| --- |
| 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) |
|
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|
|
| ## Uses |
|
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| #### Direct Use |
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| This model can be used for translation and text-to-text generation. |
|
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|
|
| ## 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 |
|
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|
|
| ## 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") |
| ``` |
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