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If the argument is set to "soft", print probability for each source token, like this: A-0 0.365083,0.328207,0.306710 0.442428,0.340282,0.217290 0.378712,0.367315,0.253973 0.321335,0.425601,0.253064 Each source token is separated from each other by a comma (,) and each target token is separated from each other by a space ( ). This option is based on the Marian NMT's option.
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Summary: If the argument is set to "soft", print probability for each source token, like this: A-0 0.365083,0.328207,0.306710 0.442428,0.340282,0.217290 0.378712,0.367315,0.253973 0.321335,0.425601,0.253064 Each source token is separated from each other by a comma (,) and each target token is separated from each other by a space ( ). This option is based on the Marian NMT's option. # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? - [x] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � Pull Request resolved: #2985 Reviewed By: alexeib Differential Revision: D25344394 Pulled By: myleott fbshipit-source-id: 659eb8f7af1ccdafacaaa91ce5ddf5d71cb3e775
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Summary: If the argument is set to "soft", print probability for each source token, like this: A-0 0.365083,0.328207,0.306710 0.442428,0.340282,0.217290 0.378712,0.367315,0.253973 0.321335,0.425601,0.253064 Each source token is separated from each other by a comma (,) and each target token is separated from each other by a space ( ). This option is based on the Marian NMT's option. # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? - [x] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � Pull Request resolved: facebookresearch#2985 Reviewed By: alexeib Differential Revision: D25344394 Pulled By: myleott fbshipit-source-id: 659eb8f7af1ccdafacaaa91ce5ddf5d71cb3e775
jinyiyang-jhu
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Feb 26, 2021
Summary: If the argument is set to "soft", print probability for each source token, like this: A-0 0.365083,0.328207,0.306710 0.442428,0.340282,0.217290 0.378712,0.367315,0.253973 0.321335,0.425601,0.253064 Each source token is separated from each other by a comma (,) and each target token is separated from each other by a space ( ). This option is based on the Marian NMT's option. # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? - [x] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � Pull Request resolved: facebookresearch/fairseq#2985 Reviewed By: alexeib Differential Revision: D25344394 Pulled By: myleott fbshipit-source-id: 659eb8f7af1ccdafacaaa91ce5ddf5d71cb3e775
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If the argument is set to "soft", print probability for each source
token, like this:
A-0 0.365083,0.328207,0.306710 0.442428,0.340282,0.217290
0.378712,0.367315,0.253973 0.321335,0.425601,0.253064
Each source token is separated from each other by a comma (,) and each
target token is separated from each other by a space ( ).
This option is based on the Marian NMT's option.
Before submitting
PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Did you have fun?
Make sure you had fun coding 🙃