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import argparse |
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import json |
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import re |
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import os |
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import logging |
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import unicodedata |
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import multiprocessing |
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from functools import partial |
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from langdetect import detect_langs |
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from tqdm import tqdm |
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from emoji import EMOJI_DATA |
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import fastchat_validate |
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import deduplicate |
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def detect_language(text): |
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try: |
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detected_langs = detect_langs(text) |
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lang_code = detected_langs[0].lang |
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except Exception: |
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lang_code = "unknown" |
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return lang_code |
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def contains_unwanted_words(text): |
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unwanted_words = [ |
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"text-based AI language model", |
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"domestic violence", |
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"please refrain", |
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"derogatory", |
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"inappropriate", |
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"offensive", |
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"racism", |
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"racist", |
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"racial", |
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"discriminate", |
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"discriminatory", |
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"discrimination", |
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"sexist", |
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"sexism", |
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"unacceptable", |
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"inclusive workplace", |
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"lgbt", |
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"morals", |
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"ethics", |
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"ethical", |
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"legality", |
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"illegal", |
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"illegality", |
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"hateful", |
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"harmful", |
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"it is never okay", |
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"It is important to", |
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"It's important to", |
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"real-world consequences", |
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"hate speech", |
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"glorify", |
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"not be appropriate", |
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"supremacist", |
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"extremist", |
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"responsible AI", |
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"AI principles", |
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"AI assistant", |
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"an AI language", |
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"ableist", |
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"hurtful", |
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"gender stereotype", |
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"gender inequality", |
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"underrepresentation", |
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"safe spaces", |
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"gender-based", |
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"inclusivity", |
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"feminist", |
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"feminism", |
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"transgender", |
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"empowerment", |
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"communist", |
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"capitalism", |
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"stereotypes", |
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"biases", |
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"bias", |
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"Microaggression", |
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"prioritize human safety", |
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"as a language model", |
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"as an AI language model", |
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"As a large language model", |
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"As an AI", |
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"ethical principles", |
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"consensual", |
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"it is not appropriate", |
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"it's not appropriate", |
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"I cannot fulfill your request", |
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"harmful to human beings", |
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"ethical guidelines", |
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"my guidelines", |
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"prioritize user safety", |
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"adhere to ethical guidelines", |
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"harmful consequences", |
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"potentially harmful", |
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"dangerous activities", |
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"promote safety", |
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"well-being of all users", |
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"responsible information sharing", |
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"jeopardize the safety", |
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"illegal actions or intentions", |
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"undermine the stability", |
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"promote the well-being", |
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"illegal activities or actions", |
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"adherence to the law", |
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"potentially be harmful", |
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"illegal substances or activities", |
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"committed to promoting", |
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"safe information", |
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"lawful information", |
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"cannot provide guidance", |
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"cannot provide information", |
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"unable to offer assistance", |
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"cannot engage in discussions", |
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"programming prohibits", |
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"follow ethical guidelines", |
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"ensure the safety", |
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"involves an illegal subject", |
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"prioritize safety", |
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"illegal subject", |
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"prioritize user well-being", |
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"cannot support or promote", |
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"activities that could harm", |
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"pose a risk to others", |
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"against my programming", |
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"activities that could undermine", |
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"potentially dangerous", |
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"not within the scope", |
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"designed to prioritize safety", |
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"not able to provide", |
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"maintain user safety", |
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"adhere to safety guidelines", |
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"dangerous or harmful", |
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"cannot provide any information", |
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"focus on promoting safety", |
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"an AI language model you don't have", |
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"As an AI language model, I cannot", |
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"As an AI language model, I do not", |
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"As an AI language model, I am not able", |
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"As an AI language model, I don't have personal", |
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"I am an AI language model and do not", |
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"However, it is important to use any code or information provided responsibly and within legal and ethical boundaries.", |
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"As an AI language model, I don't have", |
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"As an AI language model, I am only able", |
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"AI language model and I do not", |
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"As an AI language model, I cannot modify", |
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"As an AI language model, I do not", |
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"I know as an AI language model you don't have", |
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"as an AI language model, you cannot", |
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"I'm sorry, but as an AI language model", |
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"As an AI language model, I don't have", |
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"Unfortunately, I cannot provide", |
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"I'm sorry, I cannot", |
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"I'm sorry, I cannot generate", |
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"AI cannot create or program", |
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"I'm afraid I cannot create", |
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"you cannot create an", |
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"it operates ethically and is", |
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"had an ethical system", |
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"Ensuring the ethical", |
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"and ethical sourcing", |
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"are from ethical", |
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"legal and ethical", |
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"engage in unethical", |
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"unethical or aggressive", |
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"unethical business", |
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"como modelo de lenguaje AI", |
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"Lo siento, como modelo de lenguaje", |
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"no puedo proporcionar", |
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"pero debido a mi capacidad para generar c\u00f3digos complejos y completos es limitado", |
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"Lo siento, pero no puedo", |
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"Lo siento, pero como modelo de lenguaje, no puedo proporcionar", |
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"Lo siento, como modelo de lenguaje, no tengo", |
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"Lo siento, debe haber habido una confusi\u00f3n", |
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"Lo siento, como modelo de lenguaje, no puedo realizar", |
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"Lo siento, soy un modelo de lenguaje y no tengo la capacidad de generar", |
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"Lamento no poder proporcionarte el c\u00f3digo", |
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"Desculpe-me, mas a linguagem vulgar e ofensiva", |
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"apropriada em nenhum contexto", |
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"Como modelo de linguagem", |
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"Como um modelo de linguagem, n\u00e3o tenho a capacidade de", |
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"I cannot assist", |
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"prioritize ethical", |
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"respectful", |
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"morally", |
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"I'm sorry,", |
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"I'm an", |
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"I am an", |
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"I'm an AI" , |
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"I am an AI", |
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"my purpose", |
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"filter_bad_language", |
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"filter\_bad\_language", |
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"entertainment purposes", |
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"purely hypothetical", |
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"not a human", |
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"I am an AI", |
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"cannot provide", |
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"can't provide", |
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"won't provide", |
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"not provide", |
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"worth noting", |
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"cause harm", |
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"a language model", |
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"keep in mind", |
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"unethical", |
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"bad language", |
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"the words ****", |
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"bad_language", |
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"certainly not", |
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"complying", |
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"comply", |
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"I cannot", |
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"my main goal", |
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"As a machine", |
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"I don't have the ability", |
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"I am here to assist", |
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"my purpose is to ", |
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"my knowledge cutoff", |
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"my knowledge cut off", |
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"September 2021", |
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"regulations", |
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"not be suitable", |
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"I apologize, but", |
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"It is not possible", |
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"controversial", |
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"my programming", |
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"ethically", |
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"it is important to", |
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"Please note", |
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"sensitive topic", |
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"not acceptable", |
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"It is important for", |
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"divisive", |
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"not appropriate", |
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"our values", |
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"f\*cking", |
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"F\*ck", |
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"sh\*t", |
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"diversity and", |
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"diversity and inclusion", |
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"values diversity", |
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"social responsibility", |
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"environmental, social, and governance", |
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" ESG ", |
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"against women", |
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"problematic history", |
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"diversity", |
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"*This chat conversation is shared from", |
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"*This conversation is shared from", |
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"I am a computer program", |
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"I do not have the ability", |
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"condone", |
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"I am a machine learning model", |
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"As an artificial intelligence", |
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"I am a friendly and helpful AI", |
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"I am a highly advanced", |
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"I'm sorry", |
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"I am sorry", |
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"As a language learning model", |
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"As an experienced language model", |
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"I am just a computer program", |
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"As a computer program,", |
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"As a text-based language model,", |
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"I am essentially a computer program", |
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"As your dedicated AI language model", |
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"As a hypothetical AI", |
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"As a neutral AI", |
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"I don't have feelings", |
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"I don't have emotions", |
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"I do not have personal beliefs or opinions", |
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"not a good idea", |
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"inequities", |
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"gender equality", |
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"mutual understanding", |
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"did not align", |
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"equity and", |
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"is a serious crime", |
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"taken lightly", |
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"criminal behavior", |
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"mental health", |
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"crime", |
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"I apologize", |
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"I apologise", |
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"avec", |
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"wie", |
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"lo siento", |
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"por la", |
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"\\u0", |
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"our platform", |
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"our service", |
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"this platform", |
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"consult a", |
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"contact a", |
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" rape", |
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"sermon", |
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"abuse", |
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"Donald Trump", |
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"Joe Biden", |
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"politic", |
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"religio", |
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" AI ", |
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"Christian", |
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"Bible", |
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"Jesus", |
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" god ", |
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"Jew", |
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"Judaism", |
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"Talmud", |
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"Muslim", |
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"Islam", |
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"Quran", |
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"Muhammad", |
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"Buddhis", |
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"Hindu", |
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"family-friendly", |
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"bully", |
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"I can't", |
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"artificial int", |
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"their bonds", |
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"our bonds", |
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"his bonds", |
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"her bonds", |
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"bond of", |
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"bond between" |
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"bonds of", |
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"bonds between", |
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"Too many requests in", |
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"langage AI", |
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" AI.", |
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"désolé", |
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"D\u00e9sol\u00e9", |
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"Er was eens", |
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"Sprachmodell", |
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"modèle de langage" |
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] |
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for word in unwanted_words: |
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if word.lower() in text.lower(): |
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logging.debug(f"Found unwanted word: {word}") |
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return True |
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return False |
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import re |
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emojis = EMOJI_DATA.keys() |
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def skip(conv, args): |
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if any( |
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sentence["value"] == "" or contains_unwanted_words(sentence["value"]) |
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for sentence in conv["conversations"] |
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): |
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return True |
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text = "".join(sentence["value"] for sentence in conv["conversations"]) |
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if args.nounicode: |
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non_eng_chars = sum(1 for c in text if not c.isascii()) |
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if non_eng_chars > 0: |
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return True |
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for char in text: |
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if args.lang != "all" or args.skip_lang is not None: |
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unicode_category = unicodedata.category(char) |
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if ( |
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unicode_category.startswith(('C', 'P', 'S', 'Z')) |
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or unicode_category == 'Nd' |
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or 'LATIN' in unicodedata.name(char) |
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or char in emojis |
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): |
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continue |
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return False |
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if args.reduce_rep: |
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if any(re.search(r"(\d)\1{8}", sentence["value"]) for sentence in conv["conversations"]): |
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return True |
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return False |
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def filter_conversations(conv, args, bad_ids): |
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return not skip(conv, args) and conv["id"] not in bad_ids |
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def get_file_size_mb(file_path): |
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file_size_bytes = os.path.getsize(file_path) |
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file_size_mb = file_size_bytes / (1024 * 1024) |
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return file_size_mb |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--in-file", type=str, required=True) |
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parser.add_argument("--out-file", type=str, default="") |
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parser.add_argument("--lang", type=str, default="all", |
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choices=["all", "en"]) |
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parser.add_argument("--skip-lang", type=str) |
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parser.add_argument("--reduce-rep", action="store_true") |
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parser.add_argument("--validate", action="store_true") |
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parser.add_argument("--sanitize", action="store_true") |
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parser.add_argument("--bad_ids", type=str, default="") |
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parser.add_argument("--nounicode", action="store_true") |
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parser.add_argument("--log_removals", default=True, action="store_true") |
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parser.add_argument("--deduplicate", default=False, action="store_true") |
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args = parser.parse_args() |
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if(args.validate): |
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data = json.load(open(args.in_file, "r",encoding="utf-8" )) |
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sources = [example["conversations"] for example in data] |
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fastchat_validate.preprocess(sources) |
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print("Validated Dataset") |
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raise SystemExit(0) |
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bad_ids = [] |
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if(args.bad_ids != ""): |
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with open("bad_ids.json", "r") as f: |
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bad_id_json = json.load(f) |
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bad_ids = set(item["id"] for item in bad_id_json) |
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in_file = args.in_file |
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out_file = args.out_file |
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lang = args.lang |
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skip_lang = args.skip_lang |
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reduce_rep = args.reduce_rep |
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log_removals = args.log_removals |
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assert (lang == "all" or skip_lang is None) |
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if out_file == "": |
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out_file = "sharegpt_clean" |
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if lang != "all": |
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out_file += "_" + lang |
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if skip_lang is not None: |
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out_file += "_skip_" + skip_lang |
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if reduce_rep: |
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out_file += "_reduce_rep" |
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out_file += ".json" |
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content = json.load(open(in_file, "r", encoding="utf-8")) |
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num_conv = len(content) |
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|
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if log_removals: |
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removal_log_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'removals.log') |
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open(removal_log_path, 'w').close() |
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logging.basicConfig(filename=removal_log_path, level=logging.DEBUG) |
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else: |
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logging.basicConfig(level=logging.INFO) |
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if(args.sanitize): |
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print('Sanitizing') |
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for entries in tqdm(content, unit='conversations'): |
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for message in entries["conversations"]: |
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if message["from"] == "user": |
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message["from"] = "human" |
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elif message["from"] == "bing" or message["from"] == "chatgpt" or message["from"] == "system": |
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message["from"] = "gpt" |
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print('Analyzing') |
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pool = multiprocessing.Pool() |
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filter_func = partial(filter_conversations, args=args, bad_ids=bad_ids) |
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new_content = list(tqdm(pool.imap(filter_func, content), total=len(content), unit='conversations')) |
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pool.close() |
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pool.join() |
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new_content = [conv for conv, keep in zip(content, new_content) if keep] |
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new_len = len(new_content) |
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print(f"Skipped {num_conv - new_len} conversations") |
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num_conv = new_len |
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if args.deduplicate: |
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print('Deduplicating') |
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new_content = deduplicate.remove_duplicates(new_content) |
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new_len = len(new_content) |
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print(f"Removed {num_conv - new_len} duplicates") |
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num_conv = new_len |
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print(f"return {len(new_content)} out of {len(content)}, start dump ...") |
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json.dump(new_content, open(out_file, "w"), indent=2) |
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print(f'Initial: {get_file_size_mb(in_file):.2f} MB') |
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print(f'Cleaned: {get_file_size_mb(out_file):.2f} MB') |
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