| --- |
| 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 and full-text PDF mirrors for papers from top-tier AI venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, WACV, ACL, EMNLP, NAACL, IJCAI, AAAI, JMLR, Interspeech) from 2023. |
|
|
| - 📊 `papers.parquet`: The complete dataset containing all fields and all venues. |
| - 🔍 **Per-venue browse views**: Easily explore specific subsets by selecting a venue in **Subset** and a year in **Split**. |
|
|
| --- |
|
|
| ## 🏗️ Dataset Structure & Storage Strategy |
|
|
| To avoid reaching repository size limits and ensure optimal performance, the project is decoupled into two components: |
|
|
| 1. **Main Registry (This Repository):** Contains the complete searchable metadata, individual index files (`papers.parquet`), and partitioned per-venue Parquet files (`browse/`). |
| 2. **PDF Storage Shards:** The raw PDF binary files are sharded into separate, venue-specific repositories (`GenAI4ELab/papercli-papers-[venue]`). |
|
|
| --- |
|
|
| ## 🛠️ How to Download PDFs |
|
|
| Because the metadata and actual file pointers reside in this main repository, the standard workflow is to query/filter the metadata here first, then programmatically fetch the corresponding PDF binary from its respective shard. |
|
|
| ### Python Example |
|
|
| Ensure you have the Hugging Face Hub CLI client installed: |
| ```bash |
| pip install huggingface_hub |
| ``` |
|
|
| You can use the following script to look up a paper and pull its mirrored PDF automatically: |
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| # Assuming `row` is a dictionary or pandas row obtained from the metadata Parquet |
| venue_name = row['venue'].lower() |
| repo_id = f"GenAI4ELab/papercli-papers-{venue_name}" |
| |
| path = hf_hub_download( |
| repo_id=repo_id, |
| filename=row["hf_pdf_path"], |
| repo_type="dataset", |
| ) |
| print(f"Downloaded PDF to: {path}") |
| ``` |
|
|
| ## 🔗 Dataset Hub & Venue Directory |
|
|
| Here is the complete navigation map for the main dataset metadata registry and all corresponding sharded PDF storage repositories: |
|
|
| | STT | Venue / Dataset | Repository Link | |
| | :---: | :--- | :--- | |
| | 1 | 🏠 **Main Registry** (Metadata) | [GenAI4ELab/papercli-papers](https://huggingface.co/datasets/GenAI4ELab/papercli-papers) | |
| | 2 | 📂 **NeurIPS** | [GenAI4ELab/papercli-papers-neurips](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-neurips) | |
| | 3 | 📂 **AAAI** | [GenAI4ELab/papercli-papers-aaai](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-aaai) | |
| | 4 | 📂 **EMNLP** | [GenAI4ELab/papercli-papers-emnlp](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-emnlp) | |
| | 5 | 📂 **CVPR** | [GenAI4ELab/papercli-papers-cvpr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-cvpr) | |
| | 6 | 📂 **ICCV** | [GenAI4ELab/papercli-papers-iccv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-iccv) | |
| | 7 | 📂 **ICML** | [GenAI4ELab/papercli-papers-icml](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-icml) | |
| | 8 | 📂 **ACL** | [GenAI4ELab/papercli-papers-acl](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-acl) | |
| | 9 | 📂 **IJCAI** | [GenAI4ELab/papercli-papers-ijcai](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-ijcai) | |
| | 10 | 📂 **ECCV** | [GenAI4ELab/papercli-papers-eccv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-eccv) | |
| | 11 | 📂 **ICLR** | [GenAI4ELab/papercli-papers-iclr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-iclr) | |
| | 12 | 📂 **NAACL** | [GenAI4ELab/papercli-papers-naacl](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-naacl) | |
| | 13 | 📂 **Interspeech** | [GenAI4ELab/papercli-papers-interspeech](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-interspeech) | |
| | 14 | 📂 **WACV** | [GenAI4ELab/papercli-papers-wacv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-wacv) | |
| | 15 | 📂 **JMLR** | [GenAI4ELab/papercli-papers-jmlr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-jmlr) | |
|
|
| --- |
|
|
| ## 🛠️ Credits & Tools |
|
|
| This dataset was compiled and structured using **[papercli](https://github.com/Keithsel/papercli)**, an open-source tool designed to index, mirror, and shard academic papers from top-tier AI venues efficiently. |
|
|
| If you find this mirror useful, please consider starring the parent repository and the original `papercli` project! |
