Upload README.md with huggingface_hub

README.md

@@ -2,123 +2,62 @@
 tags:
 - ocr
 - document-processing
-- paddleocr-vl
-- ocr
+- glm-ocr
+- markdown
 - uv-script
 - generated
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: filename
-    dtype: string
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 17116070.0
-    num_examples: 13
-  download_size: 15229790
-  dataset_size: 17116070.0
 ---
 
-# Document Processing using PaddleOCR-VL (OCR mode)
+# Document OCR using GLM-OCR
 
-This dataset contains OCR results from images in [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input) using PaddleOCR-VL, an ultra-compact 0.9B OCR model.
+This dataset contains OCR results from images in [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
 
 ## Processing Details
 
 - **Source Dataset**: [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input)
-- **Model**: [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL)
-- **Task Mode**: `ocr` - General text extraction to markdown format
+- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
+- **Task**: text recognition
 - **Number of Samples**: 13
-- **Processing Time**: 1.9 min
-- **Processing Date**: 2026-02-06 17:45 UTC
+- **Processing Time**: 2.3 min
+- **Processing Date**: 2026-02-06 17:48 UTC
 
 ### Configuration
 
 - **Image Column**: `image`
-- **Output Column**: `paddleocr_ocr`
+- **Output Column**: `markdown`
 - **Dataset Split**: `train`
 - **Batch Size**: 16
-- **Smart Resize**: Enabled
 - **Max Model Length**: 8,192 tokens
-- **Max Output Tokens**: 4,096
-- **Temperature**: 0.0
+- **Max Output Tokens**: 16,384
+- **Temperature**: 0.01
+- **Top P**: 1e-05
 - **GPU Memory Utilization**: 80.0%
 
 ## Model Information
 
-PaddleOCR-VL is a state-of-the-art, resource-efficient model tailored for document parsing:
-- 🎯 **Ultra-compact** - Only 0.9B parameters (smallest OCR model)
-- 📝 **OCR mode** - General text extraction
-- 📊 **Table mode** - HTML table recognition
-- 📐 **Formula mode** - LaTeX mathematical notation
-- 📈 **Chart mode** - Structured chart analysis
-- 🌍 **Multilingual** - Support for multiple languages
-- ⚡ **Fast** - Quick initialization and inference
-- 🔧 **ERNIE-4.5 based** - Different architecture from Qwen models
-
-### Task Modes
-
-- **OCR**: Extract text content to markdown format
-- **Table Recognition**: Extract tables to HTML format
-- **Formula Recognition**: Extract mathematical formulas to LaTeX
-- **Chart Recognition**: Analyze and describe charts/diagrams
+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:
-- `paddleocr_ocr`: The extracted content based on task mode
+- `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 extracted content
-for example in dataset:
-    print(example["paddleocr_ocr"])
-    break
-
-# View all OCR models applied to this dataset
-inference_info = json.loads(dataset[0]["inference_info"])
-for info in inference_info:
-    print(f"Task: {info['task_mode']} - Model: {info['model_id']}")
-```
-
 ## Reproduction
 
-This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) PaddleOCR-VL script:
-
 ```bash
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     minhpvo/ocr-input \
     <output-dataset> \
-    --task-mode ocr \
     --image-column image \
     --batch-size 16 \
-    --max-model-len 8192 \
-    --max-tokens 4096 \
-    --gpu-memory-utilization 0.8
+    --task ocr
 ```
 
-## Performance
-
-- **Model Size**: 0.9B parameters (smallest among OCR models)
-- **Processing Speed**: ~0.11 images/second
-- **Architecture**: NaViT visual encoder + ERNIE-4.5-0.3B language model
-
-Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+Generated with [UV Scripts](https://huggingface.co/uv-scripts)