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Helsinki-NLP/opus-mt-en-sit Helsinki-NLP/opus-mt-en-sit
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last 30 days

pytorch

tf

Contributed by

Language Technology Research Group at the University of Helsinki university
1 team member · 1325 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-sit") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-sit")
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eng-sit

  • source group: English

  • target group: Sino-Tibetan languages

  • OPUS readme: eng-sit

  • model: transformer

  • source language(s): eng

  • target language(s): bod brx brx_Latn cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans mya nan wuu yue yue_Hans yue_Hant zho zho_Hans zho_Hant

  • 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

  • test set translations: opus2m-2020-08-01.test.txt

  • test set scores: opus2m-2020-08-01.eval.txt

Benchmarks

testset BLEU chr-F
newsdev2017-enzh-engzho.eng.zho 23.5 0.217
newstest2017-enzh-engzho.eng.zho 23.2 0.223
newstest2018-enzh-engzho.eng.zho 25.0 0.230
newstest2019-enzh-engzho.eng.zho 20.2 0.225
Tatoeba-test.eng-bod.eng.bod 0.4 0.147
Tatoeba-test.eng-brx.eng.brx 0.5 0.012
Tatoeba-test.eng.multi 25.7 0.223
Tatoeba-test.eng-mya.eng.mya 0.2 0.222
Tatoeba-test.eng-zho.eng.zho 29.2 0.249

System Info: