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Helsinki-NLP/opus-mt-zlw-zlw Helsinki-NLP/opus-mt-zlw-zlw
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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-zlw-zlw") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zlw-zlw")
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zlw-zlw

  • source group: West Slavic languages

  • target group: West Slavic languages

  • OPUS readme: zlw-zlw

  • model: transformer

  • source language(s): ces dsb hsb pol

  • target language(s): ces dsb hsb pol

  • 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

  • test set translations: opus-2020-07-27.test.txt

  • test set scores: opus-2020-07-27.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.ces-hsb.ces.hsb 2.6 0.167
Tatoeba-test.ces-pol.ces.pol 44.0 0.649
Tatoeba-test.dsb-pol.dsb.pol 8.5 0.250
Tatoeba-test.hsb-ces.hsb.ces 9.6 0.276
Tatoeba-test.multi.multi 38.8 0.580
Tatoeba-test.pol-ces.pol.ces 43.4 0.620
Tatoeba-test.pol-dsb.pol.dsb 2.1 0.159

System Info: