spaCy/website/docs/usage/_spacy-101/_training.jade

//- 💫 DOCS > USAGE > SPACY 101 > TRAINING

p
    |  spaCy's models are #[strong 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 #[strong prediction]. This prediction is based
    |  on the examples the model has seen during #[strong training]. To train
    |  a model, you first need training data – examples of text, and the
    |  labels you want the model to predict. This could be a part-of-speech tag,
    |  a named entity or any other information.

p
    |  The model is then shown the unlabelled text and will make a prediction.
    |  Because we know the correct answer, we can give the model feedback on its
    |  prediction in the form of an #[strong error gradient] of the
    |  #[strong loss function] that calculates the difference between the training
    |  example and the expected output. The greater the difference, the more
    |  significant the gradient and the updates to our model.

+aside
    |  #[strong Training data:] Examples and their annotations.#[br]
    |  #[strong Text:] The input text the model should predict a label for.#[br]
    |  #[strong Label:] The label the model should predict.#[br]
    |  #[strong Gradient:] Gradient of the loss function calculating the
    |  difference between input and expected output.

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    include ../../../assets/img/docs/training.svg
    .u-text-right
        +button("/assets/img/docs/training.svg", false, "secondary").u-text-tag View large graphic

p
    |  When training a model, we don't just want it to memorise our examples –
    |  we want it to come up with theory that can be
    |  #[strong generalised across other examples]. 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 #[em like this],
    |  is most likely a company. That's why the training data should always be
    |  representative of the data we want to process. A model trained on
    |  Wikipedia, where sentences in the first person are extremely rare, will
    |  likely perform badly on Twitter. Similarly, a model trained on romantic
    |  novels will likely perform badly on legal text.

p
    |  This also means that in order to know how the model is performing,
    |  and whether it's learning the right things, you don't only need
    |  #[strong training data] – you'll also need #[strong evaluation data]. If
    |  you only test the model with the data it was trained on, you'll have no
    |  idea how well it's generalising. 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.