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README.md

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+---
+language:
+- en
+- fr
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-en-fr
+  results:
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng fra devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 52.2
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: multi30k_test_2016_flickr
+      type: multi30k-2016_flickr
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 52.4
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: multi30k_test_2017_flickr
+      type: multi30k-2017_flickr
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 52.8
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: multi30k_test_2017_mscoco
+      type: multi30k-2017_mscoco
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 54.7
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: multi30k_test_2018_flickr
+      type: multi30k-2018_flickr
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 43.7
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: news-test2008
+      type: news-test2008
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 27.6
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newsdiscussdev2015
+      type: newsdiscussdev2015
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.4
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newsdiscusstest2015
+      type: newsdiscusstest2015
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.3
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 53.2
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: tico19-test
+      type: tico19-test
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.6
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2009
+      type: wmt-2009-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.0
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2010
+      type: wmt-2010-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.5
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2011
+      type: wmt-2011-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.0
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.8
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.6
+  - task:
+      name: Translation eng-fra
+      type: translation
+      args: eng-fra
+    dataset:
+      name: newstest2014
+      type: wmt-2014-news
+      args: eng-fra
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 41.9
+---
+# opus-mt-tc-big-en-fr
+
+Neural machine translation model for translating from English (en) to French (fr).
+
+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).
+
+* Publications: [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.)
+
+```
+@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",
+}
+```
+
+## Model info
+
+* Release: 2022-03-09
+* source language(s): eng
+* target language(s): fra
+* model: transformer-big
+* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
+* more information released models: [OPUS-MT eng-fra README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-fra/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "The Portuguese teacher is very demanding.",
+    "When was your last hearing test?"
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-en-fr"
+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:
+#     Le professeur de portugais est très exigeant.
+#     Quand a eu lieu votre dernier test auditif ?
+```
+
+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-en-fr")
+print(pipe("The Portuguese teacher is very demanding."))
+
+# expected output: Le professeur de portugais est très exigeant.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)
+* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opusTCv20210807+bt_transformer-big_2022-03-09.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 |
+|----------|---------|-------|-------|-------|--------|
+| eng-fra | tatoeba-test-v2021-08-07 | 0.69621 | 53.2 | 12681 | 106378 |
+| eng-fra | flores101-devtest | 0.72494 | 52.2 | 1012 | 28343 |
+| eng-fra | multi30k_test_2016_flickr | 0.72361 | 52.4 | 1000 | 13505 |
+| eng-fra | multi30k_test_2017_flickr | 0.72826 | 52.8 | 1000 | 12118 |
+| eng-fra | multi30k_test_2017_mscoco | 0.73547 | 54.7 | 461 | 5484 |
+| eng-fra | multi30k_test_2018_flickr | 0.66723 | 43.7 | 1071 | 15867 |
+| eng-fra | newsdiscussdev2015 | 0.60471 | 33.4 | 1500 | 27940 |
+| eng-fra | newsdiscusstest2015 | 0.64915 | 40.3 | 1500 | 27975 |
+| eng-fra | newssyscomb2009 | 0.58903 | 30.7 | 502 | 12331 |
+| eng-fra | news-test2008 | 0.55516 | 27.6 | 2051 | 52685 |
+| eng-fra | newstest2009 | 0.57907 | 30.0 | 2525 | 69263 |
+| eng-fra | newstest2010 | 0.60156 | 33.5 | 2489 | 66022 |
+| eng-fra | newstest2011 | 0.61632 | 35.0 | 3003 | 80626 |
+| eng-fra | newstest2012 | 0.59736 | 32.8 | 3003 | 78011 |
+| eng-fra | newstest2013 | 0.59700 | 34.6 | 3000 | 70037 |
+| eng-fra | newstest2014 | 0.66686 | 41.9 | 3003 | 77306 |
+| eng-fra | tico19-test | 0.63022 | 40.6 | 2100 | 64661 |
+
+## 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 Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s 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: 3405783
+* port time: Wed Apr 13 17:07:05 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+eng-fra	flores101-dev	0.72082	51.5	997	26706
+eng-fra	flores101-devtest	0.72494	52.2	1012	28343
+eng-fra	multi30k_test_2016_flickr	0.72361	52.4	1000	13505
+eng-fra	multi30k_test_2017_flickr	0.72826	52.8	1000	12118
+eng-fra	multi30k_test_2017_mscoco	0.73547	54.7	461	5484
+eng-fra	multi30k_test_2018_flickr	0.66723	43.7	1071	15867
+eng-fra	newsdiscussdev2015	0.60471	33.4	1500	27940
+eng-fra	newsdiscusstest2015	0.64915	40.3	1500	27975
+eng-fra	newssyscomb2009	0.58903	30.7	502	12331
+eng-fra	news-test2008	0.55516	27.6	2051	52685
+eng-fra	newstest2009	0.57907	30.0	2525	69263
+eng-fra	newstest2010	0.60156	33.5	2489	66022
+eng-fra	newstest2011	0.61632	35.0	3003	80626
+eng-fra	newstest2012	0.59736	32.8	3003	78011
+eng-fra	newstest2013	0.59700	34.6	3000	70037
+eng-fra	newstest2014	0.66686	41.9	3003	77306
+eng-fra	tatoeba-test-v2020-07-28	0.68090	51.7	10000	80769
+eng-fra	tatoeba-test-v2021-03-30	0.68816	52.5	10892	89269
+eng-fra	tatoeba-test-v2021-08-07	0.69621	53.2	12681	106378
+eng-fra	tico19-test	0.63022	40.6	2100	64661

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