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
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

Khalid Saifullah 37 Sep 05, 2022
Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch

N-Grammer - Pytorch Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch Install $ pip install n-grammer-pytorch Usage

Phil Wang 66 Dec 29, 2022
Twitter-NLP-Analysis - Twitter Natural Language Processing Analysis

Twitter-NLP-Analysis Business Problem I got last @turk_politika 3000 tweets with

Çağrı Karadeniz 7 Mar 12, 2022
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (SVS & TTS); AAAI 2022

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism This repository is the official PyTorch implementation of our AAAI-2022 paper, in

Jinglin Liu 829 Jan 07, 2023
Search with BERT vectors in Solr and Elasticsearch

Search with BERT vectors in Solr and Elasticsearch

Dmitry Kan 123 Dec 29, 2022
Python package for performing Entity and Text Matching using Deep Learning.

DeepMatcher DeepMatcher is a Python package for performing entity and text matching using deep learning. It provides built-in neural networks and util

461 Dec 28, 2022
An attempt to map the areas with active conflict in Ukraine using open source twitter data.

Live Action Map (LAM) An attempt to use open source data on Twitter to map areas with active conflict. Right now it is used for the Ukraine-Russia con

Kinshuk Dua 171 Nov 21, 2022
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio

Vasileios Lioutas 28 Dec 07, 2022
Implementation for paper BLEU: a Method for Automatic Evaluation of Machine Translation

BLEU Score Implementation for paper: BLEU: a Method for Automatic Evaluation of Machine Translation Author: Ba Ngoc from ProtonX BLEU score is a popul

Ngoc Nguyen Ba 6 Oct 07, 2021
Rhasspy 673 Dec 28, 2022
Code for Text Prior Guided Scene Text Image Super-Resolution

Code for Text Prior Guided Scene Text Image Super-Resolution

82 Dec 26, 2022
Python Implementation of ``Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT'' (Findings of ACL: ACL 2021)

BERT-for-Surprisal Python Implementation of ``Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT'' (Findings

7 Dec 05, 2022
A full spaCy pipeline and models for scientific/biomedical documents.

This repository contains custom pipes and models related to using spaCy for scientific documents. In particular, there is a custom tokenizer that adds

AI2 1.3k Jan 03, 2023
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition

SEW (Squeezed and Efficient Wav2vec) The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speec

ASAPP Research 67 Dec 01, 2022
Tool which allow you to detect and translate text.

Text detection and recognition This repository contains tool which allow to detect region with text and translate it one by one. Description Two pretr

Damian Panek 176 Nov 28, 2022
Code for the paper "Flexible Generation of Natural Language Deductions"

Code for the paper "Flexible Generation of Natural Language Deductions"

Kaj Bostrom 12 Nov 11, 2022
Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

FNet: Mixing Tokens with Fourier Transforms Pytorch implementation of Fnet : Mixing Tokens with Fourier Transforms. Citation: @misc{leethorp2021fnet,

Rishikesh (ऋषिकेश) 217 Dec 05, 2022
A fast, efficient universal vector embedding utility package.

Magnitude: a fast, simple vector embedding utility library A feature-packed Python package and vector storage file format for utilizing vector embeddi

Plasticity 1.5k Jan 02, 2023
Implementation of some unbalanced loss like focal_loss, dice_loss, DSC Loss, GHM Loss et.al

Implementation of some unbalanced loss for NLP task like focal_loss, dice_loss, DSC Loss, GHM Loss et.al Summary Here is a loss implementation reposit

121 Jan 01, 2023
Chatbot for the Chatango messaging platform

BroiestBot The baddest bot in the game right now. Uses the ch.py framework for joining Chantango rooms and responding to user messages. Commands If a

Todd Birchard 3 Jan 17, 2022