opus-mt-zh-en
"- Model Details - Uses - Risks, Limitations and Biases - Training - Evaluation - Citation Information - How to Get Started With the Model - **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation - **Language(s):** - S..."
⥠Quick Commands
huggingface-cli download helsinki-nlp/opus-mt-zh-en pip install -U transformers Engineering Specs
⥠Hardware
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đŦTechnical Deep Dive
Full Specifications [+]âž
đ What's Next?
⥠Quick Commands
huggingface-cli download helsinki-nlp/opus-mt-zh-en pip install -U transformers Hardware Compatibility
Multi-Tier Validation Matrix
RTX 3060 / 4060 Ti
RTX 4070 Super
RTX 4080 / Mac M3
RTX 3090 / 4090
RTX 6000 Ada
A100 / H100
Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.
README
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:
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} â {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")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:
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} â {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")đ Limitations & Considerations
- âĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
- âĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
- âĸ FNI scores are relative rankings and may change as new models are added.
- â License Unknown: Verify licensing terms before commercial use.
- âĸ Source: Unknown
Cite this model
Academic & Research Attribution
@misc{hf_model__helsinki_nlp__opus_mt_zh_en,
author = {helsinki-nlp},
title = {undefined Model},
year = {2026},
howpublished = {\url{https://huggingface.co/helsinki-nlp/opus-mt-zh-en}},
note = {Accessed via Free2AITools Knowledge Fortress}
}AI Summary: Based on Hugging Face metadata. Not a recommendation.
đĄī¸ Model Transparency Report
Verified data manifest for traceability and transparency.
đ Identity & Source
- id
- hf-model--helsinki-nlp--opus-mt-zh-en
- author
- helsinki-nlp
- tags
- transformerspytorchtfrustmariantext2text-generationtranslationzhenlicense:cc-by-4.0endpoints_compatibledeploy:azureregion:us
âī¸ Technical Specs
- architecture
- MarianMTModel
- params billions
- null
- context length
- null
đ Engagement & Metrics
- likes
- 540
- downloads
- 382,824
Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)