| --- |
| license: mit |
| task_categories: |
| - text-classification |
| - feature-extraction |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # A cleaned dataset from [paperswithcode.com](https://paperswithcode.com/) |
| *Last dataset update: July 2023* |
|
|
| This is a cleaned up dataset optained from [paperswithcode.com](https://paperswithcode.com/) through their [API](https://paperswithcode.com/api/v1/docs/) 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](https://huggingface.co/malteos/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. |
