From 931e1f6503198f9f09a64ea87769c5b2387f09a4 Mon Sep 17 00:00:00 2001 From: Alexandre Flament Date: Sun, 17 Sep 2023 09:31:47 +0000 Subject: [PATCH] searx.utils.detect_language : add model from fastlangid The project fastlangid has a FastText model to achieve higher accuracy for Japanese, Korean, Chinese languages. This commits includes the model from this project, and change searx.utils.detect_language to rely on that model as a second stage. The model is lazy loaded when the base model detect Japanese, Korean or Chinese. https://github.com/currentslab/fastlangid --- searx/data/model_s.ftz | Bin 0 -> 395365 bytes searx/utils.py | 64 ++++++++++++++++++++++++++++++++--------- 2 files changed, 51 insertions(+), 13 deletions(-) create mode 100644 searx/data/model_s.ftz diff --git a/searx/data/model_s.ftz b/searx/data/model_s.ftz new file mode 100644 index 0000000000000000000000000000000000000000..2624eaa43c60013bc06bb32e03ddc27c031d51d5 GIT binary patch literal 395365 zcmZtO1$Y$a;`ZTZU4u(;cMb0D?rwnu5{N*AKq;=p-QC^Yp->zO#i3|Jf)pqYrSSch zW=_xlTwkxlx%ZjfB)c>3yz|=Z-ZYJicqL`#pM!s``DgpDe-1nRjg>Sq8fKKF$L(}rNoB`YqrIW zT>WU~XtmpHY}&N)e>ZjQuZM|mN-cOJW2Yv*_UJwZPDy+mw|rIn_91a2hil2C|Jt;7 zt#8vJV^dW<9hCTNlgJ3|?UNXa74~c*G`}YN9HR#_|%wHe7 zCO+3UqFYRqjv3LvXi=T%nD|`$k7LxLZDI(C(#JN5kK?9}SBh4N!N06_)=YfdJ0dDt z`Ha+0>#7?sDy<6D5+4uLu*!)K1B3KSrNqY#YHN(~xUTY-OWe$-E3J)*M_B_DYCNp2 zF~t*K(>)}Tzx#9H#F$+zjE4bHiYS!0IV>VFNLvafrWgAT=~#ip&|JNZhta_S(aLP3 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_STORAGE_UNIT_VALUE: Dict[str, int] = { _XPATH_CACHE: Dict[str, XPath] = {} _LANG_TO_LC_CACHE: Dict[str, Dict[str, str]] = {} -_FASTTEXT_MODEL: Optional["fasttext.FastText._FastText"] = None +_FASTTEXT_MODEL: Dict[str, "fasttext.FastText._FastText"] = {} """fasttext model to predict laguage of a search term""" +_FASTTEXT_UNCERTAIN_SETS = frozenset(('zh', 'ko', 'ja')) +_FASTTEXT_CHINESE_FAMILY_CODES = frozenset(('zh-hant', 'zh-hans', 'zh-yue')) +_FASTTEXT_MODEL_S_THRESHOLD = 0.93 + +FastTextResult = collections.namedtuple('FastTextResult', 'language, prob') + + SEARCH_LANGUAGE_CODES = frozenset([searxng_locale[0].split('-')[0] for searxng_locale in sxng_locales]) """Languages supported by most searxng engines (:py:obj:`searx.sxng_locales.sxng_locales`).""" @@ -570,15 +578,48 @@ def eval_xpath_getindex(elements: ElementBase, xpath_spec: XPathSpecType, index: return default -def _get_fasttext_model() -> "fasttext.FastText._FastText": - global _FASTTEXT_MODEL # pylint: disable=global-statement - if _FASTTEXT_MODEL is None: +def _get_fasttext_model(model_name: str) -> "fasttext.FastText._FastText": + if model_name not in _FASTTEXT_MODEL: import fasttext # pylint: disable=import-outside-toplevel # Monkey patch: prevent fasttext from showing a (useless) warning when loading a model. fasttext.FastText.eprint = lambda x: None - _FASTTEXT_MODEL = fasttext.load_model(str(data_dir / 'lid.176.ftz')) - return _FASTTEXT_MODEL + _FASTTEXT_MODEL[model_name] = fasttext.load_model(str(data_dir / model_name)) + return _FASTTEXT_MODEL[model_name] + + +def _fast_text_predict(model: str, text: str, k: int, threshold: float): + r = _get_fasttext_model(model).predict(text, k=k, threshold=threshold) + if isinstance(r, tuple) and len(r) == 2: + return [FastTextResult(language=label.split('__label__')[1], prob=prob) for label, prob in zip(r[0], r[1])] + return [] + + +def _inner_detect_language(text: str, threshold: float = 0.3) -> Optional[str]: + text = text.replace("\n", "") + r = _fast_text_predict("lid.176.ftz", text, k=1, threshold=threshold) + if len(r) == 0: + return None + + first_result = r[0] + + # fastlang + # https://github.com/currentslab/fastlangid + # Licence Apache-2.0 license + if first_result.language in _FASTTEXT_UNCERTAIN_SETS and first_result.prob < _FASTTEXT_MODEL_S_THRESHOLD: + # lid.176.ftz models usually confuse chinese, korean, japanese words + # if the model is not so sure we pass to our model to reduce down the uncertainty + r = _fast_text_predict("model_s.ftz", text, k=1, threshold=threshold) + return r[0].language if len(r) > 0 else None + + if first_result.language == 'zh' and first_result.prob >= _FASTTEXT_MODEL_S_THRESHOLD: + # predict chinese: now we want to know which chinese family it belongs + for lang, prob in _fast_text_predict("model_s.ftz", text, k=10, threshold=0): + if lang in _FASTTEXT_CHINESE_FAMILY_CODES and prob > threshold: + return lang + return 'zh' if r[0].prob > threshold else None + + return first_result.language def detect_language(text: str, threshold: float = 0.3, only_search_languages: bool = False) -> Optional[str]: @@ -643,13 +684,10 @@ def detect_language(text: str, threshold: float = 0.3, only_search_languages: bo """ if not isinstance(text, str): raise ValueError('text must a str') - r = _get_fasttext_model().predict(text.replace('\n', ' '), k=1, threshold=threshold) - if isinstance(r, tuple) and len(r) == 2 and len(r[0]) > 0 and len(r[1]) > 0: - language = r[0][0].split('__label__')[1] - if only_search_languages and language not in SEARCH_LANGUAGE_CODES: - return None - return language - return None + language = _inner_detect_language(text, threshold=threshold) + if only_search_languages and language not in SEARCH_LANGUAGE_CODES: + return None + return language def js_variable_to_python(js_variable):