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
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