Improve language tag

README.md

@@ -1,80 +1,92 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
----
-# GAIR/DeepResearcher-7b
-
-## Introduction
-
-DeepResearcher is the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, including the ability to formulate plans, cross-validate information from multiple sources, engage in self-reflection to redirect research, and maintain honesty when unable to find definitive answers.
-
-## Model Details
-
-- **License:** Apache 2.0
-- **Model type:** Reinforcement learning-based LLM (Large Language Model).
-- **Language(s):** The model is designed for tasks in English.
-- **Finetuned from model:** The model is built using the Qwen2.5-7B-Instruct architecture .
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-### Model Sources
-
-- **Repository:** [DeepResearcher GitHub](https://github.com/GAIR-NLP/DeepResearcher) .
-- **Paper:** [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://arxiv.org/abs/2504.03160)
-
-
-## How to Get Started with the Model
-
-To get started, you can visit the [DeepResearcher repository](https://github.com/GAIR-NLP/DeepResearcher) on GitHub, where the model's code and setup instructions are provided .
-
-## Training Details
-
-### Training Data
-
-The model was trained on open-domain question-answering datasets, including:
-- **NaturalQuestions (NQ)**
-- **TriviaQA (TQ)**
-- **HotpotQA**
-- **2Wiki MultiHopQA** 
-
-### Training Procedure
-
-DeepResearcher was trained using reinforcement learning (RL) with the Group Relative Policy Optimization (GRPO) algorithm. It was tested in both in-domain (NQ, TQ, HotpotQA) and out-of-domain (Musique, Bamboogle, PopQA) settings .
-
-## Evaluation
-
-### Testing Data
-
-The model was evaluated on several datasets, including:
-- **NQ (Natural Questions)**
-- **TQ (TriviaQA)**
-- **HotpotQA**
-- **2Wiki**
-- **Musique**
-- **Bamboogle**
-- **PopQA** .
-  
-
-### Results
-
-DeepResearcher outperforms all baseline models, achieving a substantial improvement in task completion across the datasets, particularly in out-of-domain scenarios.
-
-
-## Citation
-```
-@misc{zheng2025deepresearcherscalingdeepresearch,
-      title={DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments}, 
-      author={Yuxiang Zheng and Dayuan Fu and Xiangkun Hu and Xiaojie Cai and Lyumanshan Ye and Pengrui Lu and Pengfei Liu},
-      year={2025},
-      eprint={2504.03160},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2504.03160}, 
-}
-```
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
+# GAIR/DeepResearcher-7b
+
+## Introduction
+
+DeepResearcher is the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, including the ability to formulate plans, cross-validate information from multiple sources, engage in self-reflection to redirect research, and maintain honesty when unable to find definitive answers.
+
+## Model Details
+
+- **License:** Apache 2.0
+- **Model type:** Reinforcement learning-based LLM (Large Language Model).
+- **Language(s):** The model is designed for tasks in English.
+- **Finetuned from model:** The model is built using the Qwen2.5-7B-Instruct architecture .
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+### Model Sources
+
+- **Repository:** [DeepResearcher GitHub](https://github.com/GAIR-NLP/DeepResearcher) .
+- **Paper:** [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://arxiv.org/abs/2504.03160)
+
+
+## How to Get Started with the Model
+
+To get started, you can visit the [DeepResearcher repository](https://github.com/GAIR-NLP/DeepResearcher) on GitHub, where the model's code and setup instructions are provided .
+
+## Training Details
+
+### Training Data
+
+The model was trained on open-domain question-answering datasets, including:
+- **NaturalQuestions (NQ)**
+- **TriviaQA (TQ)**
+- **HotpotQA**
+- **2Wiki MultiHopQA** 
+
+### Training Procedure
+
+DeepResearcher was trained using reinforcement learning (RL) with the Group Relative Policy Optimization (GRPO) algorithm. It was tested in both in-domain (NQ, TQ, HotpotQA) and out-of-domain (Musique, Bamboogle, PopQA) settings .
+
+## Evaluation
+
+### Testing Data
+
+The model was evaluated on several datasets, including:
+- **NQ (Natural Questions)**
+- **TQ (TriviaQA)**
+- **HotpotQA**
+- **2Wiki**
+- **Musique**
+- **Bamboogle**
+- **PopQA** .
+  
+
+### Results
+
+DeepResearcher outperforms all baseline models, achieving a substantial improvement in task completion across the datasets, particularly in out-of-domain scenarios.
+
+
+## Citation
+```
+@misc{zheng2025deepresearcherscalingdeepresearch,
+      title={DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments}, 
+      author={Yuxiang Zheng and Dayuan Fu and Xiangkun Hu and Xiaojie Cai and Lyumanshan Ye and Pengrui Lu and Pengfei Liu},
+      year={2025},
+      eprint={2504.03160},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2504.03160}, 
+}
+```