classy-core 0.3.2.dev20220910

A powerful tool to train and use your classification models.

A PyTorch-based library for fast prototyping and sharing of deep neural network models.

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Quick Links

• ↗️ Website
• 📓 Documentation
• 💻 Template
• 🔦 Examples
• ✋ Contributing Guidelines
• 🌙 Nightly Releases

In this README

• 🚀 Getting Started
• ⚡ Installation
• ⌨ Running Classy
  • classy train
  • classy predict
  • classy evaluate
  • classy serve
  • classy demo
  • classy describe
  • classy upload
  • classy download
  • Enable classy shell completion
• 🤔 Issues
• ❤️ Contributions

Getting Started using classy

If this is your first time meeting classy, don't worry! We have plenty of resources to help you learn how it works and what it can do for you.

For starters, have a look at our amazing website and our documentation!

If you want to get your hands dirty right away, have a look at our base classy template. Also, we have a few examples that you can look at to get to know classy!

Installation

For a more in-depth installation guide (covering also installing from source and through docker), please visit our installation page.

If you are using one of our templates, there is a handy setup.sh script you can use that will execute the commands to create the environment and install classy for you.

Installing via pip

Setting up a virtual environment

We strongly recommend using Conda as the environment manager when dealing with deep learning / data science / machine learning. It's also recommended that you install the PyTorch ecosystem before installing classy by following the instructions on pytorch.org

If you already have a Python 3 environment you want to use, you can skip to the Installing the library and dependencies section.

1. Download and install Conda.

2. Create a Conda environment with Python 3.8+:

   conda create -n classy python=3.8
   

3. Activate the Conda environment:

   conda activate classy
   

Installing the library and dependencies

Simply execute

pip install classy-core

and voilà! You're all set.

Looking for some adventures? Install nightly releases directly from pypi! You will not regret it :)

Running classy

Once it is installed, classy is available as a command line tool. It offers a wide variety of subcommands, all listed below. Detailed guides and references for each command is available in the documentation. Every one of classy's subcommands have a -h|--help flag available which details the various arguments & options you can use (e.g., classy train -h).

classy train

In its simplest form, classy train lets you train a transformer-based neural network for one of the tasks supported by classy (see the documentation).

classy train sentence-pair path/to/dataset/folder-or-file -n my-model

The command above will train a model to predict a label given a pair of sentences as input (e.g., Natural Language Inference or NLI) and save it under experiments/my-model. This same model can be further used by all other classy commands which require a classy model (predict, evaluate, serve, demo, upload).

classy predict

classy predict actually has two subcommands: interactive and file.

The first loads the model in memory and lets you try it out through the shell directly, so that you can test the model you trained and see what it predicts given some input. It is particularly useful when your machine cannot open a port for classy demo.

The second, instead, works on a file and produces an output where, for each input, it associates the corresponding predicted label. It is very useful when doing pre-processing or when you need to evaluate your model (although we offer classy evaluate for that).

classy evaluate

classy evaluate lets you evaluate your model on standard metrics for the task your model was trained upon. Simply run classy evaluate my-model path/to/file -o path/to/output/file and it will dump the evaluation at path/to/output/file

classy serve

classy serve <model> loads the model in memory and spawns a REST API you can use to query your model with any REST client.

classy demo

classy demo <model> spawns a Streamlit interface which lets you quickly show and query your model.

classy describe

classy describe <task> --dataset path/to/dataset runs some common metrics on a file formatted for the specific task. Great tool to run before training your model!

classy upload

classy upload <model> lets you upload your classy-trained model on the HuggingFace Hub and lets other users download / use it. (NOTE: you need a HuggingFace Hub account in order to upload to their hub)

Models uploaded via classy upload will be available for download by other classy users by simply executing classy download username@model.

classy download

classy download <model> downloads a previously uploaded classy-trained model from the HuggingFace Hub and stores it on your machine so that it is usable with any other classy command which requires a trained model (predict, evaluate, serve, demo, upload).

Models uploaded via classy upload are available by doing classy download username@model.

Enabling Shell Completion

To install shell completion, activate your conda environment and then execute

classy --install-autocomplete

From now on, whenever you activate your conda environment with classy installed, you are going to have autocompletion when pressing [TAB]!

Issues

You are more than welcome to file issues with either feature requests, bug reports, or general questions. If you already found a solution to your problem, don't hesitate to share it. Suggestions for new best practices and tricks are always welcome!

Contributions

We warmly welcome contributions from the community. If it is your first time as a contributor, we recommend you start by reading our CONTRIBUTING.md guide.

Small contributions can be made directly in a pull request. For contributing major features, we recommend you first create a issue proposing a design, so that it can be discussed before you risk wasting time.

Pull requests (PRs) must have one approving review and no requested changes before they are merged. As classy is primarily driven by SunglassesAI, we reserve the right to reject or revert contributions that we don't think are good additions or might not fit into our roadmap.