Improve language tag

README.md

@@ -1,79 +1,91 @@
----
-library_name: transformers
-tags:
-- Inductive
-- Reasoning
-datasets:
-- nsadeq/redis_generate_rule_alignment
-- nsadeq/redis_generate_rule_sft
-- nsadeq/redis_follow_rule_sft
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
----
-
----
-library_name: transformers
-tags:
-- Inductive
-- Reasoning
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
----
-
-# Model Card for Model ID
-
-ReDis-Llama is trained for improved inductive reasoning performance.
-
-### Model Description
-
-- **Developed by:** Nafis Sadeq
-- **Language(s) (NLP):** English
-- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct
-
-### Model Sources [optional]
-
-
-- **Repository:** https://github.com/NafisSadeq/reasoning-distillation
-- **Paper:** https://arxiv.org/abs/2504.10647
-
-
-## How to Get Started with the Model
-
-Follow the instructions here: https://github.com/NafisSadeq/reasoning-distillation
-
-## Training Details
-
-Training details can be found in the paper: https://arxiv.org/abs/2504.10647
-
-## Environmental Impact
-
-- **Hardware Type:** 2 × 48 GB Nvidia RTX A6000 GPUs
-- **Hours used:** 72 hours
-
-### Model Architecture and Objective
-
-This model has the same architecture as Qwen/Qwen2.5-7B-Instruct
-
-### Compute Infrastructure
-
-2 × 48 GB Nvidia RTX A6000 GPUs
-
-## Citation
-
-If you use this model, please cite the following paper.
-
-@misc{sadeq2025improvingincontextlearningreasoning,
-      title={Improving In-Context Learning with Reasoning Distillation}, 
-      author={Nafis Sadeq and Xin Xu and Zhouhang Xie and Julian McAuley and Byungkyu Kang and Prarit Lamba and Xiang Gao},
-      year={2025},
-      eprint={2504.10647},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2504.10647}, 
+---
+library_name: transformers
+tags:
+- Inductive
+- Reasoning
+datasets:
+- nsadeq/redis_generate_rule_alignment
+- nsadeq/redis_generate_rule_sft
+- nsadeq/redis_follow_rule_sft
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+---
+
+---
+library_name: transformers
+tags:
+- Inductive
+- Reasoning
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+---
+
+# Model Card for Model ID
+
+ReDis-Llama is trained for improved inductive reasoning performance.
+
+### Model Description
+
+- **Developed by:** Nafis Sadeq
+- **Language(s) (NLP):** English
+- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct
+
+### Model Sources [optional]
+
+
+- **Repository:** https://github.com/NafisSadeq/reasoning-distillation
+- **Paper:** https://arxiv.org/abs/2504.10647
+
+
+## How to Get Started with the Model
+
+Follow the instructions here: https://github.com/NafisSadeq/reasoning-distillation
+
+## Training Details
+
+Training details can be found in the paper: https://arxiv.org/abs/2504.10647
+
+## Environmental Impact
+
+- **Hardware Type:** 2 × 48 GB Nvidia RTX A6000 GPUs
+- **Hours used:** 72 hours
+
+### Model Architecture and Objective
+
+This model has the same architecture as Qwen/Qwen2.5-7B-Instruct
+
+### Compute Infrastructure
+
+2 × 48 GB Nvidia RTX A6000 GPUs
+
+## Citation
+
+If you use this model, please cite the following paper.
+
+@misc{sadeq2025improvingincontextlearningreasoning,
+      title={Improving In-Context Learning with Reasoning Distillation}, 
+      author={Nafis Sadeq and Xin Xu and Zhouhang Xie and Julian McAuley and Byungkyu Kang and Prarit Lamba and Xiang Gao},
+      year={2025},
+      eprint={2504.10647},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.10647}, 
 }