📋 A template README.md for code accompanying a Machine Learning paper
This repository is the official implementation of "Towards Neural Program Interfaces" (https://arxiv.org/abs/2030.12345).
📋 Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials
To install requirements:
pip install -r requirements.txt
📋 Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
To generate a dataset, run this command:
python construct_data.py --word <word>
To train a classifier model on the generated dataset, run this command:
python train_classifier.py
To evaluate a classifier model, run this command:
python test_classifier.py
To train an NPI model, run this command:
python train_npi.py
To evaluate an NPI model, run this command:
python evaluate_npi_fast.py
📋 Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters.
📋 Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).
You can download pretrained models here:
- My awesome model trained on ImageNet using parameters x,y,z.
📋 Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable). Alternatively you can have an additional column in your results table with a link to the models.
Our model achieves the following performance on :
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| My awesome model | 85% | 95% |
📋 Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it.
📋 Pick a licence and describe how to contribute to your code repository.