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README.md

@@ -1,50 +1,127 @@
 ---
-dataset_info:
-  features:
-  - name: question_id
-    dtype: int64
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: document_text
-    dtype: string
-  - name: page
-    dtype: int64
-  - name: other_metadata
-    struct:
-    - name: doc_id
-      dtype: int64
-    - name: image
-      dtype: string
-    - name: ucsf_document_id
-      dtype: string
-    - name: ucsf_document_page_no
-      dtype: string
-  - name: num_tokens
-    dtype: int64
-  - name: match_type
-    dtype: string
-  - name: messages
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 104556687
-    num_examples: 39455
-  - name: validation
-    num_bytes: 15389930
-    num_examples: 5349
-  download_size: 26953138
-  dataset_size: 119946617
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+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}
+}
+```