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README.md

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+---
+language:
+- da
+- en
+- fo
+- gmq
+- is
+- nb
+- nn
+- no
+- sv
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-en-gmq
+  results:
+  - task:
+      name: Translation eng-dan
+      type: translation
+      args: eng-dan
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng dan devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 47.7
+  - task:
+      name: Translation eng-isl
+      type: translation
+      args: eng-isl
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng isl devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 24.1
+  - task:
+      name: Translation eng-nob
+      type: translation
+      args: eng-nob
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng nob devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.5
+  - task:
+      name: Translation eng-swe
+      type: translation
+      args: eng-swe
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng swe devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 46.9
+  - task:
+      name: Translation eng-isl
+      type: translation
+      args: eng-isl
+    dataset:
+      name: newsdev2021.en-is
+      type: newsdev2021.en-is
+      args: eng-isl
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 22.6
+  - task:
+      name: Translation eng-dan
+      type: translation
+      args: eng-dan
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-dan
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 61.6
+  - task:
+      name: Translation eng-isl
+      type: translation
+      args: eng-isl
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-isl
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 39.9
+  - task:
+      name: Translation eng-nno
+      type: translation
+      args: eng-nno
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-nno
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.1
+  - task:
+      name: Translation eng-nob
+      type: translation
+      args: eng-nob
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-nob
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 57.3
+  - task:
+      name: Translation eng-swe
+      type: translation
+      args: eng-swe
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-swe
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 60.9
+  - task:
+      name: Translation eng-isl
+      type: translation
+      args: eng-isl
+    dataset:
+      name: newstest2021.en-is
+      type: wmt-2021-news
+      args: eng-isl
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 21.5
+---
+# opus-mt-tc-big-en-gmq
+
+Neural machine translation model for translating from English (en) 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).
+
+* 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: 2022-03-17
+* source language(s): eng
+* target language(s): dan fao isl nno nob nor swe
+* valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
+* model: transformer-big
+* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
+* more information released models: [OPUS-MT eng-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmq/README.md)
+* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
+
+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<<`
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    ">>nno<< The United States borders Canada.",
+    ">>nob<< This is the biggest hotel in this city."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-en-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:
+#     USA grensar til Canada.
+#     Dette er det største hotellet i denne byen.
+```
+
+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-gmq")
+print(pipe(">>nno<< The United States borders Canada."))
+
+# expected output: USA grensar til Canada.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt)
+* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.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-dan | tatoeba-test-v2021-08-07 | 0.75165 | 61.6 | 10795 | 79385 |
+| eng-fao | tatoeba-test-v2021-08-07 | 0.40395 | 18.3 | 294 | 1933 |
+| eng-isl | tatoeba-test-v2021-08-07 | 0.59731 | 39.9 | 2503 | 19023 |
+| eng-nno | tatoeba-test-v2021-08-07 | 0.61271 | 40.1 | 460 | 3428 |
+| eng-nob | tatoeba-test-v2021-08-07 | 0.72380 | 57.3 | 4539 | 36119 |
+| eng-swe | tatoeba-test-v2021-08-07 | 0.74197 | 60.9 | 10362 | 68067 |
+| eng-dan | flores101-devtest | 0.70810 | 47.7 | 1012 | 24638 |
+| eng-isl | flores101-devtest | 0.52076 | 24.1 | 1012 | 22834 |
+| eng-nob | flores101-devtest | 0.62760 | 34.5 | 1012 | 23873 |
+| eng-swe | flores101-devtest | 0.70129 | 46.9 | 1012 | 23121 |
+| eng-isl | newsdev2021.en-is | 0.50376 | 22.6 | 2004 | 43721 |
+| eng-isl | newstest2021.en-is | 0.50516 | 21.5 | 1000 | 25233 |
+
+## 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 17:14:46 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

@@ -0,0 +1,28 @@
+eng-dan	flores101-dev	0.69727	46.5	997	23685
+eng-isl	flores101-dev	0.52713	24.8	997	21857
+eng-nob	flores101-dev	0.62160	34.1	997	23157
+eng-swe	flores101-dev	0.70140	46.5	997	22417
+eng-dan	flores101-devtest	0.70810	47.7	1012	24638
+eng-isl	flores101-devtest	0.52076	24.1	1012	22834
+eng-nob	flores101-devtest	0.62760	34.5	1012	23873
+eng-swe	flores101-devtest	0.70129	46.9	1012	23121
+eng-isl	newsdev2021.en-is	0.50376	22.6	2004	43721
+eng-isl	newstest2021.en-is	0.50516	21.5	1000	25233
+eng-dan	tatoeba-test-v2020-07-28	0.75072	61.5	10000	73213
+eng-fao	tatoeba-test-v2020-07-28	0.40395	18.3	294	1933
+eng-isl	tatoeba-test-v2020-07-28	0.59738	39.9	2500	18999
+eng-nno	tatoeba-test-v2020-07-28	0.61271	40.1	460	3428
+eng-nob	tatoeba-test-v2020-07-28	0.72382	57.3	4539	36119
+eng-swe	tatoeba-test-v2020-07-28	0.74233	61.1	10000	65581
+eng-dan	tatoeba-test-v2021-03-30	0.75155	61.6	10437	76467
+eng-fao	tatoeba-test-v2021-03-30	0.40364	18.4	297	1948
+eng-isl	tatoeba-test-v2021-03-30	0.59722	39.9	4994	37970
+eng-nno	tatoeba-test-v2021-03-30	0.61271	40.1	460	3428
+eng-nob	tatoeba-test-v2021-03-30	0.72382	57.3	4539	36119
+eng-swe	tatoeba-test-v2021-03-30	0.74248	61.1	10151	66592
+eng-dan	tatoeba-test-v2021-08-07	0.75165	61.6	10795	79385
+eng-fao	tatoeba-test-v2021-08-07	0.40395	18.3	294	1933
+eng-isl	tatoeba-test-v2021-08-07	0.59731	39.9	2503	19023
+eng-nno	tatoeba-test-v2021-08-07	0.61271	40.1	460	3428
+eng-nob	tatoeba-test-v2021-08-07	0.72380	57.3	4539	36119
+eng-swe	tatoeba-test-v2021-08-07	0.74197	60.9	10362	68067

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+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 54667
+}

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