mirror of
https://github.com/explosion/spaCy.git
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fcbf899b08
* remove _convert_examples * fix test_gold, raise TypeError if tuples are used instead of Example's * throwing proper errors when the wrong type of objects are passed * fix deprectated format in tests * fix deprectated format in parser tests * fix tests for NEL, morph, senter, tagger, textcat * update regression tests with new Example format * use make_doc * more fixes to nlp.update calls * few more small fixes for rehearse and evaluate * only import ml_datasets if really necessary
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
"""Prevent catastrophic forgetting with rehearsal updates."""
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import plac
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import random
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import warnings
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import srsly
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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# TODO: further fix & test this script for v.3 ? (read_gold_data is never called)
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LABEL = "ANIMAL"
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TRAIN_DATA = [
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(
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"Horses are too tall and they pretend to care about your feelings",
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{"entities": [(0, 6, "ANIMAL")]},
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),
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("Do they bite?", {"entities": []}),
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(
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"horses are too tall and they pretend to care about your feelings",
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{"entities": [(0, 6, "ANIMAL")]},
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),
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("horses pretend to care about your feelings", {"entities": [(0, 6, "ANIMAL")]}),
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(
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"they pretend to care about your feelings, those horses",
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{"entities": [(48, 54, "ANIMAL")]},
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),
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("horses?", {"entities": [(0, 6, "ANIMAL")]}),
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]
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def read_raw_data(nlp, jsonl_loc):
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for json_obj in srsly.read_jsonl(jsonl_loc):
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if json_obj["text"].strip():
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doc = nlp.make_doc(json_obj["text"])
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yield Example.from_dict(doc, {})
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def read_gold_data(nlp, gold_loc):
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examples = []
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for json_obj in srsly.read_jsonl(gold_loc):
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doc = nlp.make_doc(json_obj["text"])
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ents = [(ent["start"], ent["end"], ent["label"]) for ent in json_obj["spans"]]
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example = Example.from_dict(doc, {"entities": ents})
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examples.append(example)
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return examples
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def main(model_name, unlabelled_loc):
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n_iter = 10
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dropout = 0.2
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batch_size = 4
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nlp = spacy.load(model_name)
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nlp.get_pipe("ner").add_label(LABEL)
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raw_examples = list(read_raw_data(nlp, unlabelled_loc))
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optimizer = nlp.resume_training()
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# Avoid use of Adam when resuming training. I don't understand this well
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# yet, but I'm getting weird results from Adam. Try commenting out the
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# nlp.update(), and using Adam -- you'll find the models drift apart.
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# I guess Adam is losing precision, introducing gradient noise?
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optimizer.learn_rate = 0.1
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optimizer.b1 = 0.0
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optimizer.b2 = 0.0
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sizes = compounding(1.0, 4.0, 1.001)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
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# show warnings for misaligned entity spans once
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warnings.filterwarnings("once", category=UserWarning, module="spacy")
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for itn in range(n_iter):
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random.shuffle(train_examples)
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random.shuffle(raw_examples)
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losses = {}
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r_losses = {}
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# batch up the examples using spaCy's minibatch
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raw_batches = minibatch(raw_examples, size=4)
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for batch in minibatch(train_examples, size=sizes):
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nlp.update(batch, sgd=optimizer, drop=dropout, losses=losses)
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raw_batch = list(next(raw_batches))
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nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
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print("Losses", losses)
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print("R. Losses", r_losses)
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print(nlp.get_pipe("ner").model.unseen_classes)
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test_text = "Do you like horses?"
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doc = nlp(test_text)
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print("Entities in '%s'" % test_text)
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for ent in doc.ents:
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print(ent.label_, ent.text)
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if __name__ == "__main__":
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plac.call(main)
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