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2017-05-25 12:18:02 +03:00
//- 💫 DOCS > USAGE > SPACY 101 > TRAINING
2017-06-01 12:53:16 +03:00
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| 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.
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| 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.
+image
include ../../../assets/img/docs/training.svg
.u-text-right
+button("/assets/img/docs/training.svg", false, "secondary").u-text-tag View large graphic
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| 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.
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| 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.