Github Rayaditi Toxic Comment Classification Trained models & code to predict toxic comments on all 3 jigsaw toxic comment challenges. built using ⚡ pytorch lightning and 🤗 transformers. for access to our api, please email us at [email protected]. Deep learning for toxic comment classification. identify and classify toxic online comments. discussing things you care about can be difficult. the threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions.
Github Linjianz Toxic Comment Classification 在pytorch下 采用lstm C Lstm This project is launched for kaggle competition: toxic comment classification challenge — build a multi headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity based hate. Toxic comments classification installing dependencies and loading data [ ] import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from keras.layers import textvectorization, embedding, lstm, bidirectional, dense from keras.models import sequential. In this project, i attempt to solve the problem we have with toxicity on the internet by building a machine learning model that detects these toxic comments. introduction: why toxic comment filtering is important? the internet has become an integral part of modern society and has greatly impacted the way we communicate and access information. Built a multilingual text classification model to predict the probability that a comment is toxic using the data provided by google jigsaw. the data had 4,35,775 text comments in 7 different languages.
Toxic Comment Classification Github Topics Github In this project, i attempt to solve the problem we have with toxicity on the internet by building a machine learning model that detects these toxic comments. introduction: why toxic comment filtering is important? the internet has become an integral part of modern society and has greatly impacted the way we communicate and access information. Built a multilingual text classification model to predict the probability that a comment is toxic using the data provided by google jigsaw. the data had 4,35,775 text comments in 7 different languages. Toxic comment classification – my approach problem statement (in my words) online platforms are full of nasty, toxic comments—stuff that’s not just rude, but can be outright hateful or dangerous. The baseline application consisted of two python scripts, a data cleaner and a data classifier. the data cleaner took the training data as input and created dictionaries of words for the following categories of comments: toxic, severe toxic, insult, obscene, threat and identity hate. no further data cleaning was implemented in this iteration. We start to grasp some idea of what defines each category. we see that the word “nigger” is very used in “identity hate” comments, and for example the word “die” appears in the comments categorized as “threat”. some words like “fuck” are used a lot in most categories, with the exception of the “threat”. Deep learning to identify and classify toxic comments on online forums.
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