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<!--- 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.
24 lines
927 B
Python
24 lines
927 B
Python
# coding: utf8
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from __future__ import unicode_literals
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# These exceptions are used to add NORM values based on a token's ORTH value.
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# Individual languages can also add their own exceptions and overwrite them -
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# for example, British vs. American spelling in English.
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# Norms are only set if no alternative is provided in the tokenizer exceptions.
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# Note that this does not change any other token attributes. Its main purpose
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# is to normalise the word representations so that equivalent tokens receive
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# similar representations. For example: $ and € are very different, but they're
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# both currency symbols. By normalising currency symbols to $, all symbols are
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# seen as similar, no matter how common they are in the training data.
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NORM_EXCEPTIONS = {
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"R$": "$", # Real
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"r$": "$", # Real
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"Cz$": "$", # Cruzado
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"cz$": "$", # Cruzado
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"NCz$": "$", # Cruzado Novo
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"ncz$": "$", # Cruzado Novo
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}
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