| | --- |
| | language: |
| | - zh |
| | - en |
| | - multilingual |
| | license: cc-by-4.0 |
| | tags: |
| | - translation |
| | --- |
| | |
| | ### 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:** Algmon |
| | - **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 | |
| |
|
| | ## 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") |
| | ``` |
