include ../../_includes/_mixins p | All #[+a("/docs/usage/models") spaCy models] support online learning, so | you can update a pre-trained model with new examples. You can even add | new classes to an existing model, to recognise a new entity type, | part-of-speech, or syntactic relation. Updating an existing model is | particularly useful as a "quick and dirty solution", if you have only a | few corrections or annotations. +h(2, "improving-accuracy") Improving accuracy on existing entity types p | To update the model, you first need to create an instance of | #[+api("goldparse") #[code spacy.gold.GoldParse]], with the entity labels | you want to learn. You will then pass this instance to the | #[+api("entityrecognizer#update") #[code EntityRecognizer.update()]] | method. For example: +code. import spacy from spacy.gold import GoldParse nlp = spacy.load('en') doc = nlp.make_doc(u'Facebook released React in 2014') gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE']) nlp.entity.update(doc, gold) p | You'll usually need to provide many examples to meaningfully improve the | system — a few hundred is a good start, although more is better. 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. p | One way to avoid this "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(2, "adding") Adding a new entity type p | You can add new entity types to an existing model. Let's say we want to | recognise the category #[code TECHNOLOGY]. The new category will include | programming languages, frameworks and platforms. First, we need to | register the new entity type: +code. nlp.entity.add_label('TECHNOLOGY') p | Next, iterate over your examples, calling #[code entity.update()]. As | above, we want to avoid iterating over only a small number of sentences. | A useful compromise is to run the model over a number of plain-text | sentences, and pass the entities to #[code GoldParse], as "true" | annotations. This encourages the optimizer to find a solution that | predicts the new category with minimal difference from the previous | output. +h(2, "saving-loading") Saving and loading p | After training our model, you'll usually want to save its state, and load | it back later. You can do this with the #[code Language.save_to_directory()] | method: +code. nlp.save_to_directory('/home/me/data/en_technology') p | To make the model more convenient to deploy, we recommend wrapping it as | a Python package, so that you can install it via pip and load it as a | module. spaCy comes with a handy #[+a("/docs/usage/cli#package") CLI command] | to create all required files and directories. +code(false, "bash"). python -m spacy package /home/me/data/en_technology /home/me/my_models p | To build the package and create a #[code .tar.gz] archive, run | #[code python setup.py sdist] from within its directory. +infobox("Saving and loading models") | For more information and a detailed guide on how to package your model, | see the documentation on | #[+a("/docs/usage/saving-loading") saving and loading models]. p | After you've generated and installed the package, you'll be able to | load the model as follows: +code. import en_technology nlp = en_technology.load() +h(2, "example") Example: Adding and training an #[code ANIMAL] entity 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 four sentences are | provided as examples. In practice, you'll need many more — | #[strong a few hundred] would be a good start. You will also likely need | to mix in #[strong 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. p | For the full, runnable script of this example, see | #[+src(gh("spacy", "examples/training/train_new_entity_type.py")) train_new_entity_type.py]. +code("Training the entity recognizer"). import spacy from spacy.pipeline import EntityRecognizer from spacy.gold import GoldParse from spacy.tagger import Tagger import random model_name = 'en' entity_label = 'ANIMAL' output_directory = '/path/to/model' train_data = [ ("Horses are too tall and they pretend to care about your feelings", [(0, 6, 'ANIMAL')]), ("horses are too tall and they pretend to care about your feelings", [(0, 6, 'ANIMAL')]), ("horses pretend to care about your feelings", [(0, 6, 'ANIMAL')]), ("they pretend to care about your feelings, those horses", [(48, 54, 'ANIMAL')]) ] nlp = spacy.load(model_name) nlp.entity.add_label(entity_label) ner = train_ner(nlp, train_data, output_directory) def train_ner(nlp, train_data, output_dir): # Add new words to vocab for raw_text, _ in train_data: doc = nlp.make_doc(raw_text) for word in doc: _ = nlp.vocab[word.orth] for itn in range(20): random.shuffle(train_data) for raw_text, entity_offsets in train_data: gold = GoldParse(doc, entities=entity_offsets) doc = nlp.make_doc(raw_text) nlp.tagger(doc) loss = nlp.entity.update(doc, gold) nlp.end_training() nlp.save_to_directory(output_dir) p +button(gh("spaCy", "examples/training/train_new_entity_type.py"), false, "secondary") Full example p | The actual training is performed by looping over the examples, and | calling #[code nlp.entity.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 #[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. p | After training your model, you can | #[+a("/docs/usage/saving-loading") save it to a directory]. We recommend wrapping | models as Python packages, for ease of deployment.