source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
Open-weight model · Translation
opus-mt-zh-en
by Helsinki-NLP Research Group Helsinki-NLP/opus-mt-zh-en
This model can be used for translation and text-to-text generation. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
Parameters—
Context512
Weights1.2 GB
Licensecc-by-4.0
AccessOpen weights
Monthly Downloads177.8k
Model Card
By Helsinki-NLP Research Group, published under cc-by-4.0, revision cf109095479d.
This model can be used for translation and text-to-text generation. 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) and Bender et al. (2021)). Further details about the dataset for this model can be found in the OPUS readme: zho-eng helsinkigitsha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 transformersgitsha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b portmachine: brutasse porttime: 2020-08-21-14:41 srcmultilingual: False tgtmultilingual: False reflen: 82826.0 brevitypenalty…
Read Helsinki-NLP Research Group's full model card
zho-eng
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Citation Information
- 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
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) and Bender et al. (2021)).
Further details about the dataset for this model can be found in the OPUS readme: zho-eng
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
-
download original weights: opus-2020-07-17.zip
-
test set translations: opus-2020-07-17.test.txt
Evaluation
Results
-
test set scores: 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
@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
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")
Configuration
- Architecture
- MarianMTModel
- Context length (tokens)
- 512
- Layers
- 6
- Vocabulary size
- 65,001
- Model type
- marian
Identity and Version
- Repository
- Helsinki-NLP/opus-mt-zh-en
- Publisher
- Helsinki-NLP Research Group
- Task
- Translation
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- zh, en
- Revision
- cf109095479db38d6df799875e34039d4938aaa6
- First published
- 2022-03-02
- Last updated
- 2023-08-16
Files and Weights
12 files, 1.2 GB in total. The weights are 3 files totalling 1.2 GB in bin, h5, ot.
Weights3 files · 1.2 GB
Configuration3 files · 3.2 KB
Tokenizer2 files · 1.6 MB
Documentation1 file · 3.2 KB
Other2 files · 1.6 MB
Repository1 file · 345 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 312.1 MB | 9d8ceb91d103 |
| rust_model.ot | Weights | 578.4 MB | 859d0e253169 |
| tf_model.h5 | Weights | 312.6 MB | 5cfa433c98ae |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 293 B | — |
| metadata.json | Configuration | 1.5 KB | — |
| README.md | Documentation | 3.2 KB | — |
| source.spm | Other | 804.7 KB | — |
| target.spm | Other | 806.5 KB | — |
| .gitattributes | Repository | 345 B | — |
| tokenizer_config.json | Tokenizer | 44 B | — |
| vocab.json | Tokenizer | 1.6 MB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 1.2 GB
Download from Helsinki-NLP Research Group
Released by Helsinki-NLP Research Group through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About opus-mt-zh-en
Can I use opus-mt-zh-en commercially?
Yes. opus-mt-zh-en is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.
What is opus-mt-zh-en's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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