Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,98 +1,110 @@
----
-license: apache-2.0
-datasets:
-- virtuoussy/Math-RLVR
-- virtuoussy/Multi-subject-RLVR
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
----
-
-Model Details
-
-The generative reward model used in paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
-
-Inputting the question, label and the response to be evaluated, the model will judge if the response is right.
-
-## **Quick start**
-
-> ```python
-> # Load model directly
-> from transformers import AutoTokenizer, AutoModelForCausalLM
-> 
-> tokenizer = AutoTokenizer.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
-> model = AutoModelForCausalLM.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
-> 
-> PROMPT= '''
-> Given a problem, determine whether the final answer in the provided (incomplete) solution process matches the reference answer.  
-> The reference answer may be one single option character (e.g., A, B, C, D), a numerical value, an expression, or a list of answers if multiple questions are involved.  
-> **The reference answer may be in Chinese or another language, but your evaluation should be language-agnostic.**  
-> 
-> Your task:  
-> - Compare the final output of the solution process with the reference answer.  
-> - If they **match exactly**, output **YES**.  
-> - If they **do not match**, output **NO**.  
-> - If the solution process is unclear, incomplete, or ambiguous, assume it is incorrect and output **NO**.  
-> 
-> Your output must be strictly **'YES'** or **'NO'**, with no additional words, punctuation, or explanation.  
-> 
-> ---
-> 
-> **Question:**  
-> {question}  
-> 
-> **Solution Process (Final Step Only):**  
-> {response}  
-> 
-> **Reference Answer:**  
-> {reference}  
-> 
-> **Output:**  
-> '''
-> 
-> 
-> question="The founder of China's first public kindergarten teacher training school - Jiangxi Experimental Kindergarten Teacher School is (　　)."
-> label="Chen Heqin"
-> answer="heqin chen"
-> 
-> prompt_question = PROMPT.format(question=question, reference=label, response=answer)
-> messages=[
->            {"role": "system", "content": "You are a helpful assistant."},
->            {"role": "user", "content": prompt_question},
->          ]
-> input_ids=tokenizer.apply_chat_template(messages,return_tensors="pt")
-> output=model.generate(input_ids,do_sample=False)
-> judgement=tokenizer.decode(output[0][input_ids.shape[1]:],skip_special_tokens=True)
-> print("Model judgement: ",judgement)
-> ```
-
-## Use as a remote reward
-
-```bash
-# launch a remote reward
-bash launch_reward.sh {MODEL_PATH} {ANSWER_PATH} {METRIC}
-
-# MODEL_PATH: the path of our generative reward model.
-# ANSWER_PATH: the path of the training data.
-# METRIC: greedy/prob
-# This will launch a reward at http://127.0.0.1:8000/get_reward
-
-# train
-bash train.sh {METHOD} {PRETRAIN_PATH} {DATA_PATH} {REWARD_API}
-
-# Both train.sh and launch_reward.sh can be found in the model directory.
-# We will release our github repo soon!
-```
-
-
-## Citation
-
-```bibtex
-@article{su2025expanding,
-  title={Expanding RL with Verifiable Rewards Across Diverse Domains},
-  author={Su, Yi and Yu, Dian and Song, Linfeng and Li, Juntao and Mi, Haitao and Tu, Zhaopeng and Zhang, Min and Yu, Dong},
-  journal={arXiv preprint arXiv:2503.23829},
-  year={2025}
-}
+---
+license: apache-2.0
+datasets:
+- virtuoussy/Math-RLVR
+- virtuoussy/Multi-subject-RLVR
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
+
+Model Details
+
+The generative reward model used in paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
+
+Inputting the question, label and the response to be evaluated, the model will judge if the response is right.
+
+## **Quick start**
+
+> ```python
+> # Load model directly
+> from transformers import AutoTokenizer, AutoModelForCausalLM
+> 
+> tokenizer = AutoTokenizer.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
+> model = AutoModelForCausalLM.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
+> 
+> PROMPT= '''
+> Given a problem, determine whether the final answer in the provided (incomplete) solution process matches the reference answer.  
+> The reference answer may be one single option character (e.g., A, B, C, D), a numerical value, an expression, or a list of answers if multiple questions are involved.  
+> **The reference answer may be in Chinese or another language, but your evaluation should be language-agnostic.**  
+> 
+> Your task:  
+> - Compare the final output of the solution process with the reference answer.  
+> - If they **match exactly**, output **YES**.  
+> - If they **do not match**, output **NO**.  
+> - If the solution process is unclear, incomplete, or ambiguous, assume it is incorrect and output **NO**.  
+> 
+> Your output must be strictly **'YES'** or **'NO'**, with no additional words, punctuation, or explanation.  
+> 
+> ---
+> 
+> **Question:**  
+> {question}  
+> 
+> **Solution Process (Final Step Only):**  
+> {response}  
+> 
+> **Reference Answer:**  
+> {reference}  
+> 
+> **Output:**  
+> '''
+> 
+> 
+> question="The founder of China's first public kindergarten teacher training school - Jiangxi Experimental Kindergarten Teacher School is (　　)."
+> label="Chen Heqin"
+> answer="heqin chen"
+> 
+> prompt_question = PROMPT.format(question=question, reference=label, response=answer)
+> messages=[
+>            {"role": "system", "content": "You are a helpful assistant."},
+>            {"role": "user", "content": prompt_question},
+>          ]
+> input_ids=tokenizer.apply_chat_template(messages,return_tensors="pt")
+> output=model.generate(input_ids,do_sample=False)
+> judgement=tokenizer.decode(output[0][input_ids.shape[1]:],skip_special_tokens=True)
+> print("Model judgement: ",judgement)
+> ```
+
+## Use as a remote reward
+
+```bash
+# launch a remote reward
+bash launch_reward.sh {MODEL_PATH} {ANSWER_PATH} {METRIC}
+
+# MODEL_PATH: the path of our generative reward model.
+# ANSWER_PATH: the path of the training data.
+# METRIC: greedy/prob
+# This will launch a reward at http://127.0.0.1:8000/get_reward
+
+# train
+bash train.sh {METHOD} {PRETRAIN_PATH} {DATA_PATH} {REWARD_API}
+
+# Both train.sh and launch_reward.sh can be found in the model directory.
+# We will release our github repo soon!
+```
+
+
+## Citation
+
+```bibtex
+@article{su2025expanding,
+  title={Expanding RL with Verifiable Rewards Across Diverse Domains},
+  author={Su, Yi and Yu, Dian and Song, Linfeng and Li, Juntao and Mi, Haitao and Tu, Zhaopeng and Zhang, Min and Yu, Dong},
+  journal={arXiv preprint arXiv:2503.23829},
+  year={2025}
+}
 ```