readme: ---
  language: 
  - zh
  - en
  
  tags:
  - translation
  
  license: cc-by-4.0
  ---
  
  ### zho-eng
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  - [Evaluation](#evaluation)
  - [Citation Information](#citation-information)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  
  ## Model Details
  - **Model Description:**
  - **Developed by:** Language Technology Research Group at the University of Helsinki
  - **Model Type:** Translation
  - **Language(s):**  
    - Source Language:  Chinese
    - Target Language: English
  - **License:** CC-BY-4.0
  - **Resources for more information:**
    - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
  
  
  ## Uses
  
  #### Direct Use
  
  This model can be used for translation and text-to-text generation.
  
  
  ## Risks, Limitations and Biases
  
  **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
  
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
  
  Further details about the dataset for this model can be found in the OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md)
  
  ## Training
  
  #### System Information 
  * helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
  * transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
  * port_machine: brutasse
  * port_time: 2020-08-21-14:41
  * src_multilingual: False
  * tgt_multilingual: False
  
  #### Training Data
  ##### Preprocessing
  * pre-processing: normalization + SentencePiece (spm32k,spm32k)
  * ref_len: 82826.0
  * dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT)
  * download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip)
  
  * test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt)
  
  
  ## Evaluation
  
  #### Results
  
  * test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt)
  
  * brevity_penalty: 0.948
  
  
  ## Benchmarks
  
  | testset               | BLEU  | chr-F |
  |-----------------------|-------|-------|
  | Tatoeba-test.zho.eng 	| 36.1 	| 0.548 |
  
  ## Citation Information
  
  ```bibtex
  @InProceedings{TiedemannThottingal:EAMT2020,
    author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
    title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
    booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
    year = {2020},
    address = {Lisbon, Portugal}
   }
  ```
  
  ## How to Get Started With the Model
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
  ```
  
  
  