{"id":6362,"library":"fasttext-langdetect","title":"FastText Language Detection","description":"fasttext-langdetect is a Python wrapper for Facebook's FastText language identification model. It offers fast and up to 95% accurate language detection across over 170 languages. The library, currently at version 1.0.5 (last released in January 2023), provides a straightforward interface for identifying the language of a given text string. It downloads the necessary FastText model on its first use.","status":"maintenance","version":"1.0.5","language":"en","source_language":"en","source_url":"https://github.com/zafercavdar/fasttext-langdetect.git","tags":["language-detection","fasttext","nlp","machine-learning"],"install":[{"cmd":"pip install fasttext-langdetect","lang":"bash","label":"Install via pip"}],"dependencies":[],"imports":[{"symbol":"detect","correct":"from ftlangdetect import detect"}],"quickstart":{"code":"from ftlangdetect import detect\n\n# Detect language with default settings (low_memory=False for higher accuracy)\nresult_full = detect(text=\"Bugün hava çok güzel\")\nprint(f\"Full model result: {result_full}\")\n\n# Detect language with low_memory option (smaller model, slightly less accurate)\nresult_low_memory = detect(text=\"Bugün hava çok güzel\", low_memory=True)\nprint(f\"Low-memory model result: {result_low_memory}\")\n\n# Example with English text\nenglish_text = \"Hello, world! How are you?\"\nresult_en = detect(text=english_text)\nprint(f\"English text result: {result_en}\")","lang":"python","description":"This quickstart demonstrates how to import the `detect` function and use it to identify the language of a given text. It shows examples for both the full accuracy model (default) and the low-memory model."},"warnings":[{"fix":"Ensure network connectivity and appropriate disk space for the initial model download. The model typically caches in a temporary system directory.","message":"The FastText language model files are downloaded on the first call to the `detect()` function. This requires an active internet connection and sufficient disk space (the full model is ~126MB, the low-memory version is ~917KB). Subsequent calls will use the cached models.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Choose `low_memory=True` for memory-constrained environments at the cost of slight accuracy, or `low_memory=False` (default) for maximum accuracy.","message":"The `low_memory` parameter (defaulting to `False`) controls which model is used. Setting `low_memory=True` loads a compressed model that uses less memory but may result in slightly lower detection accuracy compared to the full model (`low_memory=False`).","severity":"gotcha","affected_versions":"All versions"},{"fix":"For short texts, results may be less reliable. Consider providing more context if possible. For very long texts, breaking them into smaller, meaningful segments might improve accuracy.","message":"Language detection accuracy can be reduced for very short text inputs (e.g., single words, short phrases) or extremely long inputs. FastText models are generally optimized for text segments around 10-80 characters for optimal performance.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Consider pre-processing input text (e.g., lowercasing, basic cleaning) if dealing with informal or noisy user-generated content to potentially improve results.","message":"Pre-trained FastText models are often trained on clean, well-structured text. Noisy inputs (e.g., text with spelling errors, unusual capitalization, slang, or mixed languages/code-switching) can lead to reduced detection accuracy.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-15T00:00:00.000Z","next_check":"2026-07-14T00:00:00.000Z"}