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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-fr-en
+  results:
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: fra eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 46.0
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: multi30k_test_2016_flickr
+      type: multi30k-2016_flickr
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 49.7
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: multi30k_test_2017_flickr
+      type: multi30k-2017_flickr
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 52.0
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: multi30k_test_2017_mscoco
+      type: multi30k-2017_mscoco
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 50.6
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: multi30k_test_2018_flickr
+      type: multi30k-2018_flickr
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 44.9
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: news-test2008
+      type: news-test2008
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 26.5
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newsdiscussdev2015
+      type: newsdiscussdev2015
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.4
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newsdiscusstest2015
+      type: newsdiscusstest2015
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.2
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 59.8
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: tico19-test
+      type: tico19-test
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 41.3
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2009
+      type: wmt-2009-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.4
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2010
+      type: wmt-2010-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.4
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2011
+      type: wmt-2011-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.8
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.6
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.8
+  - task:
+      name: Translation fra-eng
+      type: translation
+      args: fra-eng
+    dataset:
+      name: newstest2014
+      type: wmt-2014-news
+      args: fra-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 39.4
+---
+# opus-mt-tc-big-fr-en
+
+Neural machine translation model for translating from French (fr) to English (en).
+
+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): fra
+* target language(s): eng
+* 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/fra-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
+* more information released models: [OPUS-MT fra-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-eng/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "J'ai adoré l'Angleterre.",
+    "C'était la seule chose à faire."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-fr-en"
+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:
+#     I loved England.
+#     It was the only thing to do.
+```
+
+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-fr-en")
+print(pipe("J'ai adoré l'Angleterre."))
+
+# expected output: I loved England.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/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/fra-eng/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 |
+|----------|---------|-------|-------|-------|--------|
+| fra-eng | tatoeba-test-v2021-08-07 | 0.73772 | 59.8 | 12681 | 101754 |
+| fra-eng | flores101-devtest | 0.69350 | 46.0 | 1012 | 24721 |
+| fra-eng | multi30k_test_2016_flickr | 0.68005 | 49.7 | 1000 | 12955 |
+| fra-eng | multi30k_test_2017_flickr | 0.70596 | 52.0 | 1000 | 11374 |
+| fra-eng | multi30k_test_2017_mscoco | 0.69356 | 50.6 | 461 | 5231 |
+| fra-eng | multi30k_test_2018_flickr | 0.65751 | 44.9 | 1071 | 14689 |
+| fra-eng | newsdiscussdev2015 | 0.59008 | 34.4 | 1500 | 27759 |
+| fra-eng | newsdiscusstest2015 | 0.62603 | 40.2 | 1500 | 26982 |
+| fra-eng | newssyscomb2009 | 0.57488 | 31.1 | 502 | 11818 |
+| fra-eng | news-test2008 | 0.54316 | 26.5 | 2051 | 49380 |
+| fra-eng | newstest2009 | 0.56959 | 30.4 | 2525 | 65399 |
+| fra-eng | newstest2010 | 0.59561 | 33.4 | 2489 | 61711 |
+| fra-eng | newstest2011 | 0.60271 | 33.8 | 3003 | 74681 |
+| fra-eng | newstest2012 | 0.59507 | 33.6 | 3003 | 72812 |
+| fra-eng | newstest2013 | 0.59691 | 34.8 | 3000 | 64505 |
+| fra-eng | newstest2014 | 0.64533 | 39.4 | 3003 | 70708 |
+| fra-eng | tico19-test | 0.63326 | 41.3 | 2100 | 56323 |
+
+## 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 19:02:28 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+fra-eng	flores101-dev	0.69835	47.0	997	23555
+fra-eng	flores101-devtest	0.69350	46.0	1012	24721
+fra-eng	multi30k_test_2016_flickr	0.68005	49.7	1000	12955
+fra-eng	multi30k_test_2017_flickr	0.70596	52.0	1000	11374
+fra-eng	multi30k_test_2017_mscoco	0.69356	50.6	461	5231
+fra-eng	multi30k_test_2018_flickr	0.65751	44.9	1071	14689
+fra-eng	newsdiscussdev2015	0.59008	34.4	1500	27759
+fra-eng	newsdiscusstest2015	0.62603	40.2	1500	26982
+fra-eng	newssyscomb2009	0.57488	31.1	502	11818
+fra-eng	news-test2008	0.54316	26.5	2051	49380
+fra-eng	newstest2009	0.56959	30.4	2525	65399
+fra-eng	newstest2010	0.59561	33.4	2489	61711
+fra-eng	newstest2011	0.60271	33.8	3003	74681
+fra-eng	newstest2012	0.59507	33.6	3003	72812
+fra-eng	newstest2013	0.59691	34.8	3000	64505
+fra-eng	newstest2014	0.64533	39.4	3003	70708
+fra-eng	tatoeba-test-v2020-07-28	0.73088	59.2	10000	77174
+fra-eng	tatoeba-test-v2021-03-30	0.73444	59.5	10892	85143
+fra-eng	tatoeba-test-v2021-08-07	0.73772	59.8	12681	101754
+fra-eng	tico19-test	0.63326	41.3	2100	56323

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