Update README.md

README.md

@@ -1,61 +1,64 @@
 ---
-license: other
+license: gemma
 license_name: license
 license_link: LICENSE
 base_model:
 - google/gemma-2-9b
 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
 
-## Model Details
 
-### Model Description
 
-GemmaX2-28-9B-Pretrain is a language model that results from continual pretraining of Gemma2-9B on a mix of 56 billion tokens of monolingual and parallel data in 28 different languages — 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. 
-
-GemmaX2-28-9B-v0.1 is the first model in the series. Compared to the current open-source state-of-the-art (SOTA) models, it achieves optimal translation performance across 28 languages, even reaching performance comparable to GPT-4 and Google Translate, indicating it has achieved translation capabilities on par with industry standards. 
+## Model Description
+GemmaX2-28-9B-Pretrain is a language model developed through continual pretraining of Gemma2-9B using a mix of 56 billion tokens from both monolingual and parallel data across 28 different languages. Please find more details in our paper: [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://arxiv.org/pdf/2502.02481).
 
 - **Developed by:** Xiaomi
-- **Model type:** A 9B parameter model base on Gemma2, we obtained GemmaX2-28-9B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data. Afterward, GemmaX2-28-9B-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. 
-- **License:**  gemma
+- **Model type:** GemmaX2-28-9B-Pretrain is obtained by continually pretraining Gemma2-9B on a large amount of monolingual and parallel data. Subsequently, GemmaX2-28-9B-v0.1 is derived through supervised finetuning on a small set of high-quality translation instruction data.
+- **Languages:** 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. 
 
-### Model Source
+## Model Source
 
 - paper: [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://arxiv.org/pdf/2502.02481)
 
-### Model Performance
+## Model Performance
 
 ![Experimental Result](main.png)
 
-## Limitations
-
-GemmaX2-28-9B-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 GemmaX2-28-9B's translation performance, and future models will be release in due course.
-
-
-
-## Run the model
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "ModelSpace/GemmaX2-28-9B-Pretrain"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-model = AutoModelForCausalLM.from_pretrained(model_id)
-
-text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
-inputs = tokenizer(text, return_tensors="pt")
 
-outputs = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+## Training Data
 
-### Training Data
+We collect monolingual data from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). For parallel data, we collect all Chinese-centric and English-centric parallel datasets from the [OPUS](https://opus.nlpl.eu/) collection up to August 2024 and conduct a series of filtering processes, such as language identification, semantic duplication filtering, quality filtering, and more.
 
-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