
### eng-gmq

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

*  model: transformer
* source language(s): eng
* target language(s): dan fao isl nno nob nob_Hebr non_Latn 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: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opus2m-2020-08-01.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng-dan.eng.dan 	| 57.7 	| 0.724 |
| Tatoeba-test.eng-fao.eng.fao 	| 9.2 	| 0.322 |
| Tatoeba-test.eng-isl.eng.isl 	| 23.8 	| 0.506 |
| Tatoeba-test.eng.multi 	| 52.8 	| 0.688 |
| Tatoeba-test.eng-non.eng.non 	| 0.7 	| 0.196 |
| Tatoeba-test.eng-nor.eng.nor 	| 50.3 	| 0.678 |
| Tatoeba-test.eng-swe.eng.swe 	| 57.8 	| 0.717 |


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

- source_languages: eng

- target_languages: gmq

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

- original_repo: Tatoeba-Challenge

- tags: ['translation']

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

- src_constituents: {'eng'}

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

- src_multilingual: False

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opus2m-2020-08-01.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opus2m-2020-08-01.test.txt

- src_alpha3: eng

- tgt_alpha3: gmq

- short_pair: en-gmq

- chrF2_score: 0.688

- bleu: 52.8

- brevity_penalty: 0.973

- ref_len: 71881.0

- src_name: English

- tgt_name: North Germanic languages

- train_date: 2020-08-01

- src_alpha2: en

- tgt_alpha2: gmq

- prefer_old: False

- long_pair: eng-gmq

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

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

### eng-gmq


* source group: English
* target group: North Germanic languages
* OPUS readme: eng-gmq
* model: transformer
* source language(s): eng
* target language(s): dan fao isl nno nob nob\_Hebr non\_Latn 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: 57.7, chr-F: 0.724
testset: URL, BLEU: 9.2, chr-F: 0.322
testset: URL, BLEU: 23.8, chr-F: 0.506
testset: URL, BLEU: 52.8, chr-F: 0.688
testset: URL, BLEU: 0.7, chr-F: 0.196
testset: URL, BLEU: 50.3, chr-F: 0.678
testset: URL, BLEU: 57.8, chr-F: 0.717


### System Info:


* hf\_name: eng-gmq
* source\_languages: eng
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['en', 'da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'eng'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: False
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: eng
* tgt\_alpha3: gmq
* short\_pair: en-gmq
* chrF2\_score: 0.688
* bleu: 52.8
* brevity\_penalty: 0.973
* ref\_len: 71881.0
* src\_name: English
* tgt\_name: North Germanic languages
* train\_date: 2020-08-01
* src\_alpha2: en
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: eng-gmq
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41



### eng-gmq


* source group: English
* target group: North Germanic languages
* OPUS readme: eng-gmq
* model: transformer
* source language(s): eng
* target language(s): dan fao isl nno nob nob\_Hebr non\_Latn 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: 57.7, chr-F: 0.724
testset: URL, BLEU: 9.2, chr-F: 0.322
testset: URL, BLEU: 23.8, chr-F: 0.506
testset: URL, BLEU: 52.8, chr-F: 0.688
testset: URL, BLEU: 0.7, chr-F: 0.196
testset: URL, BLEU: 50.3, chr-F: 0.678
testset: URL, BLEU: 57.8, chr-F: 0.717 ### System Info:


* hf\_name: eng-gmq
* source\_languages: eng
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['en', 'da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'eng'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: False
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: eng
* tgt\_alpha3: gmq
* short\_pair: en-gmq
* chrF2\_score: 0.688
* bleu: 52.8
* brevity\_penalty: 0.973
* ref\_len: 71881.0
* src\_name: English
* tgt\_name: North Germanic languages
* train\_date: 2020-08-01
* src\_alpha2: en
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: eng-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 #en #da #nb #sv #is #nn #fo #gmq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### eng-gmq


* source group: English
* target group: North Germanic languages
* OPUS readme: eng-gmq
* model: transformer
* source language(s): eng
* target language(s): dan fao isl nno nob nob\_Hebr non\_Latn 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: 57.7, chr-F: 0.724
testset: URL, BLEU: 9.2, chr-F: 0.322
testset: URL, BLEU: 23.8, chr-F: 0.506
testset: URL, BLEU: 52.8, chr-F: 0.688
testset: URL, BLEU: 0.7, chr-F: 0.196
testset: URL, BLEU: 50.3, chr-F: 0.678
testset: URL, BLEU: 57.8, chr-F: 0.717 ### System Info:


* hf\_name: eng-gmq
* source\_languages: eng
* target\_languages: gmq
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['en', 'da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']
* src\_constituents: {'eng'}
* tgt\_constituents: {'dan', 'nob', 'nob\_Hebr', 'swe', 'isl', 'nno', 'non\_Latn', 'fao'}
* src\_multilingual: False
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: eng
* tgt\_alpha3: gmq
* short\_pair: en-gmq
* chrF2\_score: 0.688
* bleu: 52.8
* brevity\_penalty: 0.973
* ref\_len: 71881.0
* src\_name: English
* tgt\_name: North Germanic languages
* train\_date: 2020-08-01
* src\_alpha2: en
* tgt\_alpha2: gmq
* prefer\_old: False
* long\_pair: eng-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 #en #da #nb #sv #is #nn #fo #gmq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### eng-gmq


* source group: English
* target group: North Germanic languages
* OPUS readme: eng-gmq
* model: transformer
* source language(s): eng
* target language(s): dan fao isl nno nob nob\_Hebr non\_Latn 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: 57.7, chr-F: 0.724
testset: URL, BLEU: 9.2, chr-F: 0.322
testset: URL, BLEU: 23.8, chr-F: 0.506
testset: URL, BLEU: 52.8, chr-F: 0.688
testset: URL, BLEU: 0.7, chr-F: 0.196
testset: URL, BLEU: 50.3, chr-F: 0.678
testset: URL, BLEU: 57.8, chr-F: 0.717