| --- |
| license: cc-by-4.0 |
| pretty_name: AI Conference & Journal Papers |
| configs: |
| - config_name: aaai |
| data_files: |
| - split: "2026" |
| path: browse/aaai/2026.parquet |
| - split: "2025" |
| path: browse/aaai/2025.parquet |
| - split: "2024" |
| path: browse/aaai/2024.parquet |
| - split: "2023" |
| path: browse/aaai/2023.parquet |
| - config_name: acl |
| data_files: |
| - split: "2025" |
| path: browse/acl/2025.parquet |
| - split: "2024" |
| path: browse/acl/2024.parquet |
| - split: "2023" |
| path: browse/acl/2023.parquet |
| - config_name: cvpr |
| data_files: |
| - split: "2026" |
| path: browse/cvpr/2026.parquet |
| - split: "2025" |
| path: browse/cvpr/2025.parquet |
| - split: "2024" |
| path: browse/cvpr/2024.parquet |
| - split: "2023" |
| path: browse/cvpr/2023.parquet |
| - config_name: eccv |
| data_files: |
| - split: "2024" |
| path: browse/eccv/2024.parquet |
| - split: "2022" |
| path: browse/eccv/2022.parquet |
| - split: "2020" |
| path: browse/eccv/2020.parquet |
| - config_name: emnlp |
| data_files: |
| - split: "2025" |
| path: browse/emnlp/2025.parquet |
| - split: "2024" |
| path: browse/emnlp/2024.parquet |
| - split: "2023" |
| path: browse/emnlp/2023.parquet |
| - config_name: iccv |
| data_files: |
| - split: "2025" |
| path: browse/iccv/2025.parquet |
| - split: "2023" |
| path: browse/iccv/2023.parquet |
| - config_name: iclr |
| data_files: |
| - split: "2026" |
| path: browse/iclr/2026.parquet |
| - split: "2025" |
| path: browse/iclr/2025.parquet |
| - split: "2024" |
| path: browse/iclr/2024.parquet |
| - split: "2023" |
| path: browse/iclr/2023.parquet |
| - config_name: icml |
| data_files: |
| - split: "2025" |
| path: browse/icml/2025.parquet |
| - split: "2024" |
| path: browse/icml/2024.parquet |
| - split: "2023" |
| path: browse/icml/2023.parquet |
| - config_name: ijcai |
| data_files: |
| - split: "2025" |
| path: browse/ijcai/2025.parquet |
| - split: "2024" |
| path: browse/ijcai/2024.parquet |
| - split: "2023" |
| path: browse/ijcai/2023.parquet |
| - config_name: interspeech |
| data_files: |
| - split: "2025" |
| path: browse/interspeech/2025.parquet |
| - split: "2024" |
| path: browse/interspeech/2024.parquet |
| - split: "2023" |
| path: browse/interspeech/2023.parquet |
| - config_name: jmlr |
| data_files: |
| - split: "2025" |
| path: browse/jmlr/2025.parquet |
| - split: "2024" |
| path: browse/jmlr/2024.parquet |
| - split: "2023" |
| path: browse/jmlr/2023.parquet |
| - split: "2022" |
| path: browse/jmlr/2022.parquet |
| - config_name: naacl |
| data_files: |
| - split: "2025" |
| path: browse/naacl/2025.parquet |
| - split: "2024" |
| path: browse/naacl/2024.parquet |
| - config_name: neurips |
| data_files: |
| - split: "2025" |
| path: browse/neurips/2025.parquet |
| - split: "2024" |
| path: browse/neurips/2024.parquet |
| - split: "2023" |
| path: browse/neurips/2023.parquet |
| - config_name: wacv |
| data_files: |
| - split: "2026" |
| path: browse/wacv/2026.parquet |
| - split: "2025" |
| path: browse/wacv/2025.parquet |
| - split: "2024" |
| path: browse/wacv/2024.parquet |
| - split: "2023" |
| path: browse/wacv/2023.parquet |
| --- |
| |
| # AI Conference & Journal Papers |
|
|
| Searchable metadata for papers from top AI venues (NeurIPS, ICML, ICLR, CVPR, ICCV, WACV, ACL, EMNLP, NAACL). |
|
|
| - `papers.parquet`: the full dataset (all fields, all venues). |
| - Per-venue browse views: pick a venue in **Subset**, a year in **Split**. |
|
|
| ### Dataset Structure |
|
|
| - `ClosedUni/papercli-papers` (main entrypoint): Contains the full index metadata parquet (`papers.parquet`) and the per-venue browse parquet views (`browse/`). |
| - `ClosedUni/papercli-papers-[venue]`: Contains the sharded PDF files of that specific venue (no metadata parquet). |
|
|
| ### PDF Storage |
|
|
| PDF files are sharded across separate datasets by venue to keep repository sizes optimal: |
| - `ClosedUni/papercli-papers-[venue]` (e.g. `ClosedUni/papercli-papers-cvpr` for CVPR PDFs) |
|
|
| To download a mirrored PDF: |
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| repo_id = f"ClosedUni/papercli-papers-{row['venue'].lower()}" |
| path = hf_hub_download( |
| repo_id=repo_id, |
| filename=row["hf_pdf_path"], |
| repo_type="dataset", |
| ) |
| ``` |
|
|
| Built with [papercli](https://github.com/Keithsel/papercli). |
