SCheck Blocklist

Python package enabling seamless integration with the SCheck Blocklist datasets

Actually Version: 1.1.0
Our Websites: scheck-blocklist.vercel.app

Installation

To download a library from PyPI via pip just use the command:

pip install scheckbl

Quick Start

Below is sample code showing the syntax:

from scheckbl import blocklist

c = blocklist.check("phrases", "vulgarisms", "f*ck")        # output: True
f = blocklist.find("phrases", "vulgarisms", "I f*ck you")    # output: True
g = blocklist.get("urls", "ads")                              # output: list
s = blocklist.similar("phrases", "vulgarisms", "f*uck", 0.3) # output: json

API Reference

`blocklist.check`

Checks whether a given keyword is present in the specified blocklist.

type_name: category group, e.g., "phrases" or "urls"
category: sublist name, e.g., "vulgarisms", "ads"
keyword: the word or phrase to check
Returns: True if keyword is found on the blocklist; otherwise, False

📌 Note: If keyword is a URL, the last segment (after the last /) is automatically extracted before checking.

`blocklist.find`

Checks whether any part of the keyword string contains entries from the blocklist.

type_name: same as above
category: same as above
keyword: full sentence or input string to search through
Returns: True if any blocklisted entry is found; otherwise, False

📌 Note: Just like check(), URLs will be trimmed to their final segment.

`blocklist.get`

Retrieves the full blocklist for the given category.

type_name: e.g. "phrases"
category: e.g. "ads"
filename (optional): specific file to load
regex (optional): regex pattern to filter results
Returns: a list of strings representing the full blocklist, each entry as a separate line

`blocklist.similar`

Finds entries in the blocklist that are similar to the input phrase.

type_name: e.g., "phrases"
category: e.g., "vulgarisms"
phrase: the string to compare
threshold: similarity threshold from 0.0 to 1.0 (default: 0.6)
Returns: list of tuples (entry: str, similarity: float) representing blocklist entries with similarity scores

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