
### sem-sem

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

*  model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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/sem-sem/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara-ara.ara.ara 	| 4.2 	| 0.200 |
| Tatoeba-test.ara-heb.ara.heb 	| 34.0 	| 0.542 |
| Tatoeba-test.ara-mlt.ara.mlt 	| 16.6 	| 0.513 |
| Tatoeba-test.heb-ara.heb.ara 	| 18.8 	| 0.477 |
| Tatoeba-test.mlt-ara.mlt.ara 	| 20.7 	| 0.388 |
| Tatoeba-test.multi.multi 	| 27.1 	| 0.507 |


### System Info: 
- hf_name: sem-sem

- source_languages: sem

- target_languages: sem

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

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem']

- src_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}

- tgt_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}

- src_multilingual: True

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

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

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

- src_alpha3: sem

- tgt_alpha3: sem

- short_pair: sem-sem

- chrF2_score: 0.507

- bleu: 27.1

- brevity_penalty: 0.972

- ref_len: 13472.0

- src_name: Semitic languages

- tgt_name: Semitic languages

- train_date: 2020-07-27

- src_alpha2: sem

- tgt_alpha2: sem

- prefer_old: False

- long_pair: sem-sem

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41
{"language": ["mt", "ar", "he", "ti", "am", "sem"], "license": "apache-2.0", "tags": ["translation"]}
transformers pytorch tf marian text2text-generation translation mt ar he ti am sem license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #mt #ar #he #ti #am #sem #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

### sem-sem


* source group: Semitic languages
* target group: Semitic languages
* OPUS readme: sem-sem
* model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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: 4.2, chr-F: 0.200
testset: URL, BLEU: 34.0, chr-F: 0.542
testset: URL, BLEU: 16.6, chr-F: 0.513
testset: URL, BLEU: 18.8, chr-F: 0.477
testset: URL, BLEU: 20.7, chr-F: 0.388
testset: URL, BLEU: 27.1, chr-F: 0.507


### System Info:


* hf\_name: sem-sem
* source\_languages: sem
* target\_languages: sem
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem']
* src\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* tgt\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: sem
* tgt\_alpha3: sem
* short\_pair: sem-sem
* chrF2\_score: 0.507
* bleu: 27.1
* brevity\_penalty: 0.972
* ref\_len: 13472.0
* src\_name: Semitic languages
* tgt\_name: Semitic languages
* train\_date: 2020-07-27
* src\_alpha2: sem
* tgt\_alpha2: sem
* prefer\_old: False
* long\_pair: sem-sem
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41



### sem-sem


* source group: Semitic languages
* target group: Semitic languages
* OPUS readme: sem-sem
* model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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: 4.2, chr-F: 0.200
testset: URL, BLEU: 34.0, chr-F: 0.542
testset: URL, BLEU: 16.6, chr-F: 0.513
testset: URL, BLEU: 18.8, chr-F: 0.477
testset: URL, BLEU: 20.7, chr-F: 0.388
testset: URL, BLEU: 27.1, chr-F: 0.507 ### System Info:


* hf\_name: sem-sem
* source\_languages: sem
* target\_languages: sem
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem']
* src\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* tgt\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: sem
* tgt\_alpha3: sem
* short\_pair: sem-sem
* chrF2\_score: 0.507
* bleu: 27.1
* brevity\_penalty: 0.972
* ref\_len: 13472.0
* src\_name: Semitic languages
* tgt\_name: Semitic languages
* train\_date: 2020-07-27
* src\_alpha2: sem
* tgt\_alpha2: sem
* prefer\_old: False
* long\_pair: sem-sem
* 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 #mt #ar #he #ti #am #sem #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### sem-sem


* source group: Semitic languages
* target group: Semitic languages
* OPUS readme: sem-sem
* model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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: 4.2, chr-F: 0.200
testset: URL, BLEU: 34.0, chr-F: 0.542
testset: URL, BLEU: 16.6, chr-F: 0.513
testset: URL, BLEU: 18.8, chr-F: 0.477
testset: URL, BLEU: 20.7, chr-F: 0.388
testset: URL, BLEU: 27.1, chr-F: 0.507 ### System Info:


* hf\_name: sem-sem
* source\_languages: sem
* target\_languages: sem
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem']
* src\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* tgt\_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: sem
* tgt\_alpha3: sem
* short\_pair: sem-sem
* chrF2\_score: 0.507
* bleu: 27.1
* brevity\_penalty: 0.972
* ref\_len: 13472.0
* src\_name: Semitic languages
* tgt\_name: Semitic languages
* train\_date: 2020-07-27
* src\_alpha2: sem
* tgt\_alpha2: sem
* prefer\_old: False
* long\_pair: sem-sem
* 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 #mt #ar #he #ti #am #sem #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### sem-sem


* source group: Semitic languages
* target group: Semitic languages
* OPUS readme: sem-sem
* model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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: 4.2, chr-F: 0.200
testset: URL, BLEU: 34.0, chr-F: 0.542
testset: URL, BLEU: 16.6, chr-F: 0.513
testset: URL, BLEU: 18.8, chr-F: 0.477
testset: URL, BLEU: 20.7, chr-F: 0.388
testset: URL, BLEU: 27.1, chr-F: 0.507