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https://github.com/Glorf/recipenlg
Set of scripts and notebooks used to produce results visible in RecipeNLG paper
https://github.com/Glorf/recipenlg
Last synced: 10 days ago
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Set of scripts and notebooks used to produce results visible in RecipeNLG paper
- Host: GitHub
- URL: https://github.com/Glorf/recipenlg
- Owner: Glorf
- Created: 2020-12-04T09:37:28.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-07T17:46:22.000Z (over 2 years ago)
- Last Synced: 2024-08-02T02:16:25.783Z (3 months ago)
- Language: Jupyter Notebook
- Size: 4.49 MB
- Stars: 581
- Watchers: 15
- Forks: 43
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
This is an archive of code which was used to produce dataset and results available in our INLG 2020 paper: [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://www.aclweb.org/anthology/2020.inlg-1.4.pdf)
## What's exciting about it?
The dataset we publish contains 2231142 cooking recipes (>2 millions). It's processed in more careful way and provides more samples than any other dataset in the area.
## Where is the dataset?
Please visit the website of our project: [recipenlg.cs.put.poznan.pl](https://recipenlg.cs.put.poznan.pl/) to download it.
**NOTE:** The dataset contains all the data we gathered including from other datasets. To access only our gathered recipes (with no 12 instead of 1/2 etc), filter the dataset for source=Gathered. It results in approx 1.6M recipes of better quality.
## I've used the dataset in my research. How to cite you?
Use the following BibTeX entry:
```
@inproceedings{bien-etal-2020-recipenlg,
title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
author = "Bie{\'n}, Micha{\l} and
Gilski, Micha{\l} and
Maciejewska, Martyna and
Taisner, Wojciech and
Wisniewski, Dawid and
Lawrynowicz, Agnieszka",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.inlg-1.4",
pages = "22--28",
}
```
## Where are your models?
The pyTorch model is available in HuggingFace model hub as [mbien/recipenlg](https://huggingface.co/mbien/recipenlg). You can therefore easily import it into your solution as follows:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("mbien/recipenlg")
model = AutoModelWithLMHead.from_pretrained("mbien/recipenlg")
```
You can also check the generation performance interactively on our website (link above).
The SpaCy NER model is available in the `ner` directory
## Could you explain X and Y?
Yes, sure! If you feel some information is missing in our paper, please check first in our [thesis](https://www.researchgate.net/publication/345308878_Cooking_recipes_generator_utilizing_a_deep_learning-based_language_model), which is much more detailed. In case of further questions, you're invited to send us a github issue, we will respond as fast as we can!
## How to run the code?
We worked on the project interactively, and our core result is a new dataset. That's why the repo is rather a set of loosely connected python files and jupyter notebooks than a working runnable solution itself. However if you feel some part crucial for the reproduction is missing or you are dedicated to make the experience smoother, send us a feature request or (preferably), a pull request.