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import re |
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import fasttext |
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from functools import reduce |
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FASTTEXT_MODEL_PATH = "/researchdisk/lid.176.bin" |
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fasttext_model = fasttext.load_model(FASTTEXT_MODEL_PATH) |
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def filter_stats(batch): |
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filter_stats_funcs = [ |
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_count_alphabet, |
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_count_numbers, |
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_count_upper, |
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_count_str_len, |
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_predict_lang, |
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_calculate_alphabet_ratio, |
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_calculate_number_ratio, |
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_calculate_upper_ratio, |
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] |
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return reduce(lambda x, y: y(x), filter_stats_funcs, batch) |
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def _count_alphabet(batch): |
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batch["alphabet_len"] = len(re.findall(r"[äÄöÖåÅa-zA-Z]", batch["text"])) |
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return batch |
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def _count_numbers(batch): |
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batch["number_len"] = len(re.findall(r"[0-9]", batch["text"])) |
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return batch |
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def _count_upper(batch): |
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batch["upper_len"] = len(re.findall(r"[ÄÖÅA-Z]", batch["text"])) |
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return batch |
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def _count_str_len(batch): |
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batch["total_len"] = len(batch["text"]) |
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return batch |
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def _predict_lang(batch): |
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pred = fasttext_model.predict(batch["text"]) |
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batch["predicted_lang"] = pred[0][0] |
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batch["predicted_lang_percentage"] = float(pred[1][0]) |
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return batch |
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def _calculate_alphabet_ratio(batch): |
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batch["alphabet_ratio"] = int(batch["alphabet_len"]) / int(batch["total_len"]) |
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return batch |
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def _calculate_number_ratio(batch): |
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batch["number_ratio"] = int(batch["number_len"]) / int(batch["total_len"]) |
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return batch |
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def _calculate_upper_ratio(batch): |
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batch["upper_ratio"] = int(batch["upper_len"]) / int(batch["total_len"]) |
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return batch |
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