Blak and white sexwebcam 2013 - 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.
Principle of map updating
Especially if you only have few examples, you'll want to train for a number of iterations.
At each iteration, the training data is shuffled to ensure the model doesn't make any generalisations based on the order of examples.
spa Cy's models are statistical and every "decision" they make – for example, which part-of-speech tag to assign, or whether a word is a named entity – is a prediction.
This prediction is based on the examples the model has seen during training.
In this case, we'll extract mentions of Google and assume they're an Based on the few examples above, you can already create six training sentences with eight entities in total.