How to use the sister.download.cached_unzip function in sister

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github tofunlp / sister / tests / test_download.py View on Github external
def test_cache_unzip(self):
        # TODO: Not tested if the zipfile is actually unziped.
        saveto = Path(self.temp_dir) / 'saveto'
        download.cached_unzip(Path(self.zippath), saveto=saveto)
github tofunlp / sister / sister / word_embedders.py View on Github external
def get_fasttext(lang: str = "en"):
    # Download.
    urls = {
            "en": "https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.simple.zip",
            "ja": "https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ja.zip",
            "fr": "https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.fr.zip",
            }
    path = download.cached_download(urls[lang])
    path = Path(path)
    dirpath = path.parent / 'fasttext' / lang
    download.cached_unzip(path, dirpath)

    print("Loading model...")
    filename = Path(urls[lang]).stem + '.bin'
    model = load_model(str(dirpath / filename))
    return model
github tofunlp / sister / sister / word_embedders.py View on Github external
def get_word2vec(lang: str = "en"):
    # Download.
    urls = {
            "en": "https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz",
            "ja": "http://public.shiroyagi.s3.amazonaws.com/latest-ja-word2vec-gensim-model.zip"
            }
    path = download.cached_download(urls[lang])
    path = Path(path)

    filename = "word2vec.gensim.model"

    print("Loading model...")

    if lang == "ja":
        dirpath = Path(download.get_cache_directory(str(Path("word2vec"))))
        download.cached_unzip(path, dirpath / lang)
        model_path = dirpath / lang / filename
        model = gensim.models.Word2Vec.load(str(model_path))

    if lang == "en":
        dirpath = Path(download.get_cache_directory(str(Path("word2vec") / "en")))
        model_path = dirpath / filename
        download.cached_decompress_gzip(path, model_path)
        model = gensim.models.KeyedVectors.load_word2vec_format(str(model_path), binary=True)

    return model