Lightweight Alternatives to spaCy Guide | Generated by AI

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Got it — you’re looking for alternatives to spaCy that are lightweight and especially useful for tasks like language detection (since you’re using langdetect and a custom simple detector).

Here are some good options:

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🔹 Language Detection–Focused Libraries

• langid.py

  • Pure Python, no external dependencies.
  • Trained on 90+ languages.
  • Deterministic and lightweight.
  • Often faster and more consistent than langdetect.

• fastText (by Facebook/Meta AI)

  • Pretrained language ID model (lid.176.bin) supports 176 languages.
  • Very fast, works on short text and noisy input.
  • Needs the fasttext Python bindings, but still relatively lightweight compared to spaCy.

• polyglot

  • Handles language detection, tokenization, POS tagging, NER.
  • Supports 100+ languages.
  • Depends on pycld2 or pyicu, slightly heavier than langid but still much lighter than spaCy.

• cld3 (Google’s Compact Language Detector v3)

  • Neural-network based, very accurate on short text.
  • Python bindings available (pycld3).
  • Tiny and efficient, but you need to install C++ extension.

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🔹 Lightweight NLP Toolkits (General)

• TextBlob

  • Wraps nltk + pattern.
  • Provides language detection, sentiment, POS tagging, translation via APIs.
  • Very simple to use, good for quick scripts, but less robust than spaCy.

• Stanza

  • From Stanford NLP.
  • Accurate tokenization, tagging, parsing.
  • Still smaller and easier to set up than spaCy, but not as “tiny” as langid/fastText.

• NLTK

  • Classic Python NLP toolkit.
  • Many utilities but less optimized and heavier than you may want.
  • Not great for production language detection, better for teaching / prototyping.

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🔹 For Your Use Case (Validation of Translations)

Given your pipeline (langdetect + rule-based checks + fallback), the best drop-in lightweight alternatives would be:

1. langid.py → deterministic, no external deps, works out of the box.
2. pycld3 → very strong for short noisy text, also lightweight.
3. fastText LID → if you want accuracy across many languages (but larger model file).

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👉 Recommendation:

• Replace langdetect with langid.py if you want simplicity and determinism.
• Use pycld3 if your text is often short (like titles or snippets).
• If you care about broad coverage and don’t mind a bigger model file, fastText is excellent.

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Do you want me to adapt your detect_languages_with_langdetect function so it can plug-and-play with langid.py or pycld3 as drop-in replacements? That way you can benchmark accuracy vs. langdetect in your translation validator.

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