Improve language tag

README.md

@@ -1,59 +1,73 @@
----
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-datasets: gagan3012/Sky-T1_preference_data_10k_reward_templated
-library_name: transformers
-model_name: Qwen-2.5-reasoning-verifier
-tags:
-- generated_from_trainer
-- trl
-- reward-trainer
-licence: license
----
-
-# Model Card for Qwen-2.5-reasoning-verifier
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [gagan3012/Sky-T1_preference_data_10k_reward_templated](https://huggingface.co/datasets/gagan3012/Sky-T1_preference_data_10k_reward_templated) dataset.
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="gagan3012/Qwen-2.5-reasoning-verifier", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/arocr/huggingface/runs/hlsna8w4) 
-
-
-This model was trained with Reward.
-
-### Framework versions
-
-- TRL: 0.14.0.dev0
-- Transformers: 4.47.1
-- Pytorch: 2.5.1+cu121
-- Datasets: 3.2.0
-- Tokenizers: 0.21.0
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+datasets: gagan3012/Sky-T1_preference_data_10k_reward_templated
+library_name: transformers
+model_name: Qwen-2.5-reasoning-verifier
+tags:
+- generated_from_trainer
+- trl
+- reward-trainer
+licence: license
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# Model Card for Qwen-2.5-reasoning-verifier
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [gagan3012/Sky-T1_preference_data_10k_reward_templated](https://huggingface.co/datasets/gagan3012/Sky-T1_preference_data_10k_reward_templated) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="gagan3012/Qwen-2.5-reasoning-verifier", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/arocr/huggingface/runs/hlsna8w4) 
+
+
+This model was trained with Reward.
+
+### Framework versions
+
+- TRL: 0.14.0.dev0
+- Transformers: 4.47.1
+- Pytorch: 2.5.1+cu121
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
 ```