First time leading an initiative for a model. I came in knowing the data wasn’t great… but I pushed through anyway. I’ve come to the conclusion many weeks later that it is not going to be enough. There are too many parameters that make the data quality bad for a NLP model (small dataset, mixed languages, typos galore, etc.) Advice on breaking this to project lead? I understand now that it was far worse for me to “hope for the best” rather than to reject the project.

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Posting as :
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Blunt and quickly

likehelpful

Asap. The best time to do that is gone. The second best time is now.

likehelpful

The sooner expectations are lowered the easier for everyone.

But one thing here... just because there are issues, doesn't mean you can't deliver at reduced scope or increased complexity. You almost never have data that's ready for optimal use at the get go. Think about adding layers. Not everything has to be done by a single transformer and with the data that happens to be on hand. Typos are more or less fixable with autocorrect, language detection is possible for subsetting, etc.

likehelpful

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