Ct2fast Opus Mt En De
| Entity Passport | |
| Registry ID | hf-model--michaelfeil--ct2fast-opus-mt-en-de |
| License | CC-BY-4.0 |
| Provider | huggingface |
Cite this model
Academic & Research Attribution
@misc{hf_model__michaelfeil__ct2fast_opus_mt_en_de,
author = {michaelfeil},
title = {Ct2fast Opus Mt En De Model},
year = {2026},
howpublished = {\url{https://huggingface.co/michaelfeil/ct2fast-opus-mt-en-de}},
note = {Accessed via Free2AITools Knowledge Fortress}
}đŦTechnical Deep Dive
Full Specifications [+]âž
Quick Commands
huggingface-cli download michaelfeil/ct2fast-opus-mt-en-de pip install -U transformers âī¸ Nexus Index V2.0
đŦ Index Insight
FNI V2.0 for Ct2fast Opus Mt En De: Semantic (S:50), Authority (A:0), Popularity (P:2), Recency (R:12), Quality (Q:50).
Verification Authority
đ What's Next?
Technical Deep Dive
# Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of Helsinki-NLP/opus-mt-en-de
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0Converted using
ct2-transformers-converter --model Helsinki-NLP/opus-mt-en-de --output_dir /home/michael/tmp-ct2fast-opus-mt-en-de --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16Checkpoint compatible to ctranslate2 and hf-hub-ctranslate2
compute_type=int8_float16fordevice="cuda"compute_type=int8fordevice="cpu"
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-en-de"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
Original description
opus-mt-en-de
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: English
- Target Language: German
- 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: en-de
Training Data
Preprocessing
pre-processing: normalization + SentencePiece
dataset: opus
download original weights: opus-2020-02-26.zip
test set translations: opus-2020-02-26.test.txt
Evaluation
Results
- test set scores: opus-2020-02-26.eval.txt
Benchmarks
| testset | BLEU | chr-F |
|---|---|---|
| newssyscomb2009.en.de | 23.5 | 0.540 |
| news-test2008.en.de | 23.5 | 0.529 |
| newstest2009.en.de | 22.3 | 0.530 |
| newstest2010.en.de | 24.9 | 0.544 |
| newstest2011.en.de | 22.5 | 0.524 |
| newstest2012.en.de | 23.0 | 0.525 |
| newstest2013.en.de | 26.9 | 0.553 |
| newstest2015-ende.en.de | 31.1 | 0.594 |
| newstest2016-ende.en.de | 37.0 | 0.636 |
| newstest2017-ende.en.de | 29.9 | 0.586 |
| newstest2018-ende.en.de | 45.2 | 0.690 |
| newstest2019-ende.en.de | 40.9 | 0.654 |
| Tatoeba.en.de | 47.3 | 0.664 |
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-en-de")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
â ī¸ Incomplete Data
Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.
View Original Source âđ 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.
Social Proof
AI Summary: Based on Hugging Face metadata. Not a recommendation.
đĄī¸ Model Transparency Report
Technical metadata sourced from upstream repositories.
đ Identity & Source
- id
- hf-model--michaelfeil--ct2fast-opus-mt-en-de
- slug
- michaelfeil--ct2fast-opus-mt-en-de
- source
- huggingface
- author
- michaelfeil
- license
- CC-BY-4.0
- tags
- transformers, ctranslate2, translation, license:cc-by-4.0, endpoints_compatible, region:us
âī¸ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
- pipeline tag
- translation
đ Engagement & Metrics
- downloads
- 23
- stars
- 0
- forks
- 0
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