This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.

Overview

Last Commit Last Commit Stars Badge Forks Badge Size Pull Requests Badge Issues Badge Language MIT License

binder colab

Python_Natural_Language_Processing

This repository contains tutorials on important topics related to Natural Language Processing (NPL).

No. Name
01 01_Tokenization_NLP
02 02_Stemming_Lemmatization
03 03_StopWords
04 04_Vocabulary_and_Matching
05 05_POS_Basics
06 06_Named_Entity_Recognition
07 07_Sentence_Segmentation
08 08_Stemming
09 09_BagofWords_N_Gram
10 10_TF_IFD

These are read-only versions. However you can Run ▶ all the codes online by clicking here ➞ binder 020_Road_Detection

Frequently asked questions

How can I thank you for writing and sharing this tutorial? 🌷

You can Star Badge and Fork Badge Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click ➞ ✰ Star and ⵖ Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.


How can I read this tutorial without an Internet connection? GIF

  1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.

    Download ZIP

  2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

  3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

    Kernel > Restart & Clear Output

This will clear all the outputs and now you can understand each statement and learn interactively.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.


Authors ✍️

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome 🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub PNG and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a star.


Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.

Owner
Milaan Parmar / Милан пармар / _米兰 帕尔马
💼👨‍🏫 Researcher • Python | MATLAB | R • Build🤯 → Test🤞 → Debug✔️ “Change Is the Only Constant in Life" ➶
Milaan Parmar / Милан пармар / _米兰 帕尔马
Summarization module based on KoBART

KoBART-summarization Install KoBART pip install git+https://github.com/SKT-AI/KoBART#egg=kobart Requirements pytorch==1.7.0 transformers==4.0.0 pytor

seujung hwan, Jung 148 Dec 28, 2022
Google AI 2018 BERT pytorch implementation

BERT-pytorch Pytorch implementation of Google AI's 2018 BERT, with simple annotation BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers f

Junseong Kim 5.3k Jan 07, 2023
Yet Another Sequence Encoder - Encode sequences to vector of vector in python !

Yase Yet Another Sequence Encoder - encode sequences to vector of vectors in python ! Why Yase ? Yase enable you to encode any sequence which can be r

Pierre PACI 12 Aug 19, 2021
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Highlights The strongest performances Tracker

Multimedia Research 485 Jan 04, 2023
Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

XLM-EMO: Multilingual Emotion Prediction in Social Media Text Abstract Detecting emotion in text allows social and computational scientists to study h

MilaNLP 35 Sep 17, 2022
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective This is the official code base for our ICLR 2021 paper

AI Secure 71 Nov 25, 2022
An example project using OpenPrompt under pytorch-lightning for prompt-based SST2 sentiment analysis model

pl_prompt_sst An example project using OpenPrompt under the framework of pytorch-lightning for a training prompt-based text classification model on SS

Zhiling Zhang 5 Oct 21, 2022
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

537 Jan 05, 2023
Open-source offline translation library written in Python. Uses OpenNMT for translations

Open source neural machine translation in Python. Designed to be used either as a Python library or desktop application. Uses OpenNMT for translations and PyQt for GUI.

Argos Open Tech 1.6k Jan 01, 2023
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 464 Jan 04, 2023
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

0 Feb 13, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
Creating a chess engine using GPT-3

GPT3Chess Creating a chess engine using GPT-3 Code for my article : https://towardsdatascience.com/gpt-3-play-chess-d123a96096a9 My game (white) vs GP

19 Dec 17, 2022
This is my reading list for my PhD in AI, NLP, Deep Learning and more.

This is my reading list for my PhD in AI, NLP, Deep Learning and more.

Zhong Peixiang 156 Dec 21, 2022
State-of-the-art NLP through transformer models in a modular design and consistent APIs.

Trapper (Transformers wRAPPER) Trapper is an NLP library that aims to make it easier to train transformer based models on downstream tasks. It wraps h

Open Business Software Solutions 42 Sep 21, 2022
Sinkhorn Transformer - Practical implementation of Sparse Sinkhorn Attention

Sinkhorn Transformer This is a reproduction of the work outlined in Sparse Sinkhorn Attention, with additional enhancements. It includes a parameteriz

Phil Wang 217 Nov 25, 2022
IEEEXtreme15.0 Questions And Answers

IEEEXtreme15.0 Questions And Answers IEEEXtreme is a global challenge in which teams of IEEE Student members – advised and proctored by an IEEE member

Dilan Perera 15 Oct 24, 2022
Grading tools for Advanced NLP (11-711)Grading tools for Advanced NLP (11-711)

Grading tools for Advanced NLP (11-711) Installation You'll need docker and unzip to use this repo. For docker, visit the official guide to get starte

Hao Zhu 2 Sep 27, 2022
原神抽卡记录数据集-Genshin Impact gacha data

提要 持续收集原神抽卡记录中 可以使用抽卡记录导出工具导出抽卡记录的json,将json文件发送至[email protected],我会在清除个人信息后

117 Dec 27, 2022
Community and sentiment analysis based on tweets

The project has set itself the goal of analyzing the thoughts and interaction of Italian users through the social posts expressed through the Twitter platform on the day of the entry into force of th

3 Nov 17, 2022