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86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
"""This example shows how to add a multi-task objective that is trained
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alongside the entity recognizer. This is an alternative to adding features
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to the model.
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The multi-task idea is to train an auxiliary model to predict some attribute,
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with weights shared between the auxiliary model and the main model. In this
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example, we're predicting the position of the word in the document.
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The model that predicts the position of the word encourages the convolutional
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layers to include the position information in their representation. The
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information is then available to the main model, as a feature.
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The overall idea is that we might know something about what sort of features
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we'd like the CNN to extract. The multi-task objectives can encourage the
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extraction of this type of feature. The multi-task objective is only used
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during training. We discard the auxiliary model before run-time.
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The specific example here is not necessarily a good idea --- but it shows
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how an arbitrary objective function for some word can be used.
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Developed and tested for spaCy 2.0.6
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"""
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import random
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import plac
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import spacy
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import os.path
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from spacy.gold import read_json_file, GoldParse
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random.seed(0)
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PWD = os.path.dirname(__file__)
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TRAIN_DATA = list(read_json_file(os.path.join(PWD, "training-data.json")))
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def get_position_label(i, words, tags, heads, labels, ents):
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"""Return labels indicating the position of the word in the document.
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"""
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if len(words) < 20:
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return "short-doc"
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elif i == 0:
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return "first-word"
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elif i < 10:
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return "early-word"
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elif i < 20:
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return "mid-word"
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elif i == len(words) - 1:
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return "last-word"
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else:
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return "late-word"
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def main(n_iter=10):
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nlp = spacy.blank("en")
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ner = nlp.create_pipe("ner")
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ner.add_multitask_objective(get_position_label)
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nlp.add_pipe(ner)
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print("Create data", len(TRAIN_DATA))
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optimizer = nlp.begin_training(get_gold_tuples=lambda: TRAIN_DATA)
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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losses = {}
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for text, annot_brackets in TRAIN_DATA:
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annotations, _ = annot_brackets
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doc = nlp.make_doc(text)
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gold = GoldParse.from_annot_tuples(doc, annotations[0])
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nlp.update(
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[doc], # batch of texts
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[gold], # batch of annotations
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drop=0.2, # dropout - make it harder to memorise data
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sgd=optimizer, # callable to update weights
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losses=losses,
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)
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print(losses.get("nn_labeller", 0.0), losses["ner"])
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# test the trained model
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for text, _ in TRAIN_DATA:
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doc = nlp(text)
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print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
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print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
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if __name__ == "__main__":
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plac.call(main)
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