Initial commit
Browse files- .gitattributes +1 -0
- README.md +198 -0
- benchmark_results.txt +1 -0
- benchmark_translations.zip +0 -0
- config.json +41 -0
- generation_config.json +16 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
- source.spm +3 -0
- special_tokens_map.json +1 -0
- target.spm +3 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
.gitattributes
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README.md
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1 |
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---
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library_name: transformers
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language:
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- chm
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- es
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- et
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- fi
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- fkv
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- fr
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- hu
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- it
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- krl
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- liv
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- mrj
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- myv
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- pt
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- se
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- udm
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- vep
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tags:
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- translation
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- opus-mt-tc-bible
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license: apache-2.0
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model-index:
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- name: opus-mt-tc-bible-big-fiu-fra_ita_por_spa
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results:
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- task:
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name: Translation multi-multi
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type: translation
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args: multi-multi
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dataset:
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name: tatoeba-test-v2020-07-28-v2023-09-26
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type: tatoeba_mt
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args: multi-multi
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metrics:
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- name: BLEU
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type: bleu
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value: 48.5
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- name: chr-F
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type: chrf
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value: 0.67829
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---
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# opus-mt-tc-bible-big-fiu-fra_ita_por_spa
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## Table of Contents
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- [Model Details](#model-details)
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- [Uses](#uses)
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- [Risks, Limitations and Biases](#risks-limitations-and-biases)
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- [How to Get Started With the Model](#how-to-get-started-with-the-model)
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- [Training](#training)
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- [Evaluation](#evaluation)
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- [Citation Information](#citation-information)
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- [Acknowledgements](#acknowledgements)
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## Model Details
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Neural machine translation model for translating from Finno-Ugrian languages (fiu) to unknown (fra+ita+por+spa).
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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).
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**Model Description:**
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- **Developed by:** Language Technology Research Group at the University of Helsinki
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- **Model Type:** Translation (transformer-big)
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- **Release**: 2024-08-17
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- **License:** Apache-2.0
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- **Language(s):**
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- Source Language(s): chm est fin fkv hun krl liv mrj myv sme udm vep vro
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- Target Language(s): fra ita por spa
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- Valid Target Language Labels: >>fra<< >>ita<< >>por<< >>spa<< >>xxx<<
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- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
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- **Resources for more information:**
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- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/fiu-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
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- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
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- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
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- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
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- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
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- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
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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. `>>fra<<`
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## Uses
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This model can be used for translation and text-to-text generation.
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## Risks, Limitations and Biases
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**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.**
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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)).
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## How to Get Started With the Model
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A short example code:
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```python
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from transformers import MarianMTModel, MarianTokenizer
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src_text = [
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">>ita<< Hány fényképet csináltatok?",
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">>ita<< Gazdag vagyok."
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]
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model_name = "pytorch-models/opus-mt-tc-bible-big-fiu-fra_ita_por_spa"
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tokenizer = MarianTokenizer.from_pretrained(model_name)
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model = MarianMTModel.from_pretrained(model_name)
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
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for t in translated:
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print( tokenizer.decode(t, skip_special_tokens=True) )
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# expected output:
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# Quante foto avete scattato?
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# Sono ricco.
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```
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You can also use OPUS-MT models with the transformers pipelines, for example:
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```python
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from transformers import pipeline
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-fiu-fra_ita_por_spa")
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print(pipe(">>ita<< Hány fényképet csináltatok?"))
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# expected output: Quante foto avete scattato?
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```
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## Training
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- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
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- **Pre-processing**: SentencePiece (spm32k,spm32k)
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- **Model Type:** transformer-big
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- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip)
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- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
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## Evaluation
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* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/fiu-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
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* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt)
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* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt)
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* benchmark results: [benchmark_results.txt](benchmark_results.txt)
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
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| langpair | testset | chr-F | BLEU | #sent | #words |
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|----------|---------|-------|-------|-------|--------|
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| multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.67829 | 48.5 | 10000 | 65198 |
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## Citation Information
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* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
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```bibtex
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@article{tiedemann2023democratizing,
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title={Democratizing neural machine translation with {OPUS-MT}},
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author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
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journal={Language Resources and Evaluation},
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number={58},
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pages={713--755},
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year={2023},
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publisher={Springer Nature},
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issn={1574-0218},
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doi={10.1007/s10579-023-09704-w}
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}
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@inproceedings{tiedemann-thottingal-2020-opus,
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title = "{OPUS}-{MT} {--} Building open translation services for the World",
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
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month = nov,
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year = "2020",
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address = "Lisboa, Portugal",
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publisher = "European Association for Machine Translation",
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url = "https://aclanthology.org/2020.eamt-1.61",
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pages = "479--480",
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}
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@inproceedings{tiedemann-2020-tatoeba,
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
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author = {Tiedemann, J{\"o}rg},
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booktitle = "Proceedings of the Fifth Conference on Machine Translation",
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month = nov,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.wmt-1.139",
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pages = "1174--1182",
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}
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```
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## Acknowledgements
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The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
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## Model conversion info
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* transformers version: 4.45.1
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* OPUS-MT git hash: 0882077
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* port time: Tue Oct 8 11:00:58 EEST 2024
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* port machine: LM0-400-22516.local
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benchmark_results.txt
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multi-multi tatoeba-test-v2020-07-28-v2023-09-26 0.67829 48.5 10000 65198
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benchmark_translations.zip
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config.json
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{
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"_name_or_path": "pytorch-models/opus-mt-tc-bible-big-fiu-fra_ita_por_spa",
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"activation_dropout": 0.0,
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"activation_function": "relu",
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"architectures": [
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"MarianMTModel"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"classifier_dropout": 0.0,
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"d_model": 1024,
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 4096,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_start_token_id": 59779,
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"decoder_vocab_size": 59780,
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"dropout": 0.1,
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"encoder_attention_heads": 16,
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"encoder_ffn_dim": 4096,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 582,
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"forced_eos_token_id": null,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_length": null,
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"max_position_embeddings": 1024,
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"model_type": "marian",
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"normalize_embedding": false,
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"num_beams": null,
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"num_hidden_layers": 6,
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"pad_token_id": 59779,
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"scale_embedding": true,
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"share_encoder_decoder_embeddings": true,
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"static_position_embeddings": true,
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"torch_dtype": "float32",
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"transformers_version": "4.45.1",
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"use_cache": true,
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"vocab_size": 59780
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}
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generation_config.json
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{
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"_from_model_config": true,
|
3 |
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"bad_words_ids": [
|
4 |
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[
|
5 |
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59779
|
6 |
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]
|
7 |
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],
|
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"bos_token_id": 0,
|
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"decoder_start_token_id": 59779,
|
10 |
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"eos_token_id": 582,
|
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"forced_eos_token_id": 582,
|
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"max_length": 512,
|
13 |
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"num_beams": 4,
|
14 |
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"pad_token_id": 59779,
|
15 |
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"transformers_version": "4.45.1"
|
16 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:41abd5e4bb88c8e15a2cd52274abf5d36c88a74336f0523de00e2d8c8a8cdaa3
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size 950557120
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pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:1118a000c994e8571feb81a68d1dc9d76f7ad029518e0726a55aa03389fbdcce
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size 950608389
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source.spm
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:82528c3ef63b9b0abe90ff8d04bbc6f3936d6eafc7b33dbb121daf1e3fb6e215
|
3 |
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size 822999
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
|
target.spm
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:b4acacc1cb3c1f7cbfed7aad8bf1b8dfeb03c0245c4e6ae3a3b64d86816b2243
|
3 |
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size 819902
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"source_lang": "fiu", "target_lang": "fra+ita+por+spa", "unk_token": "<unk>", "eos_token": "</s>", "pad_token": "<pad>", "model_max_length": 512, "sp_model_kwargs": {}, "separate_vocabs": false, "special_tokens_map_file": null, "name_or_path": "marian-models/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17/fiu-fra+ita+por+spa", "tokenizer_class": "MarianTokenizer"}
|
vocab.json
ADDED
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|
|