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---
language:
- da
- he
- nb
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-he-gmq
results:
- task:
name: Translation heb-dan
type: translation
args: heb-dan
dataset:
name: flores101-devtest
type: flores_101
args: heb dan devtest
metrics:
- name: BLEU
type: bleu
value: 31.4
- name: chr-F
type: chrf
value: 0.58023
- task:
name: Translation heb-isl
type: translation
args: heb-isl
dataset:
name: flores101-devtest
type: flores_101
args: heb isl devtest
metrics:
- name: BLEU
type: bleu
value: 14.0
- name: chr-F
type: chrf
value: 0.41998
- task:
name: Translation heb-nob
type: translation
args: heb-nob
dataset:
name: flores101-devtest
type: flores_101
args: heb nob devtest
metrics:
- name: BLEU
type: bleu
value: 23.7
- name: chr-F
type: chrf
value: 0.53086
- task:
name: Translation heb-swe
type: translation
args: heb-swe
dataset:
name: flores101-devtest
type: flores_101
args: heb swe devtest
metrics:
- name: BLEU
type: bleu
value: 29.6
- name: chr-F
type: chrf
value: 0.56881
---
# opus-mt-tc-big-he-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Hebrew (he) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-23
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): heb
- Target Language(s): dan nob nor swe
- Language Pair(s): heb-dan heb-nob heb-swe
- Valid Target Language Labels: >>dan<< >>fao<< >>isl<< >>jut<< >>nno<< >>nob<< >>non<< >>nrn<< >>ovd<< >>qer<< >>rmg<< >>swe<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT heb-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-gmq/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>dan<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain 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)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>dan<< כל שלושת הילדים של אליעזר לודוויג זמנהוף נרצחו בשואה.",
">>swe<< הסתבר שטום היה מרגל."
]
model_name = "pytorch-models/opus-mt-tc-big-he-gmq"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Alle tre børn af Eliezer Ludwig Zamenhof blev dræbt i Holocaust.
# Det visade sig att Tom var en spion.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-he-gmq")
print(pipe(">>dan<< כל שלושת הילדים של אליעזר לודוויג זמנהוף נרצחו בשואה."))
# expected output: Alle tre børn af Eliezer Ludwig Zamenhof blev dræbt i Holocaust.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| heb-dan | flores101-devtest | 0.58023 | 31.4 | 1012 | 24638 |
| heb-isl | flores101-devtest | 0.41998 | 14.0 | 1012 | 22834 |
| heb-nob | flores101-devtest | 0.53086 | 23.7 | 1012 | 23873 |
| heb-swe | flores101-devtest | 0.56881 | 29.6 | 1012 | 23121 |
## Citation Information
* Publications: [OPUS-MTBuilding open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation ChallengeRealistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Unions Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:07:45 EEST 2022
* port machine: LM0-400-22516.local