spaCy/website/usage/_training/_ner.jade

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2017-10-03 15:26:20 +03:00
//- 💫 DOCS > USAGE > TRAINING > NER
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| All #[+a("/models") spaCy models] support online learning, so
| you can update a pre-trained model with new examples. To update the
| model, you first need to create an instance of
| #[+api("goldparse") #[code GoldParse]], with the entity labels
| you want to learn. You'll usually need to provide many examples to
| meaningfully improve the system — a few hundred is a good start, although
| more is better.
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| You should avoid iterating over the same few examples multiple times, or
| the model is likely to "forget" how to annotate other examples. If you
| iterate over the same few examples, you're effectively changing the loss
| function. The optimizer will find a way to minimize the loss on your
| examples, without regard for the consequences on the examples it's no
| longer paying attention to. One way to avoid this
| #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting" problem]
| is to "remind"
| the model of other examples by augmenting your annotations with sentences
| annotated with entities automatically recognised by the original model.
| Ultimately, this is an empirical process: you'll need to
| #[strong experiment on your own data] to find a solution that works best
| for you.
+h(3, "example-new-entity-type") Example: Training an additional entity type
p
| This script shows how to add a new entity type to an existing pre-trained
| NER model. To keep the example short and simple, only a few sentences are
| provided as examples. In practice, you'll need many more — a few hundred
| would be a good start. You will also likely need to mix in examples of
| other entity types, which might be obtained by running the entity
| recognizer over unlabelled sentences, and adding their annotations to the
| training set.
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| The actual training is performed by looping over the examples, and
| calling #[+api("language#update") #[code nlp.update()]]. The
| #[code update] method steps through the words of the input. At each word,
| it makes a prediction. It then consults the annotations provided on the
| #[+api("goldparse") #[code GoldParse]] instance, to see whether it was
| right. If it was wrong, it adjusts its weights so that the correct
| action will score higher next time.
+github("spacy", "examples/training/train_new_entity_type.py")
+h(3, "example-ner-from-scratch") Example: Training an NER system from scratch
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| This example is written to be self-contained and reasonably transparent.
| To achieve that, it duplicates some of spaCy's internal functionality.
| Specifically, in this example, we don't use spaCy's built-in
| #[+api("language") #[code Language]] class to wire together the
| #[+api("vocab") #[code Vocab]], #[+api("tokenizer") #[code Tokenizer]]
| and #[+api("entityrecognizer") #[code EntityRecognizer]]. Instead, we
| write our own simle #[code Pipeline] class, so that it's easier to see
| how the pieces interact.
+github("spacy", "examples/training/train_ner_standalone.py")