Improve model card: add metadata, links, and detailed description

This PR significantly improves the model card for `Online-Searcher-Qwen-7B` by:

- Adding `pipeline_tag: text-generation` to the metadata, allowing the model to be discovered more easily (e.g., at https://huggingface.co/models?pipeline_tag=text-generation).
- Adding `license: mit` to the metadata, clearly stating the model's license as found in the GitHub repository.
- Adding a link to the original paper [SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis](https://huggingface.co/papers/2505.16834).
- Adding a link to the associated [GitHub repository](https://github.com/RUCAIBox/SimpleDeepSearcher).
- Populating the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections with information extracted from the paper abstract and GitHub README.
- Adding a dedicated "License" section within the model card content.
- Removing the auto-generated comment.

These changes provide a more comprehensive and informative model card for users.

README.md

@@ -1,5 +1,7 @@
 ---
 library_name: transformers
+pipeline_tag: text-generation
+license: mit
 tags:
 - generated_from_trainer
 model-index:
@@ -7,24 +9,27 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # Online-Searcher-Qwen-7B
 
-This model was trained from scratch on an unknown dataset.
+This model is a Qwen2-7B based deep search agent, presented in the paper [SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis](https://huggingface.co/papers/2505.16834).
+
+The code for the paper and model training is available on the [Github repository](https://github.com/RUCAIBox/SimpleDeepSearcher).
 
 ## Model description
 
-More information needed
+SimpleDeepSearcher is a lightweight yet effective framework for enhancing large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. Unlike traditional Retrieval-Augmented Generation (RAG) or Reinforcement Learning (RL)-based methods, SimpleDeepSearcher strategically synthesizes high-quality reasoning trajectories in real-world web environments. This approach enables efficient Supervised Fine-Tuning (SFT) using only a small amount of curated data, resulting in strong performance with significantly reduced computational cost and development complexity.
+
+Key contributions of the framework include:
+-   A real web-based data synthesis framework that simulates realistic user search behaviors, generating multi-turn reasoning and search trajectories.
+-   A multi-criteria data curation strategy that jointly optimizes both input question selection and output response filtering through orthogonal filtering dimensions.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for deep information seeking tasks and complex search scenarios that demand multi-step reasoning and iterative information retrieval from the web. It aims to provide practical insights for efficient deep search systems by leveraging its ability to synthesize high-quality reasoning trajectories.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned using Supervised Fine-Tuning (SFT) on a small, high-quality dataset consisting of only 871 curated samples. This data was strategically synthesized by simulating realistic user interactions in live web search environments, coupled with a multi-criteria curation strategy that optimizes diversity and quality.
 
 ## Training procedure
 
@@ -45,3 +50,7 @@ The following hyperparameters were used during training:
 - Pytorch 2.5.1+cu124
 - Datasets 2.19.0
 - Tokenizers 0.20.3
+
+## License
+
+This project is released under the [MIT License](https://github.com/RUCAIBox/SimpleDeepSearcher/blob/main/LICENSE).