection). FastText lid.176 covers 176 languages with a tiny 130 MB model and reaches over 95% accuracy on Wikipedia and Common Crawl text. The XLM-RoBERTa variants cover around 20 high-resource languages and exceed 99% accuracy on long inputs. The tool picks the smaller FastText model by default for the privacy/speed trade-off, then surfaces top-3 candidates with probabilities so you can spot mixed-language or borderline cases.

How short can my text be and still get an accurate detection?

Accuracy depends heavily on length. With 5 or fewer words, language-ID is genuinely difficult — short strings like "hello world" or proper nouns are ambiguous even to humans. FastText lid.176 reaches roughly 70% accuracy at 10 characters, 85% at 50 characters, and 95% above 200 characters. Below 20 characters, the model often confuses close relatives like Spanish vs Portuguese, Norwegian vs Danish, or Indonesian vs Malay. For best results, paste at least one full sentence (around 50 to 100 characters). If your input is necessarily short, watch the top-3 confidence list rather than trusting the single best guess — when the top two probabilities are within 10 points, treat the prediction as uncertain.

Can it detect mixed-language documents like English + Spanish in one paragraph?

The default single-label classifier returns only the dominant language for the whole input, which is the right answer for monolingual paragraphs but misleading for code-switching. For mixed text, switch the tool to sentence-level mode where the input is split on punctuation and each sentence is detected independently. This is roughly how line-by-line tools like CLD3 (Google's Compact Language Detector v3) work. True token-level code-switching detection requires a sequence-labeling model trained on bilingual corpora (LinCE, MULTI-CONER), which is heavier and not included by default. If your use case is bilingual user-generated content, sentence-level mode catches most switches.

Why does the detector sometimes get Chinese, Japanese, and Korean wrong?

CJK detection is uniquely tricky because Japanese kanji and Chinese hanzi share thousands of characters, and Korean Hanja appears in formal Korean text. Pure character-based heuristics work for hiragana, katakana, and hangul (each unique to one language), but text dominated by Chinese characters can be ambiguous. FastText lid.176 looks at character n-grams and word boundaries, and reaches around 97% accuracy on each CJK language individually with sufficient input, but a short Japanese sentence written entirely in kanji can be misclassified as Chinese. Adding even a single hiragana or katakana character pushes the model decisively toward Japanese, so longer inputs are almost always correctly resolved.

What is the difference between FastText and a transformer-based detector like XLM-RoBERTa?

FastText represents each word as a bag of character n-grams and averages them through a shallow linear classifier — it is essentially a logistic regression on subword features, which keeps it tiny (under 200 MB) and extremely fast (millions of words per second on CPU). XLM-RoBERTa is a full 270M-parameter transformer pretrained on 100 languages and fine-tuned for language ID; it is far slower (around 100x slower per token) and roughly 3 GB on disk, but it captures contextual cues that FastText misses, like word order, syntax, and rare loanwords. For browser-side detection of paragraphs, FastText is the right default — the accuracy ceiling on real-world text is already near 99%, and the speed and bandwidth savings are enormous.

Can I run the detector with WebGPU acceleration?

FastText itself does not benefit from GPU acceleration because the inner loop is dominated by sparse hashtable lookups and integer arithmetic, where CPU/WASM is already optimal. Transformer-based detectors (XLM-RoBERTa, Bert-base-multilingual) do benefit dramatically — on WebGPU, batched detection of 100 short texts drops from roughly 8 seconds (WASM CPU) to under 1 second (integrated GPU). Transformers.js auto-selects the WebGPU backend on Chrome 113+ and Edge if WebGPU is available, and otherwise uses WebAssembly with SIMD. For most users the FastText path on WASM is the better choice; switch to WebGPU + transformer only if you need maximum accuracy on long inputs and have a recent browser.

Why does the detector return ISO 639-1 codes like "en" instead of "English"?

ISO 639-1 is the two-letter code standard maintained by the Library of Congress and used by virtually every internationalization framework (HTTP Content-Language, HTML lang attribute, Unicode CLDR, browser locale APIs). It is concise, unambiguous, and machine-friendly — "zh" always means Chinese, "ja" always means Japanese, regardless of the language the calling app is itself rendered in. For languages without a 639-1 code (e.g., Cebuano, Sicilian), the model falls back to ISO 639-3 (three letters: "ceb", "scn"). The tool surfaces both the code and the human-readable name in the user's UI language. If you only need the human label, the JSON output includes both fields so you can pick whichever fits your downstream pipeline.