Update README.md

README.md

@@ -9,6 +9,35 @@ base_model:
 - ModelSpace/GemmaX2-28-2B-Pretrain
 pipeline_tag: translation
 library_name: transformers
+language:
+- ar
+- bn
+- cs
+- de
+- en
+- es
+- fa
+- fr
+- he
+- hi
+- id
+- it
+- ja
+- km
+- ko
+- lo
+- ms
+- my
+- nl
+- pl
+- pt
+- ru
+- th
+- tl
+- tr
+- ur
+- vi
+- zh
 ---
 
 # Model Card for GemmaX2-28
@@ -23,7 +52,7 @@ GemmaX2-28-2B-v0.1 is the model version of GemmaX2-28-2B-Pretrain after SFT.
 
 - **Developed by:** Xiaomi
 - **Model type:** A 2B parameter model base on Gemma2, we obtained GemmaX2-28-2B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data. Afterward, GemmaX2-28-2B-v0.1 was derived through supervised fine-tuning on a small set of high-quality instruction data.
-- **Language(s) (NLP):** Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese. 
+- **Language(s):** Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, Polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese. 
 - **License:**  gemma
 
 ### Model Source
@@ -34,11 +63,6 @@ GemmaX2-28-2B-v0.1 is the model version of GemmaX2-28-2B-Pretrain after SFT.
 
 ![Experimental Result](main.png)
 
-## Limitations
-
-GemmaX2-28-2B-v0.1 supports only the 28 most commonly used languages and does not guarantee powerful translation performance for other languages. Additionally, we will continue to improve GemmaX2-28-2B's translation performance, and future models will be release in due course.
-
-
 
 ## Run the model
 
@@ -57,9 +81,6 @@ outputs = model.generate(**inputs, max_new_tokens=50)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-### Training Data
-
-We collected monolingual data from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). For parallel data, we collected all Chinese-centric and English-centric parallel dataset from the [OPUS](https://opus.nlpl.eu/) collection up to Auguest 2024 and underwent a series of filtering processes, such as language detection, semantic duplication filtering, quality filtering, and more.
 
 ## Citation 
 
@@ -73,4 +94,9 @@ We collected monolingual data from [CulturaX](https://huggingface.co/datasets/uo
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.02481}, 
 }
-```
+```
+
+
+## Limitations
+
+GemmaX2-28-2B-v0.1 supports only the 28 most commonly used languages and does not guarantee powerful translation performance for other languages. Additionally, we will continue to improve GemmaX2-28-2B's translation performance, and future models will be release in due course.