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README.md

@@ -2,23 +2,24 @@
 tags:
 - ocr
 - document-processing
-- lighton-ocr-2
+- glm-ocr
 - markdown
 - uv-script
 - generated
 ---
 
-# Document OCR using LightOnOCR-2-1B
+# Document OCR using GLM-OCR
 
-This dataset contains OCR results from images in [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india) using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.
+This dataset contains OCR results from images in [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
 
 ## Processing Details
 
 - **Source Dataset**: [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india)
-- **Model**: [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
+- **Task**: text recognition
 - **Number of Samples**: 10
-- **Processing Time**: 4.6 min
-- **Processing Date**: 2026-02-14 18:30 UTC
+- **Processing Time**: 6.2 min
+- **Processing Date**: 2026-02-14 18:31 UTC
 
 ### Configuration
 
@@ -26,76 +27,37 @@ This dataset contains OCR results from images in [NationalLibraryOfScotland/medi
 - **Output Column**: `markdown`
 - **Dataset Split**: `train`
 - **Batch Size**: 16
-- **Target Image Size**: 1540px (longest dimension)
 - **Max Model Length**: 8,192 tokens
-- **Max Output Tokens**: 4,096
-- **Temperature**: 0.2
-- **Top P**: 0.9
+- **Max Output Tokens**: 8,192
+- **Temperature**: 0.01
+- **Top P**: 1e-05
 - **GPU Memory Utilization**: 80.0%
 
 ## Model Information
 
-LightOnOCR-2 is a next-generation fast, compact OCR model that excels at:
-- ⚡ **Fastest Speed** - 42.8 pages/second on H100 GPU (7× faster than v1)
-- 🎯 **High Accuracy** - 83.2 ± 0.9% on OlmOCR-Bench (+7.1% vs v1)
-- 🧠 **RLVR Training** - Eliminates repetition loops and formatting errors
-- 📚 **Better Dataset** - 2.5× larger training data with cleaner annotations
-- 📐 **LaTeX formulas** - Mathematical notation in LaTeX format
-- 📊 **Tables** - Extracted and formatted as markdown
-- 📝 **Document structure** - Hierarchy and layout preservation
-- 🌍 **Multilingual** - Optimized for European languages
-- 💪 **Production-ready** - Outperforms models 9× larger
-
-### Key Improvements over v1
-
-- **7.5× faster**: 42.8 vs 5.71 pages/sec on H100
-- **+7.1% accuracy**: 83.2% vs 76.1% on benchmarks
-- **Better quality**: RLVR training eliminates common OCR errors
-- **Cleaner output**: No repetition loops or formatting glitches
-- **Simpler**: Single model (no vocabulary variants)
+GLM-OCR is a compact, high-performance OCR model:
+- 0.9B parameters
+- 94.62% on OmniDocBench V1.5
+- CogViT visual encoder + GLM-0.5B language decoder
+- Multi-Token Prediction (MTP) loss for efficiency
+- Multilingual: zh, en, fr, es, ru, de, ja, ko
+- MIT licensed
 
 ## Dataset Structure
 
 The dataset contains all original columns plus:
-- `markdown`: The extracted text in markdown format with LaTeX formulas
+- `markdown`: The extracted text in markdown format
 - `inference_info`: JSON list tracking all OCR models applied to this dataset
 
-## Usage
-
-```python
-from datasets import load_dataset
-import json
-
-# Load the dataset
-dataset = load_dataset("{output_dataset_id}", split="train")
-
-# Access the markdown text
-for example in dataset:
-    print(example["markdown"])
-    break
-
-# View all OCR models applied to this dataset
-inference_info = json.loads(dataset[0]["inference_info"])
-for info in inference_info:
-    print(f"Column: {info['column_name']} - Model: {info['model_id']}")
-```
-
 ## Reproduction
 
-This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) LightOnOCR-2 script:
-
 ```bash
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     NationalLibraryOfScotland/medical-history-of-british-india \
     <output-dataset> \
     --image-column image \
-    --batch-size 16
+    --batch-size 16 \
+    --task ocr
 ```
 
-## Performance
-
-- **Processing Speed**: ~0.04 images/second
-- **Benchmark Score**: 83.2 ± 0.9% on OlmOCR-Bench
-- **Training**: RLVR (Reinforcement Learning with Verifiable Rewards)
-
-Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+Generated with [UV Scripts](https://huggingface.co/uv-scripts)