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README.md

@@ -1,49 +1,92 @@
 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: mean_ocr
-    dtype: float64
-  - name: std_ocr
-    dtype: float64
-  - name: title
-    dtype: string
-  - name: date
-    dtype: string
-  - name: language
-    list: string
-  - name: item_iiif_url
-    dtype: string
-  - name: multi_language
-    dtype: bool
-  - name: issue_uri
-    dtype: string
-  - name: id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: download_status
-    dtype: string
-  - name: download_retries
-    dtype: int64
-  - name: download_url
-    dtype: string
-  - name: markdown
-    dtype: string
-  - name: olmocr_metadata
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 110898315
-    num_examples: 100
-  download_size: 110686805
-  dataset_size: 110898315
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+viewer: false
+tags:
+- ocr
+- document-processing
+- olmocr
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using olmOCR-2-7B-1025-FP8
+
+This dataset contains markdown-formatted OCR results from images in [davanstrien/newspapers-with-images](https://huggingface.co/datasets/davanstrien/newspapers-with-images) using olmOCR-2-7B.
+
+## Processing Details
+
+- **Source Dataset**: [davanstrien/newspapers-with-images](https://huggingface.co/datasets/davanstrien/newspapers-with-images)
+- **Model**: [allenai/olmOCR-2-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8)
+- **Number of Samples**: 100
+- **Processing Time**: 0h 9m 29s
+- **Processing Date**: 2025-10-22 18:57 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 4
+- **Max Model Length**: 16,384 tokens
+- **Max Output Tokens**: 8,192
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+olmOCR-2-7B is a high-quality document OCR model based on Qwen2.5-VL-7B-Instruct, fine-tuned on olmOCR-mix-1025 dataset and optimized with GRPO reinforcement learning.
+
+Key features:
+- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format
+- 📊 **HTML tables** - Structured table extraction
+- 📝 **Document structure** - Headers, lists, formatting preserved
+- 🖼️ **Figure descriptions** - Charts and figures labeled with descriptions
+- 🔄 **Rotation detection** - Metadata about document orientation
+- 📑 **Natural reading order** - Handles multi-column and complex layouts
+- 🎯 **High accuracy** - Scores 82.4 ± 1.1 on olmOCR-Bench
+
+## Output Format
+
+Each row contains:
+- Original image from source dataset
+- `markdown`: Extracted document content in markdown format
+- `olmocr_metadata`: JSON with document metadata (language, rotation, table/diagram flags)
+
+## Columns
+
+- `image`: Original document image
+- `markdown`: Extracted text and structure in markdown
+- `olmocr_metadata`: Document metadata (primary_language, is_rotation_valid, rotation_correction, is_table, is_diagram)
+- `inference_info`: Processing metadata (model, script version, timestamp)
+
+## Reproduction
+
+```bash
+# Using HF Jobs (recommended)
+hf jobs uv run --flavor l4x1 \
+  -s HF_TOKEN \
+  https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
+  davanstrien/newspapers-with-images \
+  your-username/output-dataset
+
+# Local with GPU
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
+  davanstrien/newspapers-with-images \
+  your-username/output-dataset
+```
+
+## Citation
+
+```bibtex
+@misc{olmocr,
+      title={{olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
+      author={Jake Poznanski and Jon Borchardt and Jason Dunkelberger and Regan Huff and Daniel Lin and Aman Rangapur and Christopher Wilhelm and Kyle Lo and Luca Soldaini},
+      year={2025},
+      eprint={2502.18443},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.18443},
+}
+```
+
+---
+*Generated with [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr)*