mirror of https://github.com/explosion/spaCy.git
42 lines
2.5 KiB
Plaintext
42 lines
2.5 KiB
Plaintext
spaCy's tagger, parser, text categorizer and many other components are powered
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by **statistical models**. Every "decision" these components make – for example,
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which part-of-speech tag to assign, or whether a word is a named entity – is a
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**prediction** based on the model's current **weight values**. The weight values
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are estimated based on examples the model has seen during **training**. To train
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a model, you first need training data – examples of text, and the labels you
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want the model to predict. This could be a part-of-speech tag, a named entity or
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any other information.
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Training is an iterative process in which the model's predictions are compared
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against the reference annotations in order to estimate the **gradient of the
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loss**. The gradient of the loss is then used to calculate the gradient of the
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weights through [backpropagation](https://thinc.ai/docs/backprop101). The
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gradients indicate how the weight values should be changed so that the model's
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predictions become more similar to the reference labels over time.
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> - **Training data:** Examples and their annotations.
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> - **Text:** The input text the model should predict a label for.
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> - **Label:** The label the model should predict.
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> - **Gradient:** The direction and rate of change for a numeric value.
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> Minimising the gradient of the weights should result in predictions that are
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> closer to the reference labels on the training data.
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When training a model, we don't just want it to memorize our examples – we want
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it to come up with a theory that can be **generalized across unseen data**.
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After all, we don't just want the model to learn that this one instance of
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"Amazon" right here is a company – we want it to learn that "Amazon", in
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contexts _like this_, is most likely a company. That's why the training data
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should always be representative of the data we want to process. A model trained
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on Wikipedia, where sentences in the first person are extremely rare, will
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likely perform badly on Twitter. Similarly, a model trained on romantic novels
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will likely perform badly on legal text.
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This also means that in order to know how the model is performing, and whether
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it's learning the right things, you don't only need **training data** – you'll
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also need **evaluation data**. If you only test the model with the data it was
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trained on, you'll have no idea how well it's generalizing. If you want to train
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a model from scratch, you usually need at least a few hundred examples for both
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training and evaluation.
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