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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-gmq-en
+  results:
+  - task:
+      name: Translation dan-eng
+      type: translation
+      args: dan-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: dan eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 49.3
+  - task:
+      name: Translation isl-eng
+      type: translation
+      args: isl-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: isl eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.2
+  - task:
+      name: Translation nob-eng
+      type: translation
+      args: nob-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: nob eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 44.2
+  - task:
+      name: Translation swe-eng
+      type: translation
+      args: swe-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: swe eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 49.8
+  - task:
+      name: Translation isl-eng
+      type: translation
+      args: isl-eng
+    dataset:
+      name: newsdev2021.is-en
+      type: newsdev2021.is-en
+      args: isl-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.4
+  - task:
+      name: Translation dan-eng
+      type: translation
+      args: dan-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: dan-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 65.9
+  - task:
+      name: Translation fao-eng
+      type: translation
+      args: fao-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: fao-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.1
+  - task:
+      name: Translation isl-eng
+      type: translation
+      args: isl-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: isl-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 53.3
+  - task:
+      name: Translation nno-eng
+      type: translation
+      args: nno-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: nno-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 56.1
+  - task:
+      name: Translation nob-eng
+      type: translation
+      args: nob-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: nob-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 60.2
+  - task:
+      name: Translation swe-eng
+      type: translation
+      args: swe-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: swe-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 66.4
+  - task:
+      name: Translation isl-eng
+      type: translation
+      args: isl-eng
+    dataset:
+      name: newstest2021.is-en
+      type: wmt-2021-news
+      args: isl-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.4
+---
+# opus-mt-tc-big-gmq-en
+
+Neural machine translation model for translating from North Germanic languages (gmq) 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): dan fao isl nno nob nor swe
+* 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/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
+* more information released models: [OPUS-MT gmq-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "Han var synligt nervøs.",
+    "Inte ens Tom själv var övertygad."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-gmq-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:
+#     He was visibly nervous.
+#     Even Tom was not convinced.
+```
+
+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-gmq-en")
+print(pipe("Han var synligt nervøs."))
+
+# expected output: He was visibly nervous.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-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/gmq-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 |
+|----------|---------|-------|-------|-------|--------|
+| dan-eng | tatoeba-test-v2021-08-07 | 0.78292 | 65.9 | 10795 | 79684 |
+| fao-eng | tatoeba-test-v2021-08-07 | 0.47467 | 30.1 | 294 | 1984 |
+| isl-eng | tatoeba-test-v2021-08-07 | 0.68346 | 53.3 | 2503 | 19788 |
+| nno-eng | tatoeba-test-v2021-08-07 | 0.69788 | 56.1 | 460 | 3524 |
+| nob-eng | tatoeba-test-v2021-08-07 | 0.73524 | 60.2 | 4539 | 36823 |
+| swe-eng | tatoeba-test-v2021-08-07 | 0.77665 | 66.4 | 10362 | 68513 |
+| dan-eng | flores101-devtest | 0.72322 | 49.3 | 1012 | 24721 |
+| isl-eng | flores101-devtest | 0.59616 | 34.2 | 1012 | 24721 |
+| nob-eng | flores101-devtest | 0.68224 | 44.2 | 1012 | 24721 |
+| swe-eng | flores101-devtest | 0.72042 | 49.8 | 1012 | 24721 |
+| isl-eng | newsdev2021.is-en | 0.56709 | 30.4 | 2004 | 46383 |
+| isl-eng | newstest2021.is-en | 0.57756 | 34.4 | 1000 | 22529 |
+
+## 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 19:13:11 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

@@ -0,0 +1,28 @@
+dan-eng	flores101-dev	0.72535	49.7	997	23555
+isl-eng	flores101-dev	0.60060	34.7	997	23555
+nob-eng	flores101-dev	0.68464	43.7	997	23555
+swe-eng	flores101-dev	0.72445	50.6	997	23555
+dan-eng	flores101-devtest	0.72322	49.3	1012	24721
+isl-eng	flores101-devtest	0.59616	34.2	1012	24721
+nob-eng	flores101-devtest	0.68224	44.2	1012	24721
+swe-eng	flores101-devtest	0.72042	49.8	1012	24721
+isl-eng	newsdev2021.is-en	0.56709	30.4	2004	46383
+isl-eng	newstest2021.is-en	0.57756	34.4	1000	22529
+dan-eng	tatoeba-test-v2020-07-28	0.78295	65.9	10000	73617
+fao-eng	tatoeba-test-v2020-07-28	0.47467	30.1	294	1984
+isl-eng	tatoeba-test-v2020-07-28	0.68350	53.4	2500	19763
+nno-eng	tatoeba-test-v2020-07-28	0.69788	56.1	460	3524
+nob-eng	tatoeba-test-v2020-07-28	0.73524	60.2	4539	36823
+swe-eng	tatoeba-test-v2020-07-28	0.77685	66.6	10000	66002
+dan-eng	tatoeba-test-v2021-03-30	0.78345	65.9	10437	76848
+fao-eng	tatoeba-test-v2021-03-30	0.47446	30.1	297	2001
+isl-eng	tatoeba-test-v2021-03-30	0.68341	53.3	4994	39502
+nno-eng	tatoeba-test-v2021-03-30	0.69788	56.1	460	3524
+nob-eng	tatoeba-test-v2021-03-30	0.73524	60.2	4539	36823
+swe-eng	tatoeba-test-v2021-03-30	0.77689	66.6	10151	67008
+dan-eng	tatoeba-test-v2021-08-07	0.78292	65.9	10795	79684
+fao-eng	tatoeba-test-v2021-08-07	0.47467	30.1	294	1984
+isl-eng	tatoeba-test-v2021-08-07	0.68346	53.3	2503	19788
+nno-eng	tatoeba-test-v2021-08-07	0.69788	56.1	460	3524
+nob-eng	tatoeba-test-v2021-08-07	0.73524	60.2	4539	36823
+swe-eng	tatoeba-test-v2021-08-07	0.77665	66.4	10362	68513

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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 54765
+}

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