spaCy/website/docs/usage/101/_training.md
Sofie Van Landeghem 0b4b4f1819 Documentation for Entity Linking (#4065)
* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* typo fix

* add candidate API to kb documentation

* update API sidebar with EntityLinker and KnowledgeBase

* remove EL from 101 docs

* remove entity linker from 101 pipelines / rephrase

* custom el model instead of existing model

* set version to 2.2 for EL functionality

* update documentation for 2 CLI scripts
2019-09-12 11:38:34 +02:00

39 lines
2.4 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

spaCy's models are **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
**prediction**. This prediction is based on the 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.
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 **error gradient** of the **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.
> - **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:** Gradient of the loss function calculating the difference
> between input and expected output.
![The training process](../../images/training.svg)
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 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 _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. To update an existing model, you can already achieve
decent results with very few examples as long as they're representative.