
### afa-afa

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

*  model: transformer
* source language(s): apc ara arq arz heb kab mlt shy_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy_Latn thv
* 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-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.zip)
* test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.test.txt)
* test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara-ara.ara.ara 	| 4.3 	| 0.148 |
| Tatoeba-test.ara-heb.ara.heb 	| 31.9 	| 0.525 |
| Tatoeba-test.ara-kab.ara.kab 	| 0.3 	| 0.120 |
| Tatoeba-test.ara-mlt.ara.mlt 	| 14.0 	| 0.428 |
| Tatoeba-test.ara-shy.ara.shy 	| 1.3 	| 0.050 |
| Tatoeba-test.heb-ara.heb.ara 	| 17.0 	| 0.464 |
| Tatoeba-test.heb-kab.heb.kab 	| 1.9 	| 0.104 |
| Tatoeba-test.kab-ara.kab.ara 	| 0.3 	| 0.044 |
| Tatoeba-test.kab-heb.kab.heb 	| 5.1 	| 0.099 |
| Tatoeba-test.kab-shy.kab.shy 	| 2.2 	| 0.009 |
| Tatoeba-test.kab-tmh.kab.tmh 	| 10.7 	| 0.007 |
| Tatoeba-test.mlt-ara.mlt.ara 	| 29.1 	| 0.498 |
| Tatoeba-test.multi.multi 	| 20.8 	| 0.434 |
| Tatoeba-test.shy-ara.shy.ara 	| 1.2 	| 0.053 |
| Tatoeba-test.shy-kab.shy.kab 	| 2.0 	| 0.134 |
| Tatoeba-test.tmh-kab.tmh.kab 	| 0.0 	| 0.047 |


### System Info: 
- hf_name: afa-afa

- source_languages: afa

- target_languages: afa

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

- original_repo: Tatoeba-Challenge

- tags: ['translation']

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

- src_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}

- tgt_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}

- src_multilingual: True

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.test.txt

- src_alpha3: afa

- tgt_alpha3: afa

- short_pair: afa-afa

- chrF2_score: 0.434

- bleu: 20.8

- brevity_penalty: 1.0

- ref_len: 15215.0

- src_name: Afro-Asiatic languages

- tgt_name: Afro-Asiatic languages

- train_date: 2020-07-26

- src_alpha2: afa

- tgt_alpha2: afa

- prefer_old: False

- long_pair: afa-afa

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

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

### afa-afa


* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: afa-afa
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* 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: 4.3, chr-F: 0.148
testset: URL, BLEU: 31.9, chr-F: 0.525
testset: URL, BLEU: 0.3, chr-F: 0.120
testset: URL, BLEU: 14.0, chr-F: 0.428
testset: URL, BLEU: 1.3, chr-F: 0.050
testset: URL, BLEU: 17.0, chr-F: 0.464
testset: URL, BLEU: 1.9, chr-F: 0.104
testset: URL, BLEU: 0.3, chr-F: 0.044
testset: URL, BLEU: 5.1, chr-F: 0.099
testset: URL, BLEU: 2.2, chr-F: 0.009
testset: URL, BLEU: 10.7, chr-F: 0.007
testset: URL, BLEU: 29.1, chr-F: 0.498
testset: URL, BLEU: 20.8, chr-F: 0.434
testset: URL, BLEU: 1.2, chr-F: 0.053
testset: URL, BLEU: 2.0, chr-F: 0.134
testset: URL, BLEU: 0.0, chr-F: 0.047


### System Info:


* hf\_name: afa-afa
* source\_languages: afa
* target\_languages: afa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']
* src\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* tgt\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afa
* tgt\_alpha3: afa
* short\_pair: afa-afa
* chrF2\_score: 0.434
* bleu: 20.8
* brevity\_penalty: 1.0
* ref\_len: 15215.0
* src\_name: Afro-Asiatic languages
* tgt\_name: Afro-Asiatic languages
* train\_date: 2020-07-26
* src\_alpha2: afa
* tgt\_alpha2: afa
* prefer\_old: False
* long\_pair: afa-afa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41



### afa-afa


* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: afa-afa
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* 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: 4.3, chr-F: 0.148
testset: URL, BLEU: 31.9, chr-F: 0.525
testset: URL, BLEU: 0.3, chr-F: 0.120
testset: URL, BLEU: 14.0, chr-F: 0.428
testset: URL, BLEU: 1.3, chr-F: 0.050
testset: URL, BLEU: 17.0, chr-F: 0.464
testset: URL, BLEU: 1.9, chr-F: 0.104
testset: URL, BLEU: 0.3, chr-F: 0.044
testset: URL, BLEU: 5.1, chr-F: 0.099
testset: URL, BLEU: 2.2, chr-F: 0.009
testset: URL, BLEU: 10.7, chr-F: 0.007
testset: URL, BLEU: 29.1, chr-F: 0.498
testset: URL, BLEU: 20.8, chr-F: 0.434
testset: URL, BLEU: 1.2, chr-F: 0.053
testset: URL, BLEU: 2.0, chr-F: 0.134
testset: URL, BLEU: 0.0, chr-F: 0.047 ### System Info:


* hf\_name: afa-afa
* source\_languages: afa
* target\_languages: afa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']
* src\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* tgt\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afa
* tgt\_alpha3: afa
* short\_pair: afa-afa
* chrF2\_score: 0.434
* bleu: 20.8
* brevity\_penalty: 1.0
* ref\_len: 15215.0
* src\_name: Afro-Asiatic languages
* tgt\_name: Afro-Asiatic languages
* train\_date: 2020-07-26
* src\_alpha2: afa
* tgt\_alpha2: afa
* prefer\_old: False
* long\_pair: afa-afa
* 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 #so #ti #am #he #mt #ar #afa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### afa-afa


* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: afa-afa
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* 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: 4.3, chr-F: 0.148
testset: URL, BLEU: 31.9, chr-F: 0.525
testset: URL, BLEU: 0.3, chr-F: 0.120
testset: URL, BLEU: 14.0, chr-F: 0.428
testset: URL, BLEU: 1.3, chr-F: 0.050
testset: URL, BLEU: 17.0, chr-F: 0.464
testset: URL, BLEU: 1.9, chr-F: 0.104
testset: URL, BLEU: 0.3, chr-F: 0.044
testset: URL, BLEU: 5.1, chr-F: 0.099
testset: URL, BLEU: 2.2, chr-F: 0.009
testset: URL, BLEU: 10.7, chr-F: 0.007
testset: URL, BLEU: 29.1, chr-F: 0.498
testset: URL, BLEU: 20.8, chr-F: 0.434
testset: URL, BLEU: 1.2, chr-F: 0.053
testset: URL, BLEU: 2.0, chr-F: 0.134
testset: URL, BLEU: 0.0, chr-F: 0.047 ### System Info:


* hf\_name: afa-afa
* source\_languages: afa
* target\_languages: afa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']
* src\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* tgt\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afa
* tgt\_alpha3: afa
* short\_pair: afa-afa
* chrF2\_score: 0.434
* bleu: 20.8
* brevity\_penalty: 1.0
* ref\_len: 15215.0
* src\_name: Afro-Asiatic languages
* tgt\_name: Afro-Asiatic languages
* train\_date: 2020-07-26
* src\_alpha2: afa
* tgt\_alpha2: afa
* prefer\_old: False
* long\_pair: afa-afa
* 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 #so #ti #am #he #mt #ar #afa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 passage: ### afa-afa


* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: afa-afa
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* 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: 4.3, chr-F: 0.148
testset: URL, BLEU: 31.9, chr-F: 0.525
testset: URL, BLEU: 0.3, chr-F: 0.120
testset: URL, BLEU: 14.0, chr-F: 0.428
testset: URL, BLEU: 1.3, chr-F: 0.050
testset: URL, BLEU: 17.0, chr-F: 0.464
testset: URL, BLEU: 1.9, chr-F: 0.104
testset: URL, BLEU: 0.3, chr-F: 0.044
testset: URL, BLEU: 5.1, chr-F: 0.099
testset: URL, BLEU: 2.2, chr-F: 0.009
testset: URL, BLEU: 10.7, chr-F: 0.007
testset: URL, BLEU: 29.1, chr-F: 0.498
testset: URL, BLEU: 20.8, chr-F: 0.434
testset: URL, BLEU: 1.2, chr-F: 0.053
testset: URL, BLEU: 2.0, chr-F: 0.134
testset: URL, BLEU: 0.0, chr-F: 0.047