suman101112 Goto Github PK

basic-ds

cmmt

Gated conv neural network for code-mixed machine translation

code-mixed-tod-medical-dataset

extracting-a-challenging-dataset-from-dravidian-code-mixed-hate-speech-detection-models

Contains the implementation of paper "A Method for Extracting Challenging Dataset for Deep Study of Dravidian Code-Mixed Automated Hate Speech Detection Models"

fire-2020-dravidian-codemix

A sentiment analysis shared task on the Tamil and Malayalam code-mixed text

gaussian-hidden-markov-model

hasoc-fire-2020

Bert for multilingual hate speech and offensive content identification on english, german and hindi datasets

hate-speech-detection-on-code-mixed-dataset-using-a-fusion-of-custom-and-pre-trained-models-with-pro

With the increase in user-generated content on social media networks, hate speech and offensive language content are also increasing. From the perspective of computer science, automatic detection of such hate speech and offensive language content is an interesting problem to solve. The natural language community has taken a step to identify such content via automated hate speech and offensive content detection. The hate speech content is generated mostly on social media, and automatic hate speech and offensive language detection face many challenges due to non-standard spelling and grammar variations. Specifically, in a multilingual community, the hate content would be in code-mixed form, making the task further challenging. In this article, we propose a model for code-mixed hate speech detection. This model embeds the knowledge from both user-trained and multilingual pre-trained models. The proposed method also calculates the profanity word list and augments it. Experimental results on code-mixed hate speech and offensive language detection benchmarks show that our method outperforms the existing baselines.

nlp

online-hate-speech-recog

An online hate speech recognition system.

project

project

pytorch-seq2seq

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

techdofication-with-bert-cnn

technical domain identification with multilingual bert and convolutional models.