Randeng Deltalm 362M Zh En

🚀 Randeng-Deltalm-362M-Zh-En

This is a Chinese-to-English translation model fine-tuned on the Detalm base using the Fengshen framework, trained on a collected Chinese-English dataset (30 million entries in total) and the Chinese-English parallel data from IWSLT (200,000 entries).

• Main Page: Fengshenbang
• Github: Fengshenbang-LM

🚀 Quick Start

✨ Features

Using the Fengshen-LM framework and fine-tuning based on detalm, we obtained a translation model in the Chinese->English direction.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

# Need to download modeling_deltalm.py from Fengshenbang-LM github repo in advance,
# or you can download modeling_deltalm.py use https://huggingface.co/IDEA-CCNL/Randeng-Deltalm-362M-Zn-En/resolve/main/modeling_deltalm.py
# Strongly recommend you git clone the Fengshenbang-LM repo:
# 1. git clone https://github.com/IDEA-CCNL/Fengshenbang-LM
# 2. cd Fengshenbang-LM/fengshen/models/deltalm/

from modeling_deltalm import DeltalmForConditionalGeneration
from transformers import AutoTokenizer

model = DeltalmForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-Deltalm-362M-Zh-En")
tokenizer = AutoTokenizer.from_pretrained("microsoft/infoxlm-base")

text = "尤其在夏天,如果你决定徒步穿越雨林,就需要小心蚊子。"
inputs = tokenizer(text, max_length=512, return_tensors="pt")

generate_ids = model.generate(inputs["input_ids"], max_length=512)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

# model Output: 

📚 Documentation

Model Taxonomy

Model Information

Reference Paper: DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders

Performance

📄 License

No license information is provided in the original document, so this section is skipped.

📖 Citation

If you are using the resource for your work, please cite our paper:

@article{fengshenbang,
  author    = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

You can also cite our website:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}