mirror of https://github.com/explosion/spaCy.git
53 lines
2.8 KiB
Plaintext
53 lines
2.8 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101 > TRAINING
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p
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| spaCy's models are #[strong statistical] and every "decision" they make –
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| for example, which part-of-speech tag to assign, or whether a word is a
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| named entity – is a #[strong prediction]. This prediction is based
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| on the examples the model has seen during #[strong training]. To train
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| a model, you first need training data – examples of text, and the
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| labels you want the model to predict. This could be a part-of-speech tag,
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| a named entity or any other information.
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p
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| The model is then shown the unlabelled text and will make a prediction.
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| Because we know the correct answer, we can give the model feedback on its
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| prediction in the form of an #[strong error gradient] of the
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| #[strong loss function] that calculates the difference between the training
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| example and the expected output. The greater the difference, the more
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| significant the gradient and the updates to our model.
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+aside
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| #[strong Training data:] Examples and their annotations.#[br]
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| #[strong Text:] The input text the model should predict a label for.#[br]
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| #[strong Label:] The label the model should predict.#[br]
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| #[strong Gradient:] Gradient of the loss function calculating the
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| difference between input and expected output.
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+image
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include ../../../assets/img/docs/training.svg
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.u-text-right
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+button("/assets/img/docs/training.svg", false, "secondary").u-text-tag View large graphic
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p
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| When training a model, we don't just want it to memorise our examples –
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| we want it to come up with theory that can be
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| #[strong generalised across other examples]. After all, we don't just want
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| the model to learn that this one instance of "Amazon" right here is a
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| company – we want it to learn that "Amazon", in contexts #[em like this],
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| is most likely a company. That's why the training data should always be
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| representative of the data we want to process. A model trained on
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| 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
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| novels will likely perform badly on legal text.
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p
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| This also means that in order to know how the model is performing,
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| and whether it's learning the right things, you don't only need
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| #[strong training data] – you'll also need #[strong evaluation data]. If
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| you only test the model with the data it was trained on, you'll have no
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| idea how well it's generalising. If you want to train a model from scratch,
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| you usually need at least a few hundred examples for both training and
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| evaluation. To update an existing model, you can already achieve decent
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| results with very few examples – as long as they're representative.
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