mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
61472e7cb3
* Update _training.md Fix broken link on backpropagation * Add agreement add spacy contributor agreement
42 lines
2.5 KiB
Markdown
42 lines
2.5 KiB
Markdown
spaCy's tagger, parser, text categorizer and many other components are powered
|
||
by **statistical models**. Every "decision" these components make – for example,
|
||
which part-of-speech tag to assign, or whether a word is a named entity – is a
|
||
**prediction** based on the model's current **weight values**. The weight values
|
||
are estimated based on examples the model has seen during **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.
|
||
|
||
Training is an iterative process in which the model's predictions are compared
|
||
against the reference annotations in order to estimate the **gradient of the
|
||
loss**. The gradient of the loss is then used to calculate the gradient of the
|
||
weights through [backpropagation](https://thinc.ai/docs/backprop101). The gradients
|
||
indicate how the weight values should be changed so that the model's predictions
|
||
become more similar to the reference labels over time.
|
||
|
||
> - **Training data:** Examples and their annotations.
|
||
> - **Text:** The input text the model should predict a label for.
|
||
> - **Label:** The label the model should predict.
|
||
> - **Gradient:** The direction and rate of change for a numeric value.
|
||
> Minimising the gradient of the weights should result in predictions that are
|
||
> closer to the reference labels on the training data.
|
||
|
||
|
||
|
||
When training a model, we don't just want it to memorize our examples – we want
|
||
it to come up with a theory that can be **generalized across unseen data**.
|
||
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 _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.
|
||
|
||
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 **training data** – you'll
|
||
also need **evaluation data**. If you only test the model with the data it was
|
||
trained on, you'll have no idea how well it's generalizing. If you want to train
|
||
a model from scratch, you usually need at least a few hundred examples for both
|
||
training and evaluation.
|