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--- |
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language: |
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- zh |
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- en |
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- multilingual |
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license: cc-by-4.0 |
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tags: |
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- translation |
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--- |
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### zho-eng |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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## Model Details |
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- **Model Description:** |
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- **Developed by:** Algmon |
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- **Model Type:** Translation |
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- **Language(s):** |
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- Source Language: Chinese |
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- Target Language: English |
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- **License:** CC-BY-4.0 |
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- **Resources for more information:** |
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- [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 |
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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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)). |
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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) |
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## Training |
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#### System Information |
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* helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 |
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* transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b |
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* port_machine: brutasse |
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* port_time: 2020-08-21-14:41 |
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* src_multilingual: False |
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* tgt_multilingual: False |
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#### Training Data |
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##### Preprocessing |
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* pre-processing: normalization + SentencePiece (spm32k,spm32k) |
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* ref_len: 82826.0 |
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* dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) |
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* download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip) |
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* 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) |
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## Evaluation |
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#### Results |
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* 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) |
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* brevity_penalty: 0.948 |
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## Benchmarks |
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| testset | BLEU | chr-F | |
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|-----------------------|-------|-------| |
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| Tatoeba-test.zho.eng | 36.1 | 0.548 | |
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## How to Get Started With the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en") |
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``` |
