Add library name, pipeline tag, and correct links

README.md

@@ -1,28 +1,30 @@
 ---
-license: mit
-datasets:
-- OS-Copilot/OS-Atlas-data
 base_model:
 - ByteDance-Seed/UI-TARS-2B-SFT
+datasets:
+- OS-Copilot/OS-Atlas-data
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 # GUI-Actor-Verifier-2B
 
 
-This model was introduced in the paper [**GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents**](https://aka.ms/GUI-Actor).
+This model was introduced in the paper [**GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents**](https://huggingface.co/papers/2506.03143).
 It is developed based on [UI-TARS-2B-SFT](https://huggingface.co/ByteDance-Seed/UI-TARS-2B-SFT) and is designed to predict the correctness of an action position given a language instruction. This model is well-suited for **GUI-Actor**, as its attention map effectively provides diverse candidates for verification with only a single inference.
 
 
-For more details on model design and evaluation, please check: [🏠 Project Page](https://aka.ms/GUI-Actor) | [💻 Github Repo](https://github.com/microsoft/GUI-Actor) | [📑 Paper](https://www.arxiv.org/pdf/2506.03143).
+For more details on model design and evaluation, please check: [Project Page](https://microsoft.github.io/GUI-Actor/) | [Github Repo](https://github.com/microsoft/GUI-Actor) | [Paper](https://huggingface.co/papers/2506.03143).
 
 
 | Model List                                  | Hugging Face Link                         |
 |--------------------------------------------|--------------------------------------------|
-| **GUI-Actor-7B-Qwen2-VL**                   | [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2-VL)         |
-| **GUI-Actor-2B-Qwen2-VL**                   | [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-2B-Qwen2-VL)         |
-| **GUI-Actor-7B-Qwen2.5-VL (coming soon)**   | [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2.5-VL)       |
-| **GUI-Actor-3B-Qwen2.5-VL (coming soon)**   | [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-3B-Qwen2.5-VL)       |
-| **GUI-Actor-Verifier-2B**                   | [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-Verifier-2B)        |
+| **GUI-Actor-7B-Qwen2-VL**                   | [Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2-VL)         |
+| **GUI-Actor-2B-Qwen2-VL**                   | [Hugging Face](https://huggingface.co/microsoft/GUI-Actor-2B-Qwen2-VL)         |
+| **GUI-Actor-7B-Qwen2.5-VL (coming soon)**   | [Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2.5-VL)       |
+| **GUI-Actor-3B-Qwen2.5-VL (coming soon)**   | [Hugging Face](https://huggingface.co/microsoft/GUI-Actor-3B-Qwen2.5-VL)       |
+| **GUI-Actor-Verifier-2B**                   | [Hugging Face](https://huggingface.co/microsoft/GUI-Actor-Verifier-2B)        |
 
 
 
@@ -58,8 +60,8 @@ Table 2. Main results on the ScreenSpot-Pro and ScreenSpot-v2 with **Qwen2.5-VL*
 | **_3B models:_**
 | Qwen2.5-VL-3B  | Qwen2.5-VL    | 25.9           | 80.9           |
 | Jedi-3B        | Qwen2.5-VL    | 36.1           | 88.6           |
-| GUI-Actor-3B   | Qwen2.5-VL    | 42.2       | 91.0       |
-| GUI-Actor-3B + Verifier   | Qwen2.5-VL    | **45.9**       | **92.4**       |
+| GUI-Actor-3B   | Qwen2.5-VL    | **42.2**       | **91.0**       |
+| GUI-Actor-3B + Verifier   | Qwen2.5-VL    | 45.9       | 92.4       |
 
 ## 🚀 Usage
 The verifier takes a language instruction and an image with a red circle marking the target position as input. One example is shown below. It outputs either ‘True’ or ‘False’, and you can also use the probability of each label to score the sample.