Improve language tag

README.md

@@ -1,63 +1,74 @@
-
----
-license: apache-2.0
-datasets:
-- allenai/MADLAD-400
-language:
-- bn
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-library_name: transformers
----
-# Qwen2.5 7B Instruct for Bengali: Vocabulary expansion
-
-This model is built on top of Qwen2.5 7B Instruct adapted for Bengali using 500M target language tokens sampled from MADLAD-400. It has an additional target vocabulary of 10K.
-
-## Model Details
-
-* **Vocabulary**: This model has an additional target vocabulary of 10K.
-* **Target vocabulary initialization**: The target weights of the embedding and LM head were initialized using mean initialization.
-* **Training**: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400.
-
-
-## Model Description
-
-- **Language:** Bengali
-- **License:** Apache 2.0
-- **Fine-tuned from model:** Qwen/Qwen2.5-7B-Instruct
-
-
-## Model Sources
-
-- **Repository:** https://github.com/gucci-j/chat-cve
-- **Paper:** https://arxiv.org/abs/2412.11704
-
-
-## How to Get Started with the Model
-Use the code below to get started with the model.
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-model = AutoModelForCausalLM.from_pretrained(
-    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-bn-madlad-mean-tuned"
-)
-tokenizer = AutoTokenizer.from_pretrained(
-    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-bn-madlad-mean-tuned"
-)
-```
-
-
-## Citation
-```
-@misc{yamaguchi2024vocabularyexpansionchatmodels,
-      title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, 
-      author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
-      year={2024},
-      eprint={2412.11704},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2412.11704},
-}
-```
-
-
+---
+license: apache-2.0
+datasets:
+- allenai/MADLAD-400
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+library_name: transformers
+---
+# Qwen2.5 7B Instruct for Bengali: Vocabulary expansion
+
+This model is built on top of Qwen2.5 7B Instruct adapted for Bengali using 500M target language tokens sampled from MADLAD-400. It has an additional target vocabulary of 10K.
+
+## Model Details
+
+* **Vocabulary**: This model has an additional target vocabulary of 10K.
+* **Target vocabulary initialization**: The target weights of the embedding and LM head were initialized using mean initialization.
+* **Training**: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400.
+
+
+## Model Description
+
+- **Language:** Bengali
+- **License:** Apache 2.0
+- **Fine-tuned from model:** Qwen/Qwen2.5-7B-Instruct
+
+
+## Model Sources
+
+- **Repository:** https://github.com/gucci-j/chat-cve
+- **Paper:** https://arxiv.org/abs/2412.11704
+
+
+## How to Get Started with the Model
+Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-bn-madlad-mean-tuned"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-bn-madlad-mean-tuned"
+)
+```
+
+
+## Citation
+```
+@misc{yamaguchi2024vocabularyexpansionchatmodels,
+      title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, 
+      author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
+      year={2024},
+      eprint={2412.11704},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.11704},
+}
+```
+
+