J0nasW/paperswithcode
A cleaned dataset from paperswithcode.com Last dataset update: July 2023 This is a cleaned up dataset optained from paperswithcode.com through their API service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information. It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.… See the full description on the dataset page: https://huggingface.co/datasets/J0nasW/paperswithcode.
A cleaned dataset from paperswithcode.com
Last dataset update: July 2023
This is a cleaned up dataset optained from paperswithcode.com through their API service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information. It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.
Contents
It contains the following tables:
- papers.csv (around 56K)
- papers_train.csv (80% from 56K)
- papers_test.csv (20% from 56K)
- tasks.csv
- areas.csv
Specials
UUIDs were added to the dataset since the PapersWithCode IDs (pwc_ids) are not distinct enough. These UUIDs may change in the future with new versions of the dataset. Also, embeddings were calculated for all of the 56K papers using the brilliant model SciNCL as well as dimensionality-redused 2D coordinates using UMAP.
There is also a simple Python Notebook which was used to optain and refactor the dataset.