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Helsinki-NLP/opus-mt-zh-ms Helsinki-NLP/opus-mt-zh-ms
16 downloads
last 30 days

pytorch

tf

Contributed by

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

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-ms") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-ms")
Uploaded in S3

zho-msa

  • source group: Chinese

  • target group: Malay (macrolanguage)

  • OPUS readme: zho-msa

  • model: transformer-align

  • source language(s): cmn_Bopo cmn_Hani cmn_Latn hak_Hani yue_Bopo yue_Hani

  • target language(s): ind zsm_Latn

  • model: transformer-align

  • 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-2020-06-17.zip

  • test set translations: opus-2020-06-17.test.txt

  • test set scores: opus-2020-06-17.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.zho.msa 13.9 0.390

System Info:

  • hf_name: zho-msa

  • source_languages: zho

  • target_languages: msa

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['zh', 'ms']

  • src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}

  • tgt_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'}

  • src_multilingual: False

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

  • url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.zip

  • url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.test.txt

  • src_alpha3: zho

  • tgt_alpha3: msa

  • short_pair: zh-ms

  • chrF2_score: 0.39

  • bleu: 13.9

  • brevity_penalty: 0.9229999999999999

  • ref_len: 2762.0

  • src_name: Chinese

  • tgt_name: Malay (macrolanguage)

  • train_date: 2020-06-17

  • src_alpha2: zh

  • tgt_alpha2: ms

  • prefer_old: False

  • long_pair: zho-msa

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41