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62 lines
3.2 KiB
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62 lines
3.2 KiB
Plaintext
//- 💫 DOCS > USAGE > TRAINING > NER
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p
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| All #[+a("/models") spaCy models] support online learning, so
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| you can update a pre-trained model with new examples. To update the
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| model, you first need to create an instance of
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| #[+api("goldparse") #[code GoldParse]], with the entity labels
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| you want to learn. You'll usually need to provide many examples to
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| meaningfully improve the system — a few hundred is a good start, although
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| more is better.
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p
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| You should avoid iterating over the same few examples multiple times, or
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| the model is likely to "forget" how to annotate other examples. If you
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| iterate over the same few examples, you're effectively changing the loss
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| function. The optimizer will find a way to minimize the loss on your
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| examples, without regard for the consequences on the examples it's no
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| longer paying attention to. One way to avoid this
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| #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting" problem]
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| is to "remind"
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| the model of other examples by augmenting your annotations with sentences
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| annotated with entities automatically recognised by the original model.
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| Ultimately, this is an empirical process: you'll need to
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| #[strong experiment on your own data] to find a solution that works best
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| for you.
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+h(3, "example-new-entity-type") Example: Training an additional entity type
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p
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| This script shows how to add a new entity type to an existing pre-trained
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| NER model. To keep the example short and simple, only a few sentences are
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| provided as examples. In practice, you'll need many more — a few hundred
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| would be a good start. You will also likely need to mix in examples of
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| other entity types, which might be obtained by running the entity
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| recognizer over unlabelled sentences, and adding their annotations to the
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| training set.
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p
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| The actual training is performed by looping over the examples, and
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| calling #[+api("language#update") #[code nlp.update()]]. The
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| #[code update] method steps through the words of the input. At each word,
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| it makes a prediction. It then consults the annotations provided on the
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| #[+api("goldparse") #[code GoldParse]] instance, to see whether it was
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| right. If it was wrong, it adjusts its weights so that the correct
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| action will score higher next time.
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+github("spacy", "examples/training/train_new_entity_type.py")
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+h(3, "example-ner-from-scratch") Example: Training an NER system from scratch
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p
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| This example is written to be self-contained and reasonably transparent.
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| To achieve that, it duplicates some of spaCy's internal functionality.
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| Specifically, in this example, we don't use spaCy's built-in
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| #[+api("language") #[code Language]] class to wire together the
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| #[+api("vocab") #[code Vocab]], #[+api("tokenizer") #[code Tokenizer]]
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| and #[+api("entityrecognizer") #[code EntityRecognizer]]. Instead, we
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| write our own simle #[code Pipeline] class, so that it's easier to see
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| how the pieces interact.
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+github("spacy", "examples/training/train_ner_standalone.py")
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