Principle of map updating

Collecting training data may sound incredibly painful – and it can be, if you're planning a large-scale annotation project.However, if your main goal is to update an existing model's predictions – for example, spa Cy's named entity recognition – the hard part is 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

Collecting training data may sound incredibly painful – and it can be, if you're planning a large-scale annotation project.However, if your main goal is to update an existing model's predictions – for example, spa Cy's named entity recognition – the hard part is 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.and its gold-standard annotations, the model can be updated to learn a sentence of three words with their assigned part-of-speech tags.The tag map is part of the vocabulary and defines the annotation scheme.A model trained on Wikipedia, where sentences in the first person are extremely rare, will likely perform badly on Twitter.

||

Collecting training data may sound incredibly painful – and it can be, if you're planning a large-scale annotation project.

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.and its gold-standard annotations, the model can be updated to learn a sentence of three words with their assigned part-of-speech tags.The tag map is part of the vocabulary and defines the annotation scheme.A model trained on Wikipedia, where sentences in the first person are extremely rare, will likely perform badly on Twitter.

principle of map updating-85

That's why the training data should always be representative of the data we want to process.

If you're training a new language model, this will let you map the tags present in the treebank you train on to spa Cy's tag scheme.

Of course, it's not enough to only show a model a single example once.

For example, after processing a few sentences, you may end up with the following entities, some correct, some incorrect.

As a rule of thumb, you should allocate at least 10% of your project resources to creating training and evaluation data.

Leave a Reply