{"push_id":2992506691,"size":2,"distinct_size":2,"ref":"refs/heads/master","head":"76bbd3da6d520a6ba0d916a722c7809f8b392587","before":"df529586227865e148005d52f663c35b32138c05","commits":[{"sha":"408229ae54b91fcdc6760ac96e5bf177c40d01c7","author":{"name":"Deepak Gopinath","email":"12caa547bf14f1c1837b97527b55a0bee7fb20db@fb.com"},"message":"Semi supervised task setup, dataset loading and model building (#243)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/translate/pull/243\n\nPull Request resolved: https://github.com/pytorch/fairseq/pull/326\n\nThis code is heavily inspired (aka copied in parts) from @[139800366:Myle Ott]'s multilingual transformers implementation in [fairseq's PR #385](https://github.com/fairinternal/fairseq-py/pull/385/commits/18fa6e154781cf0c4b1596429dba7e753a545069). I am using pytorch_translate repo as a test bed to play around with the implementation before submitting a clean PR to fairseq.\n\nThe setup uses a new PytorchTranslateSemiSupervised task introduced in D10228123. The basic idea is to use FairseqMultiModel in conjunction with RoundRobinZipDataset to train 4 \"language pairs\":\n1) source-target with LanguagePairDataset\n2) target-source with reverse LanguagePairDataset\n3) source-target_mono with BacktranslationDataset\n4) target-source_mono with BacktranslationDataset\n\n1 and 3, and 2 and 4 use the same models.\n\nThis is not multilingual yet, in that only one source_lang and one target_lang can be provided. However, this can easily be converted to multilingual semi supervised training by removing the dependency on source_lang and target_lang to prepare lang_pairs.\n\nIn the next diffs, we could consider adding support for:\nChar source model\nWeighted dataset\nMultilingual multi_model\n\nReviewed By: liezl200\n\nDifferential Revision: D10318226\n\nfbshipit-source-id: 59dbfd16eea3106be7e3176fcb9fc135f58942fd","distinct":true,"url":"https://api.github.com/repos/pytorch/translate/commits/408229ae54b91fcdc6760ac96e5bf177c40d01c7"},{"sha":"76bbd3da6d520a6ba0d916a722c7809f8b392587","author":{"name":"Deepak Gopinath","email":"12caa547bf14f1c1837b97527b55a0bee7fb20db@fb.com"},"message":"Support for multi model generation and bleu evaluation (#247)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/translate/pull/247\n\nGeneration and related methods have always handled single models and single datasets. This diff adds support for multi model generation -- generating translations from multiple models in a FairseqMultiModel object, each with their own tune/test dataset from corresponding dataset in RoundRobinZipDataset object.\n\nWe use `lang_pair` as the key to load a particular model. The same key is used to load corresponding dataset from RoundRobinZipDataset, which is a property of PyTorchSemisupervisedTask\n\nReviewed By: liezl200\n\nDifferential Revision: D10525011\n\nfbshipit-source-id: 1cc8ed272e1c0253ec6d4bb139058c8c8857db91","distinct":true,"url":"https://api.github.com/repos/pytorch/translate/commits/76bbd3da6d520a6ba0d916a722c7809f8b392587"}]}
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