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* 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
39 lines
2.4 KiB
Markdown
39 lines
2.4 KiB
Markdown
spaCy's models are **statistical** and every "decision" they make – for example,
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which part-of-speech tag to assign, or whether a word is a named entity – is a
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**prediction**. This prediction is based on the examples the model has seen
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during **training**. To train a model, you first need training data – examples
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of text, and the labels you want the model to predict. This could be a
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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
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we know the correct answer, we can give the model feedback on its prediction in
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the form of an **error gradient** of the **loss function** that calculates the
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difference between the training example and the expected output. The greater the
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difference, the more significant the gradient and the updates to our model.
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> - **Training data:** Examples and their annotations.
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> - **Text:** The input text the model should predict a label for.
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> - **Label:** The label the model should predict.
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> - **Gradient:** Gradient of the loss function calculating the difference
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> between input and expected output.
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When training a model, we don't just want it to memorize our examples – we want
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it to come up with a theory that can be **generalized across other examples**.
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After all, we don't just want the model to learn that this one instance of
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"Amazon" right here is a company – we want it to learn that "Amazon", in
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contexts _like this_, is most likely a company. That's why the training data
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should always be representative of the data we want to process. A model trained
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on 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 novels
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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
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it's learning the right things, you don't only need **training data** – you'll
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also need **evaluation data**. If you only test the model with the data it was
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trained on, you'll have no idea how well it's generalizing. If you want to train
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a model from scratch, you usually need at least a few hundred examples for both
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training and evaluation. To update an existing model, you can already achieve
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decent results with very few examples – as long as they're representative.
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