Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Datasets, SOTA results of every fields of Chinese NLP
Tools and recipes to train deep learning models and build services for NLP tasks such as text classification, semantic search ranking and recall fetching, cross-lingual information retrieval, and question answering etc.
📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
Deep-Learning Model Exploration and Development for NLP
NLPGym - A toolkit to develop RL agents to solve NLP tasks.
Repo for Applied Text Mining in Python (coursera) by University of Michigan
Data and code for our paper "Exploring and Predicting Transferability across NLP Tasks", to appear at EMNLP 2020.
Contextual LSTM for NLP tasks like word prediction and word embedding creation for Deep Learning
Information Extraction System can perform NLP tasks like Named Entity Recognition, Sentence Simplification, Relation Extraction etc.
Given a pair of questions on Quora, the NLP task aims to classify whether two given questions are duplicates or not. The underlying idea is to identify whether the pair of questions have the same intent though they might have been structured differently .
Common Libraries developed in "PyTorch" for different NLP tasks. Sentiment Analysis, NER, LSTM-CRF, CRF, Semantic Parsing
Repository to track the state of the art research progress in Bengali natural language processing for most common task
Examples of Natural Language Processing. Created at the Univeristy as the project within Natural Language Processing classes in 2015. The purpose of those examples was to get to know NLP techniques and using them in sample tasks like for example QA or polish surname variations app.
I’ve written here an excerpt of model file of training a bot, implemented on a COVID-19 dataset. Attached in the document, are views of how well the model classifies input from user in relation to ones curtained in the dataset.
All about Natural Language Processing techniques.
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