Update README.md

README.md

@@ -21,8 +21,10 @@ library_name: transformers
 </div>
 
 ## News
+* `2026.07.22` 🏆 MonkeyOCRv2-B-Parsing ranks #1 among evaluated open-source models on the official [MDPBench Leaderboard](https://huggingface.co/spaces/Delores-Lin/MDPBench-leaderboard), achieving 83.3 overall across 17 languages, including digital-born and photographed documents.
 * `2026.07.21` 📦 We release [MonkeyDoc v2](https://modelscope.cn/datasets/zenosai/MonkeyDocv2), an open multilingual corpus for document-oriented pretraining. We hope it can serve as a shared data foundation for more transparent, reproducible, and fair comparisons in Document AI.
-* `2026.07.11` 🚀 We release [MonkeyOCRv2](https://github.com/Yuliang-Liu/MonkeyOCRv2), including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.
+* `2026.07.20` 🤗 MonkeyOCRv2 was featured in [Hugging Face Daily Papers](https://huggingface.co/papers/2607.11562) and has entered the Weekly Papers list. Thanks to the community for the support, upvotes, and feedback!
+* `2026.07.14` 🚀 We release [MonkeyOCRv2](https://arxiv.org/abs/2607.11562), including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.
 
 ## Use MonkeyOCRv2 as a Vision Backbone
 
@@ -45,11 +47,12 @@ encoder = AutoModel.from_pretrained(
 See the Vision Encoder [Quick Start](https://github.com/Yuliang-Liu/MonkeyOCRv2#vision-encoder) for installation and feature-extraction examples. If you adapt MonkeyOCRv2 to a new task or domain, feel free to open an issue or pull request and share the results.
 
 ## MonkeyDoc v2
-MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. The open-sourcing of MonkeyDoc v2 is still underway. So far, we have released all 52 million synthetic samples and 35 million real-world samples derived from FinePDF. Additional real-world data from other sources is currently being organized and will be released progressively. You can download the full datset as follows:
+MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. The open-sourcing of MonkeyDoc v2 is still underway. So far, we have released 52 million synthetic samples and 41 million real-world samples. You can download the full datset as follows:
 ```bash
 pip install modelscope
 modelscope download --dataset zenosai/MonkeyDocv2 --local_dir ./MonkeyDocv2
 ```
+After processing and compression, downloading the dataset currently requires approximately 10 TB of disk space. We recommend having at least 11 TB of available storage before starting the download to ensure sufficient space throughout the process.
 
 ## Model Zoo
 
@@ -249,9 +252,9 @@ python download_model.py -t modelscope -n MonkeyOCRv2-B # or MonkeyOCRv2-S / Mon
 ```bash
 cd vision
 # For MonkeyOCRv2-B and MonkeyOCRv2-S
-python extract_feature.py
+python extract_feature.py -m ../model_weight/MonkeyOCRv2-B -i ../images_test/ar.JPEG
 # For MonkeyOCRv2-AS
-python extract_feature_vitae.py
+python extract_feature_vitae.py -m ../model_weight/MonkeyOCRv2-AS -i ../images_test/ar.JPEG
 ```
 
 ### Document Parsing
@@ -1237,7 +1240,7 @@ This multi-expert agreement strategy reduces model-specific annotation errors an
 - **MonkeyOCR**: https://github.com/Yuliang-Liu/MonkeyOCR
 - **MDPBench**: https://github.com/Yuliang-Liu/MultimodalOCR
 - **MonkeyDoc**: https://modelscope.cn/datasets/zenosai/MonkeyDoc
-- **FinePDF**: https://huggingface.co/datasets/HuggingFaceFW/finepdfs
+- **MonkeyDoc v2**: https://modelscope.cn/datasets/zenosai/MonkeyDocv2
 
 ## Citation
 If you use any part of this release — the MonkeyOCRv2 encoders, MonkeyOCRv2-Parsing,