🚀 opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul
This is a neural machine translation model designed to translate from German, English, French, Portuguese, and Spanish to multiple languages. It's part of the OPUS - MT project, aiming to make NMT models accessible for various languages.
🚀 Quick Start
This model can be used with the transformers
library. Here's a basic Python code example to get you started:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul")
model = AutoModelForSeq2SeqLM.from_pretrained("opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul")
input_text = "Your input text here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translated_text)
✨ Features
- Multi - language Translation: Capable of translating from German, English, French, Portuguese, and Spanish to a wide range of target languages.
- Part of OPUS - MT Project: Leverages the resources and framework of the OPUS - MT project, which aims to provide accessible neural machine translation models for many languages.
- Transformer - based: Built on the transformer architecture, known for its effectiveness in natural language processing tasks.
📚 Documentation
Model Details
This is a neural machine translation model for translating from an unknown combination (German, English, French, Portuguese, Spanish) to multiple languages. Note that due to limited training data for most of the listed languages, the model may not support many of them well. Translation performance varies significantly, and it may not work at all for a large number of language pairs.
This model is part of the [OPUS - MT project](https://github.com/Helsinki - NLP/Opus - MT), an initiative to make neural machine translation models widely available for many languages worldwide. All models are initially trained using the [Marian NMT](https://marian - nmt.github.io/) framework, an efficient NMT implementation written in pure C++. The models have been converted to PyTorch using the transformers
library by Hugging Face. Training data is sourced from OPUS, and training pipelines follow the procedures of [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train).
Property |
Details |
Model Type |
Translation (transformer - big) |
Developed by |
Language Technology Research Group at the University of Helsinki |
Release |
2024 - 05 - 30 |
License |
Apache - 2.0 |
Source Language(s) |
German, English, French, Portuguese, Spanish |
Target Language(s) |
A large set including aai, aar, aau, etc. (see full list in original README) |
Uses
The model can be used for general translation tasks between the supported source and target languages. However, due to the limitations mentioned above, it's recommended to test its performance on specific language pairs before large - scale use.
Risks, Limitations and Biases
- Limited Language Support: Many of the target languages have limited training data, which may lead to poor translation quality or even non - functional translation for some language pairs.
- Bias: As with any machine learning model, there may be biases present in the training data that could affect the translation results.
Training
The model is trained using data from OPUS. The training pipelines follow the procedures of [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train).
Evaluation
On the tatoeba - test - v2020 - 07 - 28 - v2023 - 09 - 26
dataset for the Translation multi - multi
task:
Metric |
Value |
BLEU |
29.2 |
chr - F |
0.55024 |
Citation Information
Please refer to the official OPUS - MT project documentation for proper citation information.
Acknowledgements
Thanks to the Language Technology Research Group at the University of Helsinki for developing this model, and the contributors to the OPUS - MT project, Marian NMT, and the transformers
library.
⚠️ Important Note
Due to limited training data, the model may not work well for many of the listed target languages. It's advisable to test the model on specific language pairs before use.
💡 Usage Tip
When using the model, start with small - scale tests on your specific language pairs to evaluate its performance.