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autocorrect

Spelling corrector in python

bert-for-sequence-labeling-and-text-classification

This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.

cornell-conversational-analysis-toolkit

datascience

Repository to maintain all my clutter

emnlp20_arg_attack

emnlp20_prop_extr

fewshotintentdetection

This repository hosts data and code pour the task of Intent Detection in a few-shot learning setup.

google-research

Google Research

intent-recognition-flask-deployment

Deployed a Keras Intent Recognition model, with blending of codes from Intent Recognition tasks, Kaggle competitions, GRU, LSTM, BERT, and SGD models

jamspell

Modern spell checking library - accurate, fast, multi-language

logicnlg

The data and code for ACL2020 paper "Logical Natural Language Generation from Open-Domain Tables"

nlpdeep

NLPDeep - Deep Learning for using NLP tasks. Focus in quality text.

onnxt5

Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

pomo

PoMo: Generating Entity-Specific Post-Modifiers in Context

pungen

A pun generator based on the surprisal principle

spacy-intent-example

intent prediction example on spaCy v3

stride.ai-task-1-intent-detection-on-enron-email-set

Task 1. Intent detection on Enron email set. We define "intent" here to correspond primarily to the categories "request" and "propose". In some cases, we also apply the positive label to some sentences from the "commit" category if they contain datetime, which makes them useful. Detecting the presence of intent in email is useful in many applications, e.g., machine mediation between human and email. The dataset contains parsed sentences from the email along with their intent (either 'yes' or 'no'). You need to build a learning model which detects whether a given sentence has intent or not. Its a 2-class classification problem. Although its not required you can refer this paper for more information on the dataset : Cohen, William W., Vitor R. Carvalho, and Tom M. Mitchell. "Learning to Classify Email into``Speech Acts''." EMNLP. 2004. Find the train and test dataset in enron.zip. Tip: Try to add feature engineering into your model. Simple baseline with logistic regression gives 71% accuracy.

targer

A web application tagging and retrieval of arguments in text

tenseflow

Change the tense of any text

tesis_chatbot

text-formality-classifier

This is a text formality classifier project that examines how different machine learning models perform in classifying a text as formal or informal. Collaborators: Yonglin Wang, Xiaoyu Lu

textattack

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

textcomplexityanalyzercm

Text complexity analyzer based on Coh metrix 3.0

tito-joker

A humorous AI that uses state-of-the-art deep learning to tell jokes