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germanetpy

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.

hatespeech

inqugen

Intelligent Question Generation System

lazynlp

Library to scrape and clean web pages to create massive datasets.

legal-entity-recognition

A Dataset of German Legal Documents for Named Entity Recognition

lingfeat

LingFeat - A Comprehensive Linguistic Features Extraction ToolKit for Readability Assessment

machine_learning_examples

A collection of machine learning examples and tutorials.

malaya

Natural Language Toolkit for bahasa Malaysia, https://malaya.readthedocs.io/

multiple-choice-question-generation-t5-and-text2text

Question Generation using Google T5 and Text2Text

ner_tensorflow2.2.0

neural-question-generation-based-on-reading-comprehension

Seq2Seq architectures: BERT+GRU, BART, T5 are implemented for Neural Question Generation (NQG) on SQuAD datasets

nlpaug

Data augmentation for NLP

odenet

Open German WordNet

pawraphrase_public

Deep Learning Training script implemented in Python based on Deep Learning model T5 --- Teaching Google's T5 how to paraphrase sentences through fine tuning with PAWS dataset.

pdf_text

Taking tables in a PDF document and using PyPDF2 and re libraries to convert to csv format

pdflayouttextstripper

Converts a pdf file into a text file while keeping the layout of the original pdf. Useful to extract the content from a table in a pdf file for instance. This is a subclass of PDFTextStripper class (from the Apache PDFBox library).

pretraining-t5-pytorch-lightning

Collection of scripts to pretrain T5 in unsupervised text, using PyTorch Lightning. CORD-19 pretraining provided as example.

pygermanet

GermaNet API for Python

pyqtgraph

Fast data visualization and GUI tools for scientific / engineering applications

pytorch-lightning-imdb-bert

q-ai-frag

German NLP Question Generator (work in progress)

questgen.ai

Question generation using state-of-the-art Natural Language Processing algorithms

questgende

question-answer-generation

Question-answer generation from text

question-generation

Neural text-to-text question generation

question-generation-1

Generating multiple choice questions from text using Machine Learning.

question-generation-paper-list

A summary of must-read papers for Neural Question Generation (NQG)

question_generation

Neural question generation using transformers

question_generation_de