Update README.md

README.md

@@ -1,297 +1,306 @@
----
-license: mit
----
-
-# pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs
-
-[pdfQA](https://arxiv.org/abs/2601.02285) is a structured benchmark collection for document-level question answering and PDF understanding research.
-
-The dataset is organized to support:
-
-* Raw document processing research
-* Structured extraction pipelines
-* Retrieval-augmented QA
-* End-to-end document reasoning systems
-
-It preserves original documents alongside structured derivatives to enable reproducible evaluation across preprocessing strategies.
-
----
-
-## Dataset Structure
-
-The repository follows a strict hierarchical layout:
-
-```
-<category>/<type>/<dataset>/...
-```
-
-### Categories
-
-* `real-pdfQA/` — Real-world benchmark datasets
-* `syn-pdfQA/` — Synthetic benchmark datasets
-
-### Types
-
-Each dataset contains three file-type folders:
-
-* `01.1_Input_Files_Non_PDF/` — Original source formats (e.g., xlsx, epub, htm, tex, txt)
-* `01.2_Input_Files_PDF/` — Original PDF files
-* `01.3_Input_Files_CSV/` — Structured annotations / tabular representations
-
-### Datasets
-Each type folder contains subfolders for individual datasets. Supported datasets include:
-
-#### Real-world Datasets
-- `ClimateFinanceBench/`
-- `ClimRetrieve/`
-- `FeTaQA/`
-- `FinanceBench/`
-- `FinQA/`
-- `NaturalQuestions/`
-- `PaperTab/`
-- `PaperText/`
-- `Tat-QA/`
-
-#### Synthetic Datasets
-- `books/`
-- `financial_reports/`
-- `sustainability_disclosures/`
-- `research_articles/`
-
-
-### Example
-
-```
-syn-pdfQA/
-  01.2_Input_Files_PDF/
-    books/
-      file1.pdf
-  01.3_Input_Files_CSV/
-    books/
-      file1.csv
-  01.1_Input_Files_Non_PDF/
-    books/
-      file1.xlsx
-```
-
-This design allows:
-
-* Access to original PDFs
-* Access to structured evaluation data
-* Access to original source formats for preprocessing research
-
----
-
-## Intended Use
-
-This dataset is intended for:
-
-* PDF parsing and layout understanding
-* Financial and sustainability document QA
-* Retrieval-augmented generation (RAG)
-* Multi-modal document pipelines
-* Table extraction and structured reasoning
-* Robustness evaluation across preprocessing pipelines
-
-It is particularly useful for comparing:
-
-* Direct PDF-based reasoning
-* OCR pipelines
-* Structured table extraction
-* Raw-source ingestion approaches
-
----
-
-## Access Patterns
-
-The dataset supports multiple access patterns depending on research
-needs.
-
-All official download scripts are available in the GitHub repository:
-
-👉 https://github.com/tobischimanski/pdfQA
-
-Scripts are provided in both:
-
--   **Bash (git + Git LFS)** --- recommended for large-scale downloads\
--   **Python (huggingface_hub API)** --- recommended for programmatic
-    workflows
-
-------------------------------------------------------------------------
-
-### 1️⃣ Download Everything
-
-Download the entire repository (all categories, types, and datasets).
-
-#### Bash (git + LFS)
-
-``` bash
-./tools/download_using_bash/download_all.sh
-```
-
-[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_all.sh)
-
-
-#### Python (HF API)
-
-``` bash
-python tools/download_using_python/download_all.py
-```
-
-[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_all.py)
-
-------------------------------------------------------------------------
-
-### 2️⃣ Download by Category
-
-Download only:
-
--   `real-pdfQA/`
--   or `syn-pdfQA/`
-
-#### Example
-
-``` bash
-./tools/download_using_bash/download_category.sh syn-pdfQA
-```
-
-[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_category.sh)
-
-[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_category.py)
-
-------------------------------------------------------------------------
-
-### 3️⃣ Download by Dataset (All Types)
-
-Download a single dataset across all three file-type folders:
-
--   `01.1_Input_Files_Non_PDF/`
--   `01.2_Input_Files_PDF/`
--   `01.3_Input_Files_CSV/`
-
-#### Example
-
-``` bash
-./tools/download_using_bash/download_dataset.sh syn-pdfQA books
-```
-
-[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_dataset.sh)
-
-[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_dataset.py)
-
-------------------------------------------------------------------------
-
-### 4️⃣ Download Arbitrary Folders
-
-Download one or multiple arbitrary folder paths.
-
-#### Example
-
-``` bash
-./tools/download_using_bash/download_folders.sh \
-  "syn-pdfQA/01.2_Input_Files_PDF/books" \
-  "syn-pdfQA/01.3_Input_Files_CSV/books"
-```
-
-[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_folders.sh)
-
-[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_folders.py)
-
-------------------------------------------------------------------------
-
-### 5️⃣ Download Specific Files
-
-Download one or more individual files.
-
-#### Example (Bash)
-
-``` bash
-./tools/download_using_bash/download_files.sh \
-  "syn-pdfQA/01.2_Input_Files_PDF/books/file1.pdf"
-```
-
-[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_files.sh)
-
-[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_files.py)
-
-------------------------------------------------------------------------
-
-### 6️⃣ Direct API Access (Single File)
-
-Files can also be downloaded directly using the Hugging Face API. Example:
-
-``` python
-from huggingface_hub import hf_hub_download
-
-hf_hub_download(
-    repo_id="pdfqa/pdfQA-Benchmark",
-    repo_type="dataset",
-    filename="syn-pdfQA/01.2_Input_Files_PDF/FinQA/AAL_2010.pdf"
-)
-```
-
-------------------------------------------------------------------------
-
-## Recommended Usage
-
--   For **large-scale research experiments** → use **Bash + git LFS**
-    (fully resumable).
--   For **automated pipelines** → use **Python scripts**.
--   For **fine-grained subset control** → use folder or file-based
-    scripts.
-
----
-
-## Data Modalities
-
-Depending on the dataset:
-
-* Financial reports
-* Sustainability disclosures
-* Structured financial QA corpora
-* Table-heavy documents
-* Mixed structured/unstructured content
-
-Formats may include: `PDF`, `CSV`, `XLS/XLSX`, `EPUB`, `HTML/HTM`, `TEX`, `TXT`
-
----
-
-## Research Motivation
-
-Many document QA benchmarks release only structured data or only PDFs.
-pdfQA preserves **all representations**:
-
-* Original document
-* Structured derivative
-* Raw source format (if available)
-
-This enables:
-
-* Studying preprocessing impact
-* Comparing parsing strategies
-* Evaluating robustness to format variation
-* End-to-end pipeline benchmarking
-
----
-
-## Citation
-
-If you use **pdfQA**, please cite:
-
-```
-@misc{schimanski2026pdfqa,
-      title={pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs}, 
-      author={Tobias Schimanski and Imene Kolli and Yu Fan and Ario Saeid Vaghefi and Jingwei Ni and Elliott Ash and Markus Leippold},
-      year={2026},
-      eprint={2601.02285},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2601.02285}, 
-}
-```
-
----
-
-## Contact
-
-Visit [https://github.com/tobischimanski/pdfQA](https://github.com/tobischimanski/pdfQA) for access and updates.
+---
+license: mit
+task_categories:
+- question-answering
+- table-question-answering
+language:
+- en
+tags:
+- research
+- climate
+- finance
+---
+
+# pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs
+
+[pdfQA](https://arxiv.org/abs/2601.02285) is a structured benchmark collection for document-level question answering and PDF understanding research.
+
+The dataset is organized to support:
+
+* Raw document processing research
+* Structured extraction pipelines
+* Retrieval-augmented QA
+* End-to-end document reasoning systems
+
+It preserves original documents alongside structured derivatives to enable reproducible evaluation across preprocessing strategies.
+
+---
+
+## Dataset Structure
+
+The repository follows a strict hierarchical layout:
+
+```
+<category>/<type>/<dataset>/...
+```
+
+### Categories
+
+* `real-pdfQA/` — Real-world benchmark datasets
+* `syn-pdfQA/` — Synthetic benchmark datasets
+
+### Types
+
+Each dataset contains three file-type folders:
+
+* `01.1_Input_Files_Non_PDF/` — Original source formats (e.g., xlsx, epub, htm, tex, txt)
+* `01.2_Input_Files_PDF/` — Original PDF files
+* `01.3_Input_Files_CSV/` — Structured annotations / tabular representations
+
+### Datasets
+Each type folder contains subfolders for individual datasets. Supported datasets include:
+
+#### Real-world Datasets
+- `ClimateFinanceBench/`
+- `ClimRetrieve/`
+- `FeTaQA/`
+- `FinanceBench/`
+- `FinQA/`
+- `NaturalQuestions/`
+- `PaperTab/`
+- `PaperText/`
+- `Tat-QA/`
+
+#### Synthetic Datasets
+- `books/`
+- `financial_reports/`
+- `sustainability_disclosures/`
+- `research_articles/`
+
+
+### Example
+
+```
+syn-pdfQA/
+  01.2_Input_Files_PDF/
+    books/
+      file1.pdf
+  01.3_Input_Files_CSV/
+    books/
+      file1.csv
+  01.1_Input_Files_Non_PDF/
+    books/
+      file1.xlsx
+```
+
+This design allows:
+
+* Access to original PDFs
+* Access to structured evaluation data
+* Access to original source formats for preprocessing research
+
+---
+
+## Intended Use
+
+This dataset is intended for:
+
+* PDF parsing and layout understanding
+* Financial and sustainability document QA
+* Retrieval-augmented generation (RAG)
+* Multi-modal document pipelines
+* Table extraction and structured reasoning
+* Robustness evaluation across preprocessing pipelines
+
+It is particularly useful for comparing:
+
+* Direct PDF-based reasoning
+* OCR pipelines
+* Structured table extraction
+* Raw-source ingestion approaches
+
+---
+
+## Access Patterns
+
+The dataset supports multiple access patterns depending on research
+needs.
+
+All official download scripts are available in the GitHub repository:
+
+👉 https://github.com/tobischimanski/pdfQA
+
+Scripts are provided in both:
+
+-   **Bash (git + Git LFS)** --- recommended for large-scale downloads\
+-   **Python (huggingface_hub API)** --- recommended for programmatic
+    workflows
+
+------------------------------------------------------------------------
+
+### 1️⃣ Download Everything
+
+Download the entire repository (all categories, types, and datasets).
+
+#### Bash (git + LFS)
+
+``` bash
+./tools/download_using_bash/download_all.sh
+```
+
+[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_all.sh)
+
+
+#### Python (HF API)
+
+``` bash
+python tools/download_using_python/download_all.py
+```
+
+[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_all.py)
+
+------------------------------------------------------------------------
+
+### 2️⃣ Download by Category
+
+Download only:
+
+-   `real-pdfQA/`
+-   or `syn-pdfQA/`
+
+#### Example
+
+``` bash
+./tools/download_using_bash/download_category.sh syn-pdfQA
+```
+
+[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_category.sh)
+
+[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_category.py)
+
+------------------------------------------------------------------------
+
+### 3️⃣ Download by Dataset (All Types)
+
+Download a single dataset across all three file-type folders:
+
+-   `01.1_Input_Files_Non_PDF/`
+-   `01.2_Input_Files_PDF/`
+-   `01.3_Input_Files_CSV/`
+
+#### Example
+
+``` bash
+./tools/download_using_bash/download_dataset.sh syn-pdfQA books
+```
+
+[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_dataset.sh)
+
+[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_dataset.py)
+
+------------------------------------------------------------------------
+
+### 4️⃣ Download Arbitrary Folders
+
+Download one or multiple arbitrary folder paths.
+
+#### Example
+
+``` bash
+./tools/download_using_bash/download_folders.sh \
+  "syn-pdfQA/01.2_Input_Files_PDF/books" \
+  "syn-pdfQA/01.3_Input_Files_CSV/books"
+```
+
+[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_folders.sh)
+
+[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_folders.py)
+
+------------------------------------------------------------------------
+
+### 5️⃣ Download Specific Files
+
+Download one or more individual files.
+
+#### Example (Bash)
+
+``` bash
+./tools/download_using_bash/download_files.sh \
+  "syn-pdfQA/01.2_Input_Files_PDF/books/file1.pdf"
+```
+
+[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_files.sh)
+
+[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_files.py)
+
+------------------------------------------------------------------------
+
+### 6️⃣ Direct API Access (Single File)
+
+Files can also be downloaded directly using the Hugging Face API. Example:
+
+``` python
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(
+    repo_id="pdfqa/pdfQA-Benchmark",
+    repo_type="dataset",
+    filename="syn-pdfQA/01.2_Input_Files_PDF/FinQA/AAL_2010.pdf"
+)
+```
+
+------------------------------------------------------------------------
+
+## Recommended Usage
+
+-   For **large-scale research experiments** → use **Bash + git LFS**
+    (fully resumable).
+-   For **automated pipelines** → use **Python scripts**.
+-   For **fine-grained subset control** → use folder or file-based
+    scripts.
+
+---
+
+## Data Modalities
+
+Depending on the dataset:
+
+* Financial reports
+* Sustainability disclosures
+* Structured financial QA corpora
+* Table-heavy documents
+* Mixed structured/unstructured content
+
+Formats may include: `PDF`, `CSV`, `XLS/XLSX`, `EPUB`, `HTML/HTM`, `TEX`, `TXT`
+
+---
+
+## Research Motivation
+
+Many document QA benchmarks release only structured data or only PDFs.
+pdfQA preserves **all representations**:
+
+* Original document
+* Structured derivative
+* Raw source format (if available)
+
+This enables:
+
+* Studying preprocessing impact
+* Comparing parsing strategies
+* Evaluating robustness to format variation
+* End-to-end pipeline benchmarking
+
+---
+
+## Citation
+
+If you use **pdfQA**, please cite:
+
+```
+@misc{schimanski2026pdfqa,
+      title={pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs}, 
+      author={Tobias Schimanski and Imene Kolli and Yu Fan and Ario Saeid Vaghefi and Jingwei Ni and Elliott Ash and Markus Leippold},
+      year={2026},
+      eprint={2601.02285},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.02285}, 
+}
+```
+
+---
+
+## Contact
+
+Visit [https://github.com/tobischimanski/pdfQA](https://github.com/tobischimanski/pdfQA) for access and updates.