
### gmq-gmq

* source group: North Germanic languages 
* target group: North Germanic languages 
*  OPUS readme: [gmq-gmq](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-gmq/README.md)

*  model: transformer
* source language(s): dan fao isl nno nob swe
* target language(s): dan fao isl nno nob swe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.dan-fao.dan.fao 	| 8.1 	| 0.173 |
| Tatoeba-test.dan-isl.dan.isl 	| 52.5 	| 0.827 |
| Tatoeba-test.dan-nor.dan.nor 	| 62.8 	| 0.772 |
| Tatoeba-test.dan-swe.dan.swe 	| 67.6 	| 0.802 |
| Tatoeba-test.fao-dan.fao.dan 	| 11.3 	| 0.306 |
| Tatoeba-test.fao-isl.fao.isl 	| 26.3 	| 0.359 |
| Tatoeba-test.fao-nor.fao.nor 	| 36.8 	| 0.531 |
| Tatoeba-test.fao-swe.fao.swe 	| 0.0 	| 0.632 |
| Tatoeba-test.isl-dan.isl.dan 	| 67.0 	| 0.739 |
| Tatoeba-test.isl-fao.isl.fao 	| 14.5 	| 0.243 |
| Tatoeba-test.isl-nor.isl.nor 	| 51.8 	| 0.674 |
| Tatoeba-test.isl-swe.isl.swe 	| 100.0 	| 1.000 |
| Tatoeba-test.multi.multi 	| 64.7 	| 0.782 |
| Tatoeba-test.nor-dan.nor.dan 	| 65.6 	| 0.797 |
| Tatoeba-test.nor-fao.nor.fao 	| 9.4 	| 0.362 |
| Tatoeba-test.nor-isl.nor.isl 	| 38.8 	| 0.587 |
| Tatoeba-test.nor-nor.nor.nor 	| 51.9 	| 0.721 |
| Tatoeba-test.nor-swe.nor.swe 	| 66.5 	| 0.789 |
| Tatoeba-test.swe-dan.swe.dan 	| 67.6 	| 0.802 |
| Tatoeba-test.swe-fao.swe.fao 	| 0.0 	| 0.268 |
| Tatoeba-test.swe-isl.swe.isl 	| 65.8 	| 0.914 |
| Tatoeba-test.swe-nor.swe.nor 	| 60.6 	| 0.755 |


### System Info: 
- hf_name: gmq-gmq

- source_languages: gmq

- target_languages: gmq

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-gmq/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']

- src_constituents: {'dan', 'nob', 'nob_Hebr', 'swe', 'isl', 'nno', 'non_Latn', 'fao'}

- tgt_constituents: {'dan', 'nob', 'nob_Hebr', 'swe', 'isl', 'nno', 'non_Latn', 'fao'}

- src_multilingual: True

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.test.txt

- src_alpha3: gmq

- tgt_alpha3: gmq

- short_pair: gmq-gmq

- chrF2_score: 0.782

- bleu: 64.7

- brevity_penalty: 0.9940000000000001

- ref_len: 49385.0

- src_name: North Germanic languages

- tgt_name: North Germanic languages

- train_date: 2020-07-27

- src_alpha2: gmq

- tgt_alpha2: gmq

- prefer_old: False

- long_pair: gmq-gmq

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41
{"language": ["da", "nb", "sv", "is", "nn", "fo", "gmq"], "license": "apache-2.0", "tags": ["translation"]}
transformers pytorch tf marian text2text-generation translation da nb sv is nn fo gmq license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #da #nb #sv #is #nn #fo #gmq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

### gmq-gmq


* source group: North Germanic languages
* target group: North Germanic languages
* OPUS readme: gmq-gmq
* model: transformer
* source language(s): dan fao isl nno nob swe
* target language(s): dan fao isl nno nob swe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL


Benchmarks
----------


testset: URL, BLEU: 8.1, chr-F: 0.173
testset: URL, BLEU: 52.5, chr-F: 0.827
testset: URL, BLEU: 62.8, chr-F: 0.772
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 11.3, chr-F: 0.306
testset: URL, BLEU: 26.3, chr-F: 0.359
testset: URL, BLEU: 36.8, chr-F: 0.531
testset: URL, BLEU: 0.0, chr-F: 0.632
testset: URL, BLEU: 67.0, chr-F: 0.739
testset: URL, BLEU: 14.5, chr-F: 0.243
testset: URL, BLEU: 51.8, chr-F: 0.674
testset: URL, BLEU: 100.0, chr-F: 1.000
testset: URL, BLEU: 64.7, chr-F: 0.782
testset: URL, BLEU: 65.6, chr-F: 0.797
testset: URL, BLEU: 9.4, chr-F: 0.362
testset: URL, BLEU: 38.8, chr-F: 0.587
testset: URL, BLEU: 51.9, chr-F: 0.721
testset: URL, BLEU: 66.5, chr-F: 0.789
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 0.0, chr-F: 0.268
testset: URL, BLEU: 65.8, chr-F: 0.914
testset: URL, BLEU: 60.6, chr-F: 0.755


### System Info:


* hf\_name: gmq-gmq
* source\_languages: gmq
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: gmq
* tgt\_alpha3: gmq
* short\_pair: gmq-gmq
* chrF2\_score: 0.782
* bleu: 64.7
* brevity\_penalty: 0.9940000000000001
* ref\_len: 49385.0
* src\_name: North Germanic languages
* tgt\_name: North Germanic languages
* train\_date: 2020-07-27
* src\_alpha2: gmq
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: gmq-gmq
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41



### gmq-gmq


* source group: North Germanic languages
* target group: North Germanic languages
* OPUS readme: gmq-gmq
* model: transformer
* source language(s): dan fao isl nno nob swe
* target language(s): dan fao isl nno nob swe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL


Benchmarks
----------


testset: URL, BLEU: 8.1, chr-F: 0.173
testset: URL, BLEU: 52.5, chr-F: 0.827
testset: URL, BLEU: 62.8, chr-F: 0.772
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 11.3, chr-F: 0.306
testset: URL, BLEU: 26.3, chr-F: 0.359
testset: URL, BLEU: 36.8, chr-F: 0.531
testset: URL, BLEU: 0.0, chr-F: 0.632
testset: URL, BLEU: 67.0, chr-F: 0.739
testset: URL, BLEU: 14.5, chr-F: 0.243
testset: URL, BLEU: 51.8, chr-F: 0.674
testset: URL, BLEU: 100.0, chr-F: 1.000
testset: URL, BLEU: 64.7, chr-F: 0.782
testset: URL, BLEU: 65.6, chr-F: 0.797
testset: URL, BLEU: 9.4, chr-F: 0.362
testset: URL, BLEU: 38.8, chr-F: 0.587
testset: URL, BLEU: 51.9, chr-F: 0.721
testset: URL, BLEU: 66.5, chr-F: 0.789
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 0.0, chr-F: 0.268
testset: URL, BLEU: 65.8, chr-F: 0.914
testset: URL, BLEU: 60.6, chr-F: 0.755 ### System Info:


* hf\_name: gmq-gmq
* source\_languages: gmq
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: gmq
* tgt\_alpha3: gmq
* short\_pair: gmq-gmq
* chrF2\_score: 0.782
* bleu: 64.7
* brevity\_penalty: 0.9940000000000001
* ref\_len: 49385.0
* src\_name: North Germanic languages
* tgt\_name: North Germanic languages
* train\_date: 2020-07-27
* src\_alpha2: gmq
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: gmq-gmq
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #da #nb #sv #is #nn #fo #gmq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### gmq-gmq


* source group: North Germanic languages
* target group: North Germanic languages
* OPUS readme: gmq-gmq
* model: transformer
* source language(s): dan fao isl nno nob swe
* target language(s): dan fao isl nno nob swe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL


Benchmarks
----------


testset: URL, BLEU: 8.1, chr-F: 0.173
testset: URL, BLEU: 52.5, chr-F: 0.827
testset: URL, BLEU: 62.8, chr-F: 0.772
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 11.3, chr-F: 0.306
testset: URL, BLEU: 26.3, chr-F: 0.359
testset: URL, BLEU: 36.8, chr-F: 0.531
testset: URL, BLEU: 0.0, chr-F: 0.632
testset: URL, BLEU: 67.0, chr-F: 0.739
testset: URL, BLEU: 14.5, chr-F: 0.243
testset: URL, BLEU: 51.8, chr-F: 0.674
testset: URL, BLEU: 100.0, chr-F: 1.000
testset: URL, BLEU: 64.7, chr-F: 0.782
testset: URL, BLEU: 65.6, chr-F: 0.797
testset: URL, BLEU: 9.4, chr-F: 0.362
testset: URL, BLEU: 38.8, chr-F: 0.587
testset: URL, BLEU: 51.9, chr-F: 0.721
testset: URL, BLEU: 66.5, chr-F: 0.789
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 0.0, chr-F: 0.268
testset: URL, BLEU: 65.8, chr-F: 0.914
testset: URL, BLEU: 60.6, chr-F: 0.755 ### System Info:


* hf\_name: gmq-gmq
* source\_languages: gmq
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: gmq
* tgt\_alpha3: gmq
* short\_pair: gmq-gmq
* chrF2\_score: 0.782
* bleu: 64.7
* brevity\_penalty: 0.9940000000000001
* ref\_len: 49385.0
* src\_name: North Germanic languages
* tgt\_name: North Germanic languages
* train\_date: 2020-07-27
* src\_alpha2: gmq
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: gmq-gmq
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
passage: TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #da #nb #sv #is #nn #fo #gmq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 passage: ### gmq-gmq


* source group: North Germanic languages
* target group: North Germanic languages
* OPUS readme: gmq-gmq
* model: transformer
* source language(s): dan fao isl nno nob swe
* target language(s): dan fao isl nno nob swe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL


Benchmarks
----------


testset: URL, BLEU: 8.1, chr-F: 0.173
testset: URL, BLEU: 52.5, chr-F: 0.827
testset: URL, BLEU: 62.8, chr-F: 0.772
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 11.3, chr-F: 0.306
testset: URL, BLEU: 26.3, chr-F: 0.359
testset: URL, BLEU: 36.8, chr-F: 0.531
testset: URL, BLEU: 0.0, chr-F: 0.632
testset: URL, BLEU: 67.0, chr-F: 0.739
testset: URL, BLEU: 14.5, chr-F: 0.243
testset: URL, BLEU: 51.8, chr-F: 0.674
testset: URL, BLEU: 100.0, chr-F: 1.000
testset: URL, BLEU: 64.7, chr-F: 0.782
testset: URL, BLEU: 65.6, chr-F: 0.797
testset: URL, BLEU: 9.4, chr-F: 0.362
testset: URL, BLEU: 38.8, chr-F: 0.587
testset: URL, BLEU: 51.9, chr-F: 0.721
testset: URL, BLEU: 66.5, chr-F: 0.789
testset: URL, BLEU: 67.6, chr-F: 0.802
testset: URL, BLEU: 0.0, chr-F: 0.268
testset: URL, BLEU: 65.8, chr-F: 0.914
testset: URL, BLEU: 60.6, chr-F: 0.755