Some cleaning functions apply automatically. In the example in the Partition section, the output Philadelphia Eaglesâ\x80\x99 victory automatically gets converted to Philadelphia Eagles' victory in partition_html using the replace_unicode_quotes cleaning function. You can see how that works in the code snippet below:

from unstructured.cleaners.core import replace_unicode_quotes

replace_unicode_quotes("Philadelphia Eaglesâ\x80\x99 victory")

Document elements in unstructured include an apply method that allow you to apply the text cleaning to the document element without instantiating a new element. The apply method expects a callable that takes a string as input and produces another string as output. In the example below, we invoke the replace_unicode_quotes cleaning function using the apply method.

from unstructured.documents.elements import Text

element = Text("Philadelphia Eaglesâ\x80\x99 victory")
element.apply(replace_unicode_quotes)
print(element)

Since a cleaning function is just a str -> str function, users can also easily include their own cleaning functions for custom data preparation tasks. In the example below, we remove citations from a section of text.

import re

remove_citations = lambda text: re.sub("\[\d{1,3}\]", "", text)

element = Text("[1] Geolocated combat footage has confirmed Russian gains in the Dvorichne area northwest of Svatove.")
element.apply(remove_citations)
print(element)

See below for a full list of cleaning functions in the unstructured library.

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bytes_string_to_string

Converts an output string that looks like a byte string to a string using the specified encoding. This happens sometimes in partition_html when there is a character like an emoji that isn’t expected by the HTML parser. In that case, the encoded bytes get processed.

Examples:

from unstructured.cleaners.core import bytes_string_to_string

text = "Hello ð\x9f\x98\x80"
# The output should be "Hello 😀"
bytes_string_to_string(text, encoding="utf-8")


from unstructured.cleaners.core import bytes_string_to_string
from unstructured.partition.html import partition_html

text = """\n<html charset="utf-8"><p>Hello 😀</p></html>"""
elements = partition_html(text=text)
elements[0].apply(bytes_string_to_string)
# The output should be "Hello 😀"
elements[0].text

For more information about the bytes_string_to_string function, you can check the source code here.

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clean

Cleans a section of text with options including removing bullets, extra whitespace, dashes and trailing punctuation. Optionally, you can choose to lowercase the output.

Options:

  • Applies clean_bullets if bullets=True.

  • Applies clean_extra_whitespace if extra_whitespace=True.

  • Applies clean_dashes if dashes=True.

  • Applies clean_trailing_punctuation if trailing_punctuation=True.

  • Lowercases the output if lowercase=True.

Examples:

from unstructured.cleaners.core import clean

# Returns "an excellent point!"
clean("● An excellent point!", bullets=True, lowercase=True)

# Returns "ITEM 1A: RISK FACTORS"
clean("ITEM 1A:     RISK-FACTORS", extra_whitespace=True, dashes=True)

For more information about the clean function, you can check the source code here.

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clean_bullets

Removes bullets from the beginning of text. Bullets that do not appear at the beginning of the text are not removed.

Examples:

from unstructured.cleaners.core import clean_bullets

# Returns "An excellent point!"
clean_bullets("● An excellent point!")

# Returns "I love Morse Code! ●●●"
clean_bullets("I love Morse Code! ●●●")

For more information about the clean_bullets function, you can check the source code here.

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clean_dashes

Removes dashes from a section of text. Also handles special characters such as \u2013.

Examples:

from unstructured.cleaners.core import clean_dashes

# Returns "ITEM 1A: RISK FACTORS"
clean_dashes("ITEM 1A: RISK-FACTORS\u2013")

For more information about the clean_dashes function, you can check the source code here.

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clean_non_ascii_chars

Removes non-ascii characters from a string.

Examples:

from unstructured.cleaners.core import clean_non_ascii_chars

text = "\x88This text contains ®non-ascii characters!●"

# Returns "This text contains non-ascii characters!"
clean_non_ascii_chars(text)

For more information about the clean_non_ascii_chars function, you can check the source code here.

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clean_ordered_bullets

Remove alphanumeric bullets from the beginning of text up to three “sub-section” levels.

Examples:

from unstructured.cleaners.core import clean_ordered_bullets

# Returns "This is a very important point"
clean_ordered_bullets("1.1 This is a very important point")

# Returns "This is a very important point ●"
clean_ordered_bullets("a.b This is a very important point ●")

For more information about the clean_ordered_bullets function, you can check the source code here.

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clean_postfix

Removes the postfix from a string if they match a specified pattern.

Options:

  • Ignores case if ignore_case is set to True. The default is False.

  • Strips trailing whitespace is strip is set to True. The default is True.

Examples:

from unstructured.cleaners.core import clean_postfix

text = "The end! END"

# Returns "The end!"
clean_postfix(text, r"(END|STOP)", ignore_case=True)

For more information about the clean_postfix function, you can check the source code here.

​
clean_prefix

Removes the prefix from a string if they match a specified pattern.

Options:

  • Ignores case if ignore_case is set to True. The default is False.

  • Strips leading whitespace is strip is set to True. The default is True.

Examples:

from unstructured.cleaners.core import clean_prefix

text = "SUMMARY: This is the best summary of all time!"

# Returns "This is the best summary of all time!"
clean_prefix(text, r"(SUMMARY|DESCRIPTION):", ignore_case=True)

For more information about the clean_prefix function, you can check the source code here.

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clean_trailing_punctuation

Removes trailing punctuation from a section of text.

Examples:

from unstructured.cleaners.core import clean_trailing_punctuation

# Returns "ITEM 1A: RISK FACTORS"
clean_trailing_punctuation("ITEM 1A: RISK FACTORS.")

For more information about the clean_trailing_punctuation function, you can check the source code here.

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group_broken_paragraphs

Groups together paragraphs that are broken up with line breaks for visual or formatting purposes. This is common in .txt files. By default, group_broken_paragraphs groups together lines split by \n. You can change that behavior with the line_split kwarg. The function considers \n\n to be a paragraph break by default. You can change that behavior with the paragraph_split kwarg.

Examples:

from unstructured.cleaners.core import group_broken_paragraphs

text = """The big brown fox
was walking down the lane.

At the end of the lane, the
fox met a bear."""

group_broken_paragraphs(text)


import re
from unstructured.cleaners.core import group_broken_paragraphs

para_split_re = re.compile(r"(\s*\n\s*){3}")

text = """The big brown fox

was walking down the lane.


At the end of the lane, the

fox met a bear."""

group_broken_paragraphs(text, paragraph_split=para_split_re)

For more information about the group_broken_paragraphs function, you can check the source code here.

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remove_punctuation

Removes ASCII and unicode punctuation from a string.

Examples:

from unstructured.cleaners.core import remove_punctuation

# Returns "A lovely quote"
remove_punctuation("“A lovely quote!”")

For more information about the remove_punctuation function, you can check the source code here.

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replace_unicode_quotes

Replaces unicode quote characters such as \x91 in strings.

Examples:

from unstructured.cleaners.core import replace_unicode_quotes

# Returns "“A lovely quote!”"
replace_unicode_characters("\x93A lovely quote!\x94")

# Returns ""‘A lovely quote!’"
replace_unicode_characters("\x91A lovely quote!\x92")

For more information about the replace_unicode_quotes function, you can check the source code here.

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translate_text

The translate_text cleaning functions translates text between languages. translate_text uses the Helsinki NLP MT models from transformers for machine translation. Works for Russian, Chinese, Arabic, and many other languages.

Parameters:

  • text: the input string to translate.

  • source_lang: the two letter language code for the source language of the text. If source_lang is not specified, the language will be detected using langdetect.

  • target_lang: the two letter language code for the target language for translation. Defaults to "en".

Examples:

from unstructured.cleaners.translate import translate_text

# Output is "I'm a Berliner!"
translate_text("Ich bin ein Berliner!")

# Output is "I can also translate Russian!"
translate_text("Я тоже можно переводать русский язык!", "ru", "en")

For more information about the translate_text function, you can check the source code here.