mirror of
https://github.com/explosion/spaCy.git
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fbfc418745
* keep trf tok2vec and wordpiecer components during update * also support transformer models for other example scripts
94 lines
3.1 KiB
Python
94 lines
3.1 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 srsly
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import spacy
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from spacy.gold import GoldParse
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from spacy.util import minibatch, compounding
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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 doc
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def read_gold_data(nlp, gold_loc):
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docs = []
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golds = []
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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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gold = GoldParse(doc, entities=ents)
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docs.append(doc)
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golds.append(gold)
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return list(zip(docs, golds))
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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_docs = 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.alpha = 0.1
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optimizer.b1 = 0.0
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optimizer.b2 = 0.0
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# get names of other pipes to disable them during training
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pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
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sizes = compounding(1.0, 4.0, 1.001)
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with nlp.disable_pipes(*other_pipes):
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(raw_docs)
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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_docs, size=4)
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for batch in minibatch(TRAIN_DATA, size=sizes):
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docs, golds = zip(*batch)
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nlp.update(docs, golds, 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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