Updating training


05-Aug-2017 20:10

If you want to train a model from scratch, you usually need at least a few hundred examples for both training and evaluation.To update an existing model, you can already achieve decent results with very few examples – as long as they're representative.After all, we don't just want the model to learn that this one instance of "Amazon" right here is a company – we want it to learn that "Amazon", in contexts , is most likely a company.That's why the training data should always be representative of the data we want to process.

However, if your main goal is to update an existing model's predictions – for example, spa Cy's named entity recognition – the hard is part usually not creating the actual annotations.While there are some entity annotations that are more or less universally correct – like Canada being a geopolitical entity – your application may have its very own definition of the NER annotation scheme.train_data = [ ("Uber blew through

However, if your main goal is to update an existing model's predictions – for example, spa Cy's named entity recognition – the hard is part usually not creating the actual annotations.

While there are some entity annotations that are more or less universally correct – like Canada being a geopolitical entity – your application may have its very own definition of the NER annotation scheme.train_data = [ ("Uber blew through $1 million a week", [(0, 4, 'ORG')]), ("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]), ("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]), ("Google Maps launches location sharing", [(0, 11, "PRODUCT")]), ("Google rebrands its business apps", [(0, 6, "ORG")]), ("look what i found on google!

😂", [(21, 27, "PRODUCT")])] If you need to label a lot of data, check out Prodigy, a new, active learning-powered annotation tool we've developed.

If you're looking to improve an existing model, you might be able to start off with only a handful of examples.

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However, if your main goal is to update an existing model's predictions – for example, spa Cy's named entity recognition – the hard is part usually not creating the actual annotations.While there are some entity annotations that are more or less universally correct – like Canada being a geopolitical entity – your application may have its very own definition of the NER annotation scheme.train_data = [ ("Uber blew through $1 million a week", [(0, 4, 'ORG')]), ("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]), ("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]), ("Google Maps launches location sharing", [(0, 11, "PRODUCT")]), ("Google rebrands its business apps", [(0, 6, "ORG")]), ("look what i found on google!😂", [(21, 27, "PRODUCT")])] If you need to label a lot of data, check out Prodigy, a new, active learning-powered annotation tool we've developed.If you're looking to improve an existing model, you might be able to start off with only a handful of examples.

million a week", [(0, 4, 'ORG')]), ("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]), ("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]), ("Google Maps launches location sharing", [(0, 11, "PRODUCT")]), ("Google rebrands its business apps", [(0, 6, "ORG")]), ("look what i found on google!😂", [(21, 27, "PRODUCT")])] If you need to label a lot of data, check out Prodigy, a new, active learning-powered annotation tool we've developed.If you're looking to improve an existing model, you might be able to start off with only a handful of examples.

updating training-19

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Keep in mind that you'll always want a lot more than that for evaluation – especially previous errors the model has made.For example, after processing a few sentences, you may end up with the following entities, some correct, some incorrect.



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