Generating new names based on trends in data using GPT2 (Transformer network)

Overview

MLOpsNameGenerator

Overall Goal

The goal of the project is to develop a model that is capable of creating Pokémon names based on its description, using principles orginization and version control, reproduceability, etc.

Framework

The framework we use is Transformer. We intend to use the Natural Language Processing (NLP) part of the framework. The model we are going to use is GPT-2 doing finetuning over it so we can specialize it over our precise problem.

Data

Initially, we pretend to use the description of each Pokémon using the PokéAPI, which is a RESTful API linked to a database of details of Pokémon.

Relevant querys to the API:

  • Obtain the list of all Pokémon:

    https://pokeapi.co/api/v2/pokedex/national
    
  • Get the description of each Pokémon:

    https://pokeapi.co/api/v2/pokemon-species/{PKMN_SPECIE_NUMBER}
    

Commands

  • make requirements: Installs all requirements from requirements.txt.
  • make devrequirements: Installs additional dependencies for development.
  • make datafolders: Creates folders for the data in the project (data/raw, data/processed, data/external and data/interim)
  • make data: Downloads and process the data.
  • make clean: Deletes compiled Python files
  • make train: Trains model
  • make deploy: Uploads the updates cleaning and fixing style

RoadMap

Week 1

Goal of this week is to setup the project. This includes: Setting up the makefile, setting up the first model and a script for training the model, fetching the data required to train the models, setting up hydra to test with hyperparameters and setting up docker for containerization.

Alba Alejandro Gustav
Data obtaining and processing Test usage of GPT-2 Develop model using GPT-2
Hydra and config. files Review and change structure of the train script -
Add wandb to log training progress Do predict script -

Week 2

Week3

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Cites and references

PokéAPI

Movie name generation with GPT-2

Huggingface transformers

Huggingface notebooks

NameKrea An AI That Generates Domain Names


DTU Course 02476 - Machine Learning Operations

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Owner
Gustav Lang Moesmand
Gustav Lang Moesmand
FactSumm: Factual Consistency Scorer for Abstractive Summarization

FactSumm: Factual Consistency Scorer for Abstractive Summarization FactSumm is a toolkit that scores Factualy Consistency for Abstract Summarization W

devfon 83 Jan 09, 2023
null

CP-Cluster Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection, Instance Segme

Yichun Shen 41 Dec 08, 2022
Subtitle Workshop (subshop): tools to download and synchronize subtitles

SUBSHOP Tools to download, remove ads, and synchronize subtitles. SUBSHOP Purpose Limitations Required Web Credentials Installation, Configuration, an

Joe D 4 Feb 13, 2022
Sample data associated with the Aurora-BP study

The Aurora-BP Study and Dataset This repository contains sample code, sample data, and explanatory information for working with the Aurora-BP dataset

Microsoft 16 Dec 12, 2022
⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x using fastT5.

Reduce T5 model size by 3X and increase the inference speed up to 5X. Install Usage Details Functionalities Benchmarks Onnx model Quantized onnx model

Kiran R 399 Jan 05, 2023
Behavioral Testing of Clinical NLP Models

Behavioral Testing of Clinical NLP Models This repository contains code for testing the behavior of clinical prediction models based on patient letter

Betty van Aken 2 Sep 20, 2022
Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

PythonTextObfuscator Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense. Requi

2 Aug 29, 2022
Perform sentiment analysis on textual data that people generally post on websites like social networks and movie review sites.

Sentiment Analyzer The goal of this project is to perform sentiment analysis on textual data that people generally post on websites like social networ

Madhusudan.C.S 53 Mar 01, 2022
Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

Sentiment Analysis Project This project contains two sentiment analysis programs for Hotel Reviews using a Hotel Reviews dataset from Datafiniti. The

Simran Farrukh 0 Mar 28, 2022
An implementation of the Pay Attention when Required transformer

Pay Attention when Required (PAR) Transformer-XL An implementation of the Pay Attention when Required transformer from the paper: https://arxiv.org/pd

7 Aug 11, 2022
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind

Jonas Djondo 1 Nov 18, 2021
Pre-training BERT masked language models with custom vocabulary

Pre-training BERT Masked Language Models (MLM) This repository contains the method to pre-train a BERT model using custom vocabulary. It was used to p

Stella Douka 14 Nov 02, 2022
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries

Moment-DETR QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L. Berg, Mohit Bansal For dataset de

Jie Lei 雷杰 133 Dec 22, 2022
This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

Word-Level Coreference Resolution This is a repository with the code to reproduce the experiments described in the paper of the same name, which was a

79 Dec 27, 2022
This project consists of data analysis and data visualization (done using python)of all IPL seasons from 2008 to 2019 and answering the most asked questions about the IPL.

IPL-data-analysis This project consists of data analysis and data visualization of all IPL seasons from 2008 to 2019 and answering the most asked ques

Sivateja A T 2 Feb 08, 2022
Automatically search Stack Overflow for the command you want to run

stackshell Automatically search Stack Overflow (and other Stack Exchange sites) for the command you want to ru Use the up and down arrows to change be

circuit10 22 Oct 27, 2021
Text Analysis & Topic Extraction on Android App user reviews

AndroidApp_TextAnalysis Hi, there! This is code archive for Text Analysis and Topic Extraction from user_reviews of Android App. Dataset Source : http

Fitrie Ratnasari 1 Feb 14, 2022
TweebankNLP - Pre-trained Tweet NLP Pipeline (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Models + Tweebank-NER

TweebankNLP This repo contains the new Tweebank-NER dataset and off-the-shelf Twitter-Stanza pipeline for state-of-the-art Tweet NLP, as described in

Laboratory for Social Machines 84 Dec 20, 2022
BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

OpenBMB 377 Jan 02, 2023
मराठी भाषा वाचविण्याचा एक प्रयास. इंग्रजी ते मराठीचा शब्दकोश. An attempt to preserve the Marathi language. A lightweight and ad free English to Marathi thesaurus.

For English, scroll down मराठी शब्द मराठी भाषा वाचवण्यासाठी मी हा ओपन सोर्स प्रोजेक्ट सुरू केला आहे. माझ्या मते, आपली भाषा हळूहळू आणि कोणाचाही लक्षात

मुक्त स्त्रोत 20 Oct 11, 2022