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eddeb36c96
<!--- Provide a general summary of your changes in the title. --> ## Description - [x] Use [`black`](https://github.com/ambv/black) to auto-format all `.py` files. - [x] Update flake8 config to exclude very large files (lemmatization tables etc.) - [x] Update code to be compatible with flake8 rules - [x] Fix various small bugs, inconsistencies and messy stuff in the language data - [x] Update docs to explain new code style (`black`, `flake8`, when to use `# fmt: off` and `# fmt: on` and what `# noqa` means) Once #2932 is merged, which auto-formats and tidies up the CLI, we'll be able to run `flake8 spacy` actually get meaningful results. At the moment, the code style and linting isn't applied automatically, but I'm hoping that the new [GitHub Actions](https://github.com/features/actions) will let us auto-format pull requests and post comments with relevant linting information. ### Types of change enhancement, code style ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
100 lines
1.3 KiB
Python
100 lines
1.3 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from ...attrs import LIKE_NUM
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_num_words = set(
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"""
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صفر
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واحد
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إثنان
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اثنان
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ثلاثة
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ثلاثه
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أربعة
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أربعه
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خمسة
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خمسه
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ستة
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سته
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سبعة
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سبعه
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ثمانية
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ثمانيه
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تسعة
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تسعه
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ﻋﺸﺮﺓ
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ﻋﺸﺮه
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عشرون
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عشرين
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ثلاثون
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ثلاثين
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اربعون
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اربعين
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أربعون
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أربعين
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خمسون
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خمسين
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ستون
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ستين
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سبعون
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سبعين
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ثمانون
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ثمانين
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تسعون
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تسعين
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مائتين
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مائتان
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ثلاثمائة
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خمسمائة
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سبعمائة
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الف
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آلاف
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ملايين
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مليون
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مليار
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مليارات
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""".split()
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)
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_ordinal_words = set(
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"""
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اول
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أول
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حاد
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واحد
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ثان
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ثاني
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ثالث
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رابع
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خامس
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سادس
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سابع
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ثامن
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تاسع
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عاشر
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""".split()
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)
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def like_num(text):
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"""
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Check if text resembles a number
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"""
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if text.startswith(("+", "-", "±", "~")):
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text = text[1:]
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text = text.replace(",", "").replace(".", "")
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if text.isdigit():
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return True
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if text.count("/") == 1:
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num, denom = text.split("/")
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if num.isdigit() and denom.isdigit():
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return True
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if text in _num_words:
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return True
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if text in _ordinal_words:
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return True
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return False
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LEX_ATTRS = {LIKE_NUM: like_num}
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