README.md

---
license: other
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - document-qa
  - ocr
  - extractive-qa
  - nanochat
  - sft
pretty_name: DocVQA for Nanochat
size_categories:
  - 10K<n<100K
source_datasets:
  - pixparse/docvqa-single-page-questions
---

# DocVQA for Nanochat

Single-page document QA dataset processed for nanochat fine-tuning.

## Description

This dataset is derived from [pixparse/docvqa-single-page-questions](https://huggingface.co/datasets/pixparse/docvqa-single-page-questions) and has been processed for efficient fine-tuning of small language models with limited context windows.

## Modifications from Source

- **OCR truncation**: Answer-priority truncation ensures the answer is always present in the truncated context. Lines containing the answer are prioritized, then surrounding context is added until the token budget is reached.
- **Page numbers**: Added "Page X" header at the top of each document from `other_metadata.ucsf_document_page_no`
- **Token budget**: Documents truncated to fit within 1750 tokens (for 2048 context window models)
- **Short answers**: Filtered to answers ≤150 characters
- **Format**: Conversation format compatible with nanochat's CustomJSON task loader

## Statistics

| Split | Samples | Total Tokens | Avg Tokens |
|-------|---------|--------------|------------|
| Train | 39,455 | 15,495,380 | 393 |
| Validation | 5,349 | 2,218,651 | 415 |
| **Total** | **44,804** | **17,714,031** | - |

## Tokenizer

Token counts computed with **tiktoken cl100k_base** (GPT-4's tokenizer). This is a GPT-4 style BPE tokenizer similar to what nanochat uses.

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `question_id` | int | Original question ID from DocVQA |
| `question` | str | The question to answer |
| `answer` | str | The extracted answer (or "Not found in document.") |
| `document_text` | str | OCR text with page number prepended |
| `page` | int | Page number from OCR results (always 1 for single-page) |
| `other_metadata` | dict | Full metadata from source (ucsf_document_id, doc_id, etc.) |
| `num_tokens` | int | Exact token count (tiktoken cl100k_base) |
| `match_type` | str | How answer was matched: "exact", "fuzzy", or "none" |
| `messages` | list | Conversation format for training |

## Usage

### With HuggingFace Datasets

```python
from datasets import load_dataset

ds = load_dataset("morgan/docvqa-nanochat")

# Access a sample
sample = ds["train"][0]
print(f"Question: {{sample['question']}}")
print(f"Answer: {{sample['answer']}}")
print(f"Tokens: {{sample['num_tokens']}}")
```

### For Nanochat Training

The `messages` field is formatted for nanochat's CustomJSON task:

```python
# Download and convert to JSONL
from datasets import load_dataset
import json

ds = load_dataset("morgan/docvqa-nanochat", split="train")
with open("docvqa_train.jsonl", "w") as f:
    for row in ds:
        f.write(json.dumps(row["messages"]) + "\n")

# Then use with CustomJSON
from tasks.customjson import CustomJSON
train_ds = CustomJSON(filepath="docvqa_train.jsonl")
```

## Document Format

Each document is formatted as:

```
Document:
Page 4
R. J. REYNOLDS TOBACCO COMPANY
RETAIL PARTNERS MARKETING PLAN CONTRACT
...

Question: When is the contract effective date?
```

## License

Same as source dataset ([pixparse/docvqa-single-page-questions](https://huggingface.co/datasets/pixparse/docvqa-single-page-questions)).

## Citation

If you use this dataset, please cite the original DocVQA paper:

```bibtex
@inproceedings{mathew2021docvqa,
  title={DocVQA: A Dataset for VQA on Document Images},
  author={Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, CV},
  booktitle={WACV},
  year={2021}
}
```