mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 16:07:41 +03:00
Merge branch 'develop' into spacy.io
This commit is contained in:
Ines Montani
commit
948ca2bb3e
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@ -54,9 +54,9 @@ valuable if it's shared publicly, so that more people can benefit from it.
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| Type | Platforms |
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| ------------------------ | ------------------------------------------------------ |
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| 🚨**Bug Reports** | [GitHub Issue Tracker] |
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| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
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| 🎁 **Feature Requests** | [GitHub Issue Tracker] |
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| 👩💻**Usage Questions** | [Stack Overflow] · [Gitter Chat] · [Reddit User Group] |
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| 👩💻 **Usage Questions** | [Stack Overflow] · [Gitter Chat] · [Reddit User Group] |
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| 🗯 **General Discussion** | [Gitter Chat] · [Reddit User Group] |
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[github issue tracker]: https://github.com/explosion/spaCy/issues
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@ -4,7 +4,7 @@ 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
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from spacy.util import minibatch, compounding
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LABEL = "ANIMAL"
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@ -54,9 +54,17 @@ def main(model_name, unlabelled_loc):
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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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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
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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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@ -64,13 +72,22 @@ def main(model_name, unlabelled_loc):
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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=batch_size)
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for doc, gold in TRAIN_DATA:
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nlp.update([doc], [gold], sgd=optimizer, drop=dropout, losses=losses)
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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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@ -45,19 +45,19 @@ 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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{"entities": [(0, 6, LABEL)]},
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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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{"entities": [(0, 6, LABEL)]},
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),
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("horses pretend to care about your feelings", {"entities": [(0, 6, "ANIMAL")]}),
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("horses pretend to care about your feelings", {"entities": [(0, 6, LABEL)]}),
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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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{"entities": [(48, 54, LABEL)]},
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),
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("horses?", {"entities": [(0, 6, "ANIMAL")]}),
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("horses?", {"entities": [(0, 6, LABEL)]}),
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]
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@ -67,8 +67,9 @@ TRAIN_DATA = [
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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def main(model=None, new_model_name="animal", output_dir=None, n_iter=10):
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def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
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"""Set up the pipeline and entity recognizer, and train the new entity."""
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random.seed(0)
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if model is not None:
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nlp = spacy.load(model) # load existing spaCy model
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print("Loaded model '%s'" % model)
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@ -85,21 +86,22 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=10):
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ner = nlp.get_pipe("ner")
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ner.add_label(LABEL) # add new entity label to entity recognizer
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# Adding extraneous labels shouldn't mess anything up
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ner.add_label('VEGETABLE')
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if model is None:
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optimizer = nlp.begin_training()
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else:
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# Note that 'begin_training' initializes the models, so it'll zero out
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# existing entity types.
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optimizer = nlp.entity.create_optimizer()
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optimizer = nlp.resume_training()
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move_names = list(ner.move_names)
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
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with nlp.disable_pipes(*other_pipes): # only train NER
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sizes = compounding(1.0, 4.0, 1.001)
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# batch up the examples using spaCy's minibatch
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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batches = minibatch(TRAIN_DATA, size=sizes)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(texts, annotations, sgd=optimizer, drop=0.35, losses=losses)
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@ -124,6 +126,8 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=10):
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# test the saved model
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print("Loading from", output_dir)
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nlp2 = spacy.load(output_dir)
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# Check the classes have loaded back consistently
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assert nlp2.get_pipe('ner').move_names == move_names
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doc2 = nlp2(test_text)
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for ent in doc2.ents:
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print(ent.label_, ent.text)
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@ -571,8 +571,6 @@ def build_text_classifier(nr_class, width=64, **cfg):
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zero_init(Affine(nr_class, nr_class * 2, drop_factor=0.0))
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>> logistic
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)
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model = (
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(linear_model | cnn_model)
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>> output_layer
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@ -4,7 +4,7 @@
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# fmt: off
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__title__ = "spacy-nightly"
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__version__ = "2.1.0a9.dev1"
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__version__ = "2.1.0a9.dev2"
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__summary__ = "Industrial-strength Natural Language Processing (NLP) with Python and Cython"
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__uri__ = "https://spacy.io"
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__author__ = "Explosion AI"
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@ -290,7 +290,8 @@ class Errors(object):
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"NBOR_RELOP.")
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E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
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"have been declared in previous edges.")
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E102 = ("Can't merge non-disjoint spans. '{token}' is already part of tokens to merge")
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E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
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"tokens to merge.")
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E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A token"
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" can only be part of one entity, so make sure the entities you're "
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"setting don't overlap.")
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@ -318,12 +319,12 @@ class Errors(object):
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"So instead of pickling the span, pickle the Doc it belongs to or "
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"use Span.as_doc to convert the span to a standalone Doc object.")
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E113 = ("The newly split token can only have one root (head = 0).")
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E114 = ("The newly split token needs to have a root (head = 0)")
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E115 = ("All subtokens must have associated heads")
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E114 = ("The newly split token needs to have a root (head = 0).")
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E115 = ("All subtokens must have associated heads.")
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E116 = ("Cannot currently add labels to pre-trained text classifier. Add "
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"labels before training begins. This functionality was available "
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"in previous versions, but had significant bugs that led to poor "
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"performance")
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"performance.")
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E117 = ("The newly split tokens must match the text of the original token. "
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"New orths: {new}. Old text: {old}.")
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@ -24,52 +24,68 @@ _latin_l_supplement = r"\u00DF-\u00F6\u00F8-\u00FF"
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_latin_supplement = r"\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u00FF"
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# letters with diacritics - Catalan, Czech, Latin, Latvian, Lithuanian, Polish, Slovak, Turkish, Welsh
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_latin_u_extendedA = r"\u0100\u0102\u0104\u0106\u0108\u010A\u010C\u010E\u0110\u0112\u0114\u0116\u0118\u011A\u011C" \
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r"\u011E\u0120\u0122\u0124\u0126\u0128\u012A\u012C\u012E\u0130\u0132\u0134\u0136\u0139\u013B" \
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r"\u013D\u013F\u0141\u0143\u0145\u0147\u014A\u014C\u014E\u0150\u0152\u0154\u0156\u0158" \
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r"\u015A\u015C\u015E\u0160\u0162\u0164\u0166\u0168\u016A\u016C\u016E\u0170\u0172\u0174\u0176" \
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r"\u0178\u0179\u017B\u017D"
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_latin_l_extendedA = r"\u0101\u0103\u0105\u0107\u0109\u010B\u010D\u010F\u0111\u0113\u0115\u0117\u0119\u011B\u011D" \
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r"\u011F\u0121\u0123\u0125\u0127\u0129\u012B\u012D\u012F\u0131\u0133\u0135\u0137\u0138\u013A" \
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r"\u013C\u013E\u0140\u0142\u0144\u0146\u0148\u0149\u014B\u014D\u014F\u0151\u0153\u0155\u0157" \
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r"\u0159\u015B\u015D\u015F\u0161\u0163\u0165\u0167\u0169\u016B\u016D\u016F\u0171\u0173\u0175" \
|
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r"\u0177\u017A\u017C\u017E\u017F"
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_latin_u_extendedA = (
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r"\u0100\u0102\u0104\u0106\u0108\u010A\u010C\u010E\u0110\u0112\u0114\u0116\u0118\u011A\u011C"
|
||||
r"\u011E\u0120\u0122\u0124\u0126\u0128\u012A\u012C\u012E\u0130\u0132\u0134\u0136\u0139\u013B"
|
||||
r"\u013D\u013F\u0141\u0143\u0145\u0147\u014A\u014C\u014E\u0150\u0152\u0154\u0156\u0158"
|
||||
r"\u015A\u015C\u015E\u0160\u0162\u0164\u0166\u0168\u016A\u016C\u016E\u0170\u0172\u0174\u0176"
|
||||
r"\u0178\u0179\u017B\u017D"
|
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)
|
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_latin_l_extendedA = (
|
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r"\u0101\u0103\u0105\u0107\u0109\u010B\u010D\u010F\u0111\u0113\u0115\u0117\u0119\u011B\u011D"
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r"\u011F\u0121\u0123\u0125\u0127\u0129\u012B\u012D\u012F\u0131\u0133\u0135\u0137\u0138\u013A"
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r"\u013C\u013E\u0140\u0142\u0144\u0146\u0148\u0149\u014B\u014D\u014F\u0151\u0153\u0155\u0157"
|
||||
r"\u0159\u015B\u015D\u015F\u0161\u0163\u0165\u0167\u0169\u016B\u016D\u016F\u0171\u0173\u0175"
|
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r"\u0177\u017A\u017C\u017E\u017F"
|
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)
|
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_latin_extendedA = r"\u0100-\u017F"
|
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|
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# special characters - Khoisan, Pan-Nigerian, Pinyin, Romanian
|
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# those that are a combination of both upper and lower letters are only included in the group _latin_extendedB
|
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_latin_u_extendedB = r"\u0181\u0182\u0184\u0186\u0187\u0189-\u018B\u018E-\u0191\u0193\u0194\u0196-\u0198\u019C" \
|
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r"\u019D\u019F\u01A0\u01A2\u01A4\u01A6\u01A7\u01A9\u01AC\u01AE\u01AF\u01B1-\u01B3\u01B5" \
|
||||
r"\u01B7\u01B8\u01BC\u01C4\u01C7\u01CA\u01CD\u01CF\u01D1\u01D3\u01D5\u01D7\u01D9\u01DB" \
|
||||
r"\u01DE\u01E0\u01E2\u01E4\u01E6\u01E8\u01EA\u01EC\u01EE\u01F1\u01F4\u01F6-\u01F8\u01FA" \
|
||||
r"\u01FC\u01FE\u0200\u0202\u0204\u0206\u0208\u020A\u020C\u020E\u0210\u0212\u0214\u0216" \
|
||||
r"\u0218\u021A\u021C\u021E\u0220\u0222\u0224\u0226\u0228\u022A\u022C\u022E\u0230\u0232" \
|
||||
r"\u023A\u023B\u023D\u023E\u0241\u0243-\u0246\u0248\u024A\u024C\u024E"
|
||||
_latin_l_extendedB = r"\u0180\u0183\u0185\u0188\u018C\u018D\u0192\u0195\u0199-\u019B\u019E\u01A1\u01A3\u01A5" \
|
||||
r"\u01A8\u01AA\u01AB\u01AD\u01B0\u01B4\u01B6\u01B9\u01BA\u01BD-\u01BF\u01C6\u01C9\u01CC" \
|
||||
r"\u01CE\u01D0\u01D2\u01D4\u01D6\u01D8\u01DA\u01DC\u01DD\u01DF\u01E1\u01E3\u01E5\u01E7" \
|
||||
r"\u01E9\u01EB\u01ED\u01EF\u01F0\u01F3\u01F5\u01F9\u01FB\u01FD\u01FF\u0201\u0203\u0205" \
|
||||
r"\u0207\u0209\u020B\u020D\u020F\u0211\u0213\u0215\u0217\u0219\u021B\u021D\u021F\u0221" \
|
||||
r"\u0223\u0225\u0227\u0229\u022B\u022D\u022F\u0231\u0233-\u0239\u023C\u023F\u0240\u0242" \
|
||||
r"\u0247\u0249\u024B\u024D\u024F"
|
||||
_latin_u_extendedB = (
|
||||
r"\u0181\u0182\u0184\u0186\u0187\u0189-\u018B\u018E-\u0191\u0193\u0194\u0196-\u0198\u019C"
|
||||
r"\u019D\u019F\u01A0\u01A2\u01A4\u01A6\u01A7\u01A9\u01AC\u01AE\u01AF\u01B1-\u01B3\u01B5"
|
||||
r"\u01B7\u01B8\u01BC\u01C4\u01C7\u01CA\u01CD\u01CF\u01D1\u01D3\u01D5\u01D7\u01D9\u01DB"
|
||||
r"\u01DE\u01E0\u01E2\u01E4\u01E6\u01E8\u01EA\u01EC\u01EE\u01F1\u01F4\u01F6-\u01F8\u01FA"
|
||||
r"\u01FC\u01FE\u0200\u0202\u0204\u0206\u0208\u020A\u020C\u020E\u0210\u0212\u0214\u0216"
|
||||
r"\u0218\u021A\u021C\u021E\u0220\u0222\u0224\u0226\u0228\u022A\u022C\u022E\u0230\u0232"
|
||||
r"\u023A\u023B\u023D\u023E\u0241\u0243-\u0246\u0248\u024A\u024C\u024E"
|
||||
)
|
||||
_latin_l_extendedB = (
|
||||
r"\u0180\u0183\u0185\u0188\u018C\u018D\u0192\u0195\u0199-\u019B\u019E\u01A1\u01A3\u01A5"
|
||||
r"\u01A8\u01AA\u01AB\u01AD\u01B0\u01B4\u01B6\u01B9\u01BA\u01BD-\u01BF\u01C6\u01C9\u01CC"
|
||||
r"\u01CE\u01D0\u01D2\u01D4\u01D6\u01D8\u01DA\u01DC\u01DD\u01DF\u01E1\u01E3\u01E5\u01E7"
|
||||
r"\u01E9\u01EB\u01ED\u01EF\u01F0\u01F3\u01F5\u01F9\u01FB\u01FD\u01FF\u0201\u0203\u0205"
|
||||
r"\u0207\u0209\u020B\u020D\u020F\u0211\u0213\u0215\u0217\u0219\u021B\u021D\u021F\u0221"
|
||||
r"\u0223\u0225\u0227\u0229\u022B\u022D\u022F\u0231\u0233-\u0239\u023C\u023F\u0240\u0242"
|
||||
r"\u0247\u0249\u024B\u024D\u024F"
|
||||
)
|
||||
_latin_extendedB = r"\u0180-\u01BF\u01C4-\u024F"
|
||||
|
||||
# special characters - Uighur, Uralic Phonetic
|
||||
_latin_u_extendedC = r"\u2C60\u2C62-\u2C64\u2C67\u2C69\u2C6B\u2C6D-\u2C70\u2C72\u2C75\u2C7E\u2C7F"
|
||||
_latin_l_extendedC = r"\u2C61\u2C65\u2C66\u2C68\u2C6A\u2C6C\u2C71\u2C73\u2C74\u2C76-\u2C7B"
|
||||
_latin_u_extendedC = (
|
||||
r"\u2C60\u2C62-\u2C64\u2C67\u2C69\u2C6B\u2C6D-\u2C70\u2C72\u2C75\u2C7E\u2C7F"
|
||||
)
|
||||
_latin_l_extendedC = (
|
||||
r"\u2C61\u2C65\u2C66\u2C68\u2C6A\u2C6C\u2C71\u2C73\u2C74\u2C76-\u2C7B"
|
||||
)
|
||||
_latin_extendedC = r"\u2C60-\u2C7B\u2C7E\u2C7F"
|
||||
|
||||
# special characters - phonetic, Mayan, Medieval
|
||||
_latin_u_extendedD = r"\uA722\uA724\uA726\uA728\uA72A\uA72C\uA72E\uA732\uA734\uA736\uA738\uA73A\uA73C" \
|
||||
r"\uA73E\uA740\uA742\uA744\uA746\uA748\uA74A\uA74C\uA74E\uA750\uA752\uA754\uA756\uA758" \
|
||||
r"\uA75A\uA75C\uA75E\uA760\uA762\uA764\uA766\uA768\uA76A\uA76C\uA76E\uA779\uA77B\uA77D" \
|
||||
r"\uA77E\uA780\uA782\uA784\uA786\uA78B\uA78D\uA790\uA792\uA796\uA798\uA79A\uA79C\uA79E" \
|
||||
r"\uA7A0\uA7A2\uA7A4\uA7A6\uA7A8\uA7AA-\uA7AE\uA7B0-\uA7B4\uA7B6\uA7B8"
|
||||
_latin_l_extendedD = r"\uA723\uA725\uA727\uA729\uA72B\uA72D\uA72F-\uA731\uA733\uA735\uA737\uA739\uA73B\uA73D" \
|
||||
r"\uA73F\uA741\uA743\uA745\uA747\uA749\uA74B\uA74D\uA74F\uA751\uA753\uA755\uA757\uA759" \
|
||||
r"\uA75B\uA75D\uA75F\uA761\uA763\uA765\uA767\uA769\uA76B\uA76D\uA76F\uA771-\uA778\uA77A" \
|
||||
r"\uA77C\uA77F\uA781\uA783\uA785\uA787\uA78C\uA78E\uA791\uA793-\uA795\uA797\uA799\uA79B" \
|
||||
r"\uA79D\uA79F\uA7A1\uA7A3\uA7A5\uA7A7\uA7A9\uA7AF\uA7B5\uA7B7\uA7B9\uA7FA"
|
||||
_latin_u_extendedD = (
|
||||
r"\uA722\uA724\uA726\uA728\uA72A\uA72C\uA72E\uA732\uA734\uA736\uA738\uA73A\uA73C"
|
||||
r"\uA73E\uA740\uA742\uA744\uA746\uA748\uA74A\uA74C\uA74E\uA750\uA752\uA754\uA756\uA758"
|
||||
r"\uA75A\uA75C\uA75E\uA760\uA762\uA764\uA766\uA768\uA76A\uA76C\uA76E\uA779\uA77B\uA77D"
|
||||
r"\uA77E\uA780\uA782\uA784\uA786\uA78B\uA78D\uA790\uA792\uA796\uA798\uA79A\uA79C\uA79E"
|
||||
r"\uA7A0\uA7A2\uA7A4\uA7A6\uA7A8\uA7AA-\uA7AE\uA7B0-\uA7B4\uA7B6\uA7B8"
|
||||
)
|
||||
_latin_l_extendedD = (
|
||||
r"\uA723\uA725\uA727\uA729\uA72B\uA72D\uA72F-\uA731\uA733\uA735\uA737\uA739\uA73B\uA73D"
|
||||
r"\uA73F\uA741\uA743\uA745\uA747\uA749\uA74B\uA74D\uA74F\uA751\uA753\uA755\uA757\uA759"
|
||||
r"\uA75B\uA75D\uA75F\uA761\uA763\uA765\uA767\uA769\uA76B\uA76D\uA76F\uA771-\uA778\uA77A"
|
||||
r"\uA77C\uA77F\uA781\uA783\uA785\uA787\uA78C\uA78E\uA791\uA793-\uA795\uA797\uA799\uA79B"
|
||||
r"\uA79D\uA79F\uA7A1\uA7A3\uA7A5\uA7A7\uA7A9\uA7AF\uA7B5\uA7B7\uA7B9\uA7FA"
|
||||
)
|
||||
_latin_extendedD = r"\uA722-\uA76F\uA771-\uA787\uA78B-\uA78E\uA790-\uA7B9\uA7FA"
|
||||
|
||||
# special characters - phonetic Teuthonista and Sakha
|
||||
|
|
@ -81,42 +97,80 @@ _latin_l_phonetic = r"\u0250-\u02AF\u1D00-\u1D25\u1D6B-\u1D77\u1D79-\u1D9A"
|
|||
_latin_phonetic = _latin_l_phonetic
|
||||
|
||||
# letters with multiple diacritics - Vietnamese
|
||||
_latin_u_diacritics = r"\u1E00\u1E02\u1E04\u1E06\u1E08\u1E0A\u1E0C\u1E0E\u1E10\u1E12\u1E14\u1E16\u1E18\u1E1A" \
|
||||
r"\u1E1C\u1E1E\u1E20\u1E22\u1E24\u1E26\u1E28\u1E2A\u1E2C\u1E2E\u1E30\u1E32\u1E34\u1E36" \
|
||||
r"\u1E38\u1E3A\u1E3C\u1E3E\u1E40\u1E42\u1E44\u1E46\u1E48\u1E4A\u1E4C\u1E4E\u1E50\u1E52" \
|
||||
r"\u1E54\u1E56\u1E58\u1E5A\u1E5C\u1E5E\u1E60\u1E62\u1E64\u1E66\u1E68\u1E6A\u1E6C\u1E6E" \
|
||||
r"\u1E70\u1E72\u1E74\u1E76\u1E78\u1E7A\u1E7C\u1E7E\u1E80\u1E82\u1E84\u1E86\u1E88\u1E8A" \
|
||||
r"\u1E8C\u1E8E\u1E90\u1E92\u1E94\u1E9E\u1EA0\u1EA2\u1EA4\u1EA6\u1EA8\u1EAA\u1EAC\u1EAE" \
|
||||
r"\u1EB0\u1EB2\u1EB4\u1EB6\u1EB8\u1EBA\u1EBC\u1EBE\u1EC0\u1EC2\u1EC4\u1EC6\u1EC8" \
|
||||
r"\u1ECA\u1ECC\u1ECE\u1ED0\u1ED2\u1ED4\u1ED6\u1ED8\u1EDA\u1EDC\u1EDE\u1EE0\u1EE2\u1EE4" \
|
||||
r"\u1EE6\u1EE8\u1EEA\u1EEC\u1EEE\u1EF0\u1EF2\u1EF4\u1EF6\u1EF8\u1EFA\u1EFC\u1EFE"
|
||||
_latin_l_diacritics = r"\u1E01\u1E03\u1E05\u1E07\u1E09\u1E0B\u1E0D\u1E0F\u1E11\u1E13\u1E15\u1E17\u1E19\u1E1B" \
|
||||
r"\u1E1D\u1E1F\u1E21\u1E23\u1E25\u1E27\u1E29\u1E2B\u1E2D\u1E2F\u1E31\u1E33\u1E35\u1E37" \
|
||||
r"\u1E39\u1E3B\u1E3D\u1E3F\u1E41\u1E43\u1E45\u1E47\u1E49\u1E4B\u1E4D\u1E4F\u1E51\u1E53" \
|
||||
r"\u1E55\u1E57\u1E59\u1E5B\u1E5D\u1E5F\u1E61\u1E63\u1E65\u1E67\u1E69\u1E6B\u1E6D\u1E6F" \
|
||||
r"\u1E71\u1E73\u1E75\u1E77\u1E79\u1E7B\u1E7D\u1E7F\u1E81\u1E83\u1E85\u1E87\u1E89\u1E8B" \
|
||||
r"\u1E8D\u1E8F\u1E91\u1E93\u1E95-\u1E9D\u1E9F\u1EA1\u1EA3\u1EA5\u1EA7\u1EA9\u1EAB\u1EAD" \
|
||||
r"\u1EAF\u1EB1\u1EB3\u1EB5\u1EB7\u1EB9\u1EBB\u1EBD\u1EBF\u1EC1\u1EC3\u1EC5\u1EC7\u1EC9" \
|
||||
r"\u1ECB\u1ECD\u1ECF\u1ED1\u1ED3\u1ED5\u1ED7\u1ED9\u1EDB\u1EDD\u1EDF\u1EE1\u1EE3\u1EE5" \
|
||||
r"\u1EE7\u1EE9\u1EEB\u1EED\u1EEF\u1EF1\u1EF3\u1EF5\u1EF7\u1EF9\u1EFB\u1EFD\u1EFF"
|
||||
_latin_u_diacritics = (
|
||||
r"\u1E00\u1E02\u1E04\u1E06\u1E08\u1E0A\u1E0C\u1E0E\u1E10\u1E12\u1E14\u1E16\u1E18\u1E1A"
|
||||
r"\u1E1C\u1E1E\u1E20\u1E22\u1E24\u1E26\u1E28\u1E2A\u1E2C\u1E2E\u1E30\u1E32\u1E34\u1E36"
|
||||
r"\u1E38\u1E3A\u1E3C\u1E3E\u1E40\u1E42\u1E44\u1E46\u1E48\u1E4A\u1E4C\u1E4E\u1E50\u1E52"
|
||||
r"\u1E54\u1E56\u1E58\u1E5A\u1E5C\u1E5E\u1E60\u1E62\u1E64\u1E66\u1E68\u1E6A\u1E6C\u1E6E"
|
||||
r"\u1E70\u1E72\u1E74\u1E76\u1E78\u1E7A\u1E7C\u1E7E\u1E80\u1E82\u1E84\u1E86\u1E88\u1E8A"
|
||||
r"\u1E8C\u1E8E\u1E90\u1E92\u1E94\u1E9E\u1EA0\u1EA2\u1EA4\u1EA6\u1EA8\u1EAA\u1EAC\u1EAE"
|
||||
r"\u1EB0\u1EB2\u1EB4\u1EB6\u1EB8\u1EBA\u1EBC\u1EBE\u1EC0\u1EC2\u1EC4\u1EC6\u1EC8"
|
||||
r"\u1ECA\u1ECC\u1ECE\u1ED0\u1ED2\u1ED4\u1ED6\u1ED8\u1EDA\u1EDC\u1EDE\u1EE0\u1EE2\u1EE4"
|
||||
r"\u1EE6\u1EE8\u1EEA\u1EEC\u1EEE\u1EF0\u1EF2\u1EF4\u1EF6\u1EF8\u1EFA\u1EFC\u1EFE"
|
||||
)
|
||||
_latin_l_diacritics = (
|
||||
r"\u1E01\u1E03\u1E05\u1E07\u1E09\u1E0B\u1E0D\u1E0F\u1E11\u1E13\u1E15\u1E17\u1E19\u1E1B"
|
||||
r"\u1E1D\u1E1F\u1E21\u1E23\u1E25\u1E27\u1E29\u1E2B\u1E2D\u1E2F\u1E31\u1E33\u1E35\u1E37"
|
||||
r"\u1E39\u1E3B\u1E3D\u1E3F\u1E41\u1E43\u1E45\u1E47\u1E49\u1E4B\u1E4D\u1E4F\u1E51\u1E53"
|
||||
r"\u1E55\u1E57\u1E59\u1E5B\u1E5D\u1E5F\u1E61\u1E63\u1E65\u1E67\u1E69\u1E6B\u1E6D\u1E6F"
|
||||
r"\u1E71\u1E73\u1E75\u1E77\u1E79\u1E7B\u1E7D\u1E7F\u1E81\u1E83\u1E85\u1E87\u1E89\u1E8B"
|
||||
r"\u1E8D\u1E8F\u1E91\u1E93\u1E95-\u1E9D\u1E9F\u1EA1\u1EA3\u1EA5\u1EA7\u1EA9\u1EAB\u1EAD"
|
||||
r"\u1EAF\u1EB1\u1EB3\u1EB5\u1EB7\u1EB9\u1EBB\u1EBD\u1EBF\u1EC1\u1EC3\u1EC5\u1EC7\u1EC9"
|
||||
r"\u1ECB\u1ECD\u1ECF\u1ED1\u1ED3\u1ED5\u1ED7\u1ED9\u1EDB\u1EDD\u1EDF\u1EE1\u1EE3\u1EE5"
|
||||
r"\u1EE7\u1EE9\u1EEB\u1EED\u1EEF\u1EF1\u1EF3\u1EF5\u1EF7\u1EF9\u1EFB\u1EFD\u1EFF"
|
||||
)
|
||||
_latin_diacritics = r"\u1E00-\u1EFF"
|
||||
|
||||
# all lower latin classes
|
||||
LATIN_LOWER_BASIC = _latin_l_standard + _latin_l_standard_fullwidth + _latin_l_supplement + _latin_l_extendedA
|
||||
LATIN_LOWER = LATIN_LOWER_BASIC + _latin_l_extendedB + _latin_l_extendedC + _latin_l_extendedD + _latin_l_extendedE \
|
||||
+ _latin_l_phonetic + _latin_l_diacritics
|
||||
LATIN_LOWER_BASIC = (
|
||||
_latin_l_standard
|
||||
+ _latin_l_standard_fullwidth
|
||||
+ _latin_l_supplement
|
||||
+ _latin_l_extendedA
|
||||
)
|
||||
LATIN_LOWER = (
|
||||
LATIN_LOWER_BASIC
|
||||
+ _latin_l_extendedB
|
||||
+ _latin_l_extendedC
|
||||
+ _latin_l_extendedD
|
||||
+ _latin_l_extendedE
|
||||
+ _latin_l_phonetic
|
||||
+ _latin_l_diacritics
|
||||
)
|
||||
|
||||
# all upper latin classes
|
||||
LATIN_UPPER_BASIC = _latin_u_standard + _latin_u_standard_fullwidth + _latin_u_supplement + _latin_u_extendedA
|
||||
LATIN_UPPER = LATIN_UPPER_BASIC + _latin_u_extendedB + _latin_u_extendedC + _latin_u_extendedD + _latin_u_diacritics
|
||||
LATIN_UPPER_BASIC = (
|
||||
_latin_u_standard
|
||||
+ _latin_u_standard_fullwidth
|
||||
+ _latin_u_supplement
|
||||
+ _latin_u_extendedA
|
||||
)
|
||||
LATIN_UPPER = (
|
||||
LATIN_UPPER_BASIC
|
||||
+ _latin_u_extendedB
|
||||
+ _latin_u_extendedC
|
||||
+ _latin_u_extendedD
|
||||
+ _latin_u_diacritics
|
||||
)
|
||||
|
||||
# all latin classes
|
||||
LATIN_BASIC = _latin_standard + _latin_standard_fullwidth + _latin_supplement + _latin_extendedA
|
||||
LATIN = LATIN_BASIC + _latin_extendedB + _latin_extendedC + _latin_extendedD + _latin_extendedE \
|
||||
+ _latin_phonetic + _latin_diacritics
|
||||
LATIN_BASIC = (
|
||||
_latin_standard + _latin_standard_fullwidth + _latin_supplement + _latin_extendedA
|
||||
)
|
||||
LATIN = (
|
||||
LATIN_BASIC
|
||||
+ _latin_extendedB
|
||||
+ _latin_extendedC
|
||||
+ _latin_extendedD
|
||||
+ _latin_extendedE
|
||||
+ _latin_phonetic
|
||||
+ _latin_diacritics
|
||||
)
|
||||
|
||||
_persian = r"\u0620-\u064A\u066E-\u06D5\u06E5-\u06FF\u0750-\u077F\u08A0-\u08BD" \
|
||||
r"\uFB50-\uFBB1\uFBD3-\uFD3D\uFD50-\uFDC7\uFDF0-\uFDFB\uFE70-\uFEFC\U0001EE00-\U0001EEBB"
|
||||
_persian = (
|
||||
r"\u0620-\u064A\u066E-\u06D5\u06E5-\u06FF\u0750-\u077F\u08A0-\u08BD"
|
||||
r"\uFB50-\uFBB1\uFBD3-\uFD3D\uFD50-\uFDC7\uFDF0-\uFDFB\uFE70-\uFEFC\U0001EE00-\U0001EEBB"
|
||||
)
|
||||
|
||||
_russian_lower = r"ёа-я"
|
||||
_russian_upper = r"ЁА-Я"
|
||||
|
|
@ -165,33 +219,35 @@ _hyphens = "- – — -- --- —— ~"
|
|||
|
||||
# Various symbols like dingbats, but also emoji
|
||||
# Details: https://www.compart.com/en/unicode/category/So
|
||||
_other_symbols = r"\u00A6\u00A9\u00AE\u00B0\u0482\u058D\u058E\u060E\u060F\u06DE\u06E9\u06FD\u06FE\u07F6\u09FA\u0B70" \
|
||||
r"\u0BF3-\u0BF8\u0BFA\u0C7F\u0D4F\u0D79\u0F01-\u0F03\u0F13\u0F15-\u0F17\u0F1A-\u0F1F\u0F34" \
|
||||
r"\u0F36\u0F38\u0FBE-\u0FC5\u0FC7-\u0FCC\u0FCE\u0FCF\u0FD5-\u0FD8\u109E\u109F\u1390-\u1399" \
|
||||
r"\u1940\u19DE-\u19FF\u1B61-\u1B6A\u1B74-\u1B7C\u2100\u2101\u2103-\u2106\u2108\u2109\u2114\u2116" \
|
||||
r"\u2117\u211E-\u2123\u2125\u2127\u2129\u212E\u213A\u213B\u214A\u214C\u214D\u214F\u218A\u218B" \
|
||||
r"\u2195-\u2199\u219C-\u219F\u21A1\u21A2\u21A4\u21A5\u21A7-\u21AD\u21AF-\u21CD\u21D0\u21D1\u21D3" \
|
||||
r"\u21D5-\u21F3\u2300-\u2307\u230C-\u231F\u2322-\u2328\u232B-\u237B\u237D-\u239A\u23B4-\u23DB" \
|
||||
r"\u23E2-\u2426\u2440-\u244A\u249C-\u24E9\u2500-\u25B6\u25B8-\u25C0\u25C2-\u25F7\u2600-\u266E" \
|
||||
r"\u2670-\u2767\u2794-\u27BF\u2800-\u28FF\u2B00-\u2B2F\u2B45\u2B46\u2B4D-\u2B73\u2B76-\u2B95" \
|
||||
r"\u2B98-\u2BC8\u2BCA-\u2BFE\u2CE5-\u2CEA\u2E80-\u2E99\u2E9B-\u2EF3\u2F00-\u2FD5\u2FF0-\u2FFB" \
|
||||
r"\u3004\u3012\u3013\u3020\u3036\u3037\u303E\u303F\u3190\u3191\u3196-\u319F\u31C0-\u31E3" \
|
||||
r"\u3200-\u321E\u322A-\u3247\u3250\u3260-\u327F\u328A-\u32B0\u32C0-\u32FE\u3300-\u33FF\u4DC0-\u4DFF" \
|
||||
r"\uA490-\uA4C6\uA828-\uA82B\uA836\uA837\uA839\uAA77-\uAA79\uFDFD\uFFE4\uFFE8\uFFED\uFFEE\uFFFC" \
|
||||
r"\uFFFD\U00010137-\U0001013F\U00010179-\U00010189\U0001018C-\U0001018E\U00010190-\U0001019B" \
|
||||
r"\U000101A0\U000101D0-\U000101FC\U00010877\U00010878\U00010AC8\U0001173F\U00016B3C-\U00016B3F" \
|
||||
r"\U00016B45\U0001BC9C\U0001D000-\U0001D0F5\U0001D100-\U0001D126\U0001D129-\U0001D164" \
|
||||
r"\U0001D16A-\U0001D16C\U0001D183\U0001D184\U0001D18C-\U0001D1A9\U0001D1AE-\U0001D1E8" \
|
||||
r"\U0001D200-\U0001D241\U0001D245\U0001D300-\U0001D356\U0001D800-\U0001D9FF\U0001DA37-\U0001DA3A" \
|
||||
r"\U0001DA6D-\U0001DA74\U0001DA76-\U0001DA83\U0001DA85\U0001DA86\U0001ECAC\U0001F000-\U0001F02B" \
|
||||
r"\U0001F030-\U0001F093\U0001F0A0-\U0001F0AE\U0001F0B1-\U0001F0BF\U0001F0C1-\U0001F0CF" \
|
||||
r"\U0001F0D1-\U0001F0F5\U0001F110-\U0001F16B\U0001F170-\U0001F1AC\U0001F1E6-\U0001F202" \
|
||||
r"\U0001F210-\U0001F23B\U0001F240-\U0001F248\U0001F250\U0001F251\U0001F260-\U0001F265" \
|
||||
r"\U0001F300-\U0001F3FA\U0001F400-\U0001F6D4\U0001F6E0-\U0001F6EC\U0001F6F0-\U0001F6F9" \
|
||||
r"\U0001F700-\U0001F773\U0001F780-\U0001F7D8\U0001F800-\U0001F80B\U0001F810-\U0001F847" \
|
||||
r"\U0001F850-\U0001F859\U0001F860-\U0001F887\U0001F890-\U0001F8AD\U0001F900-\U0001F90B" \
|
||||
r"\U0001F910-\U0001F93E\U0001F940-\U0001F970\U0001F973-\U0001F976\U0001F97A\U0001F97C-\U0001F9A2" \
|
||||
r"\U0001F9B0-\U0001F9B9\U0001F9C0-\U0001F9C2\U0001F9D0-\U0001F9FF\U0001FA60-\U0001FA6D"
|
||||
_other_symbols = (
|
||||
r"\u00A6\u00A9\u00AE\u00B0\u0482\u058D\u058E\u060E\u060F\u06DE\u06E9\u06FD\u06FE\u07F6\u09FA\u0B70"
|
||||
r"\u0BF3-\u0BF8\u0BFA\u0C7F\u0D4F\u0D79\u0F01-\u0F03\u0F13\u0F15-\u0F17\u0F1A-\u0F1F\u0F34"
|
||||
r"\u0F36\u0F38\u0FBE-\u0FC5\u0FC7-\u0FCC\u0FCE\u0FCF\u0FD5-\u0FD8\u109E\u109F\u1390-\u1399"
|
||||
r"\u1940\u19DE-\u19FF\u1B61-\u1B6A\u1B74-\u1B7C\u2100\u2101\u2103-\u2106\u2108\u2109\u2114\u2116"
|
||||
r"\u2117\u211E-\u2123\u2125\u2127\u2129\u212E\u213A\u213B\u214A\u214C\u214D\u214F\u218A\u218B"
|
||||
r"\u2195-\u2199\u219C-\u219F\u21A1\u21A2\u21A4\u21A5\u21A7-\u21AD\u21AF-\u21CD\u21D0\u21D1\u21D3"
|
||||
r"\u21D5-\u21F3\u2300-\u2307\u230C-\u231F\u2322-\u2328\u232B-\u237B\u237D-\u239A\u23B4-\u23DB"
|
||||
r"\u23E2-\u2426\u2440-\u244A\u249C-\u24E9\u2500-\u25B6\u25B8-\u25C0\u25C2-\u25F7\u2600-\u266E"
|
||||
r"\u2670-\u2767\u2794-\u27BF\u2800-\u28FF\u2B00-\u2B2F\u2B45\u2B46\u2B4D-\u2B73\u2B76-\u2B95"
|
||||
r"\u2B98-\u2BC8\u2BCA-\u2BFE\u2CE5-\u2CEA\u2E80-\u2E99\u2E9B-\u2EF3\u2F00-\u2FD5\u2FF0-\u2FFB"
|
||||
r"\u3004\u3012\u3013\u3020\u3036\u3037\u303E\u303F\u3190\u3191\u3196-\u319F\u31C0-\u31E3"
|
||||
r"\u3200-\u321E\u322A-\u3247\u3250\u3260-\u327F\u328A-\u32B0\u32C0-\u32FE\u3300-\u33FF\u4DC0-\u4DFF"
|
||||
r"\uA490-\uA4C6\uA828-\uA82B\uA836\uA837\uA839\uAA77-\uAA79\uFDFD\uFFE4\uFFE8\uFFED\uFFEE\uFFFC"
|
||||
r"\uFFFD\U00010137-\U0001013F\U00010179-\U00010189\U0001018C-\U0001018E\U00010190-\U0001019B"
|
||||
r"\U000101A0\U000101D0-\U000101FC\U00010877\U00010878\U00010AC8\U0001173F\U00016B3C-\U00016B3F"
|
||||
r"\U00016B45\U0001BC9C\U0001D000-\U0001D0F5\U0001D100-\U0001D126\U0001D129-\U0001D164"
|
||||
r"\U0001D16A-\U0001D16C\U0001D183\U0001D184\U0001D18C-\U0001D1A9\U0001D1AE-\U0001D1E8"
|
||||
r"\U0001D200-\U0001D241\U0001D245\U0001D300-\U0001D356\U0001D800-\U0001D9FF\U0001DA37-\U0001DA3A"
|
||||
r"\U0001DA6D-\U0001DA74\U0001DA76-\U0001DA83\U0001DA85\U0001DA86\U0001ECAC\U0001F000-\U0001F02B"
|
||||
r"\U0001F030-\U0001F093\U0001F0A0-\U0001F0AE\U0001F0B1-\U0001F0BF\U0001F0C1-\U0001F0CF"
|
||||
r"\U0001F0D1-\U0001F0F5\U0001F110-\U0001F16B\U0001F170-\U0001F1AC\U0001F1E6-\U0001F202"
|
||||
r"\U0001F210-\U0001F23B\U0001F240-\U0001F248\U0001F250\U0001F251\U0001F260-\U0001F265"
|
||||
r"\U0001F300-\U0001F3FA\U0001F400-\U0001F6D4\U0001F6E0-\U0001F6EC\U0001F6F0-\U0001F6F9"
|
||||
r"\U0001F700-\U0001F773\U0001F780-\U0001F7D8\U0001F800-\U0001F80B\U0001F810-\U0001F847"
|
||||
r"\U0001F850-\U0001F859\U0001F860-\U0001F887\U0001F890-\U0001F8AD\U0001F900-\U0001F90B"
|
||||
r"\U0001F910-\U0001F93E\U0001F940-\U0001F970\U0001F973-\U0001F976\U0001F97A\U0001F97C-\U0001F9A2"
|
||||
r"\U0001F9B0-\U0001F9B9\U0001F9C0-\U0001F9C2\U0001F9D0-\U0001F9FF\U0001FA60-\U0001FA6D"
|
||||
)
|
||||
|
||||
UNITS = merge_chars(_units)
|
||||
CURRENCY = merge_chars(_currency)
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ cdef struct WeightsC:
|
|||
const float* feat_bias
|
||||
const float* hidden_bias
|
||||
const float* hidden_weights
|
||||
const float* seen_classes
|
||||
|
||||
|
||||
cdef struct ActivationsC:
|
||||
|
|
|
|||
|
|
@ -44,8 +44,10 @@ cdef WeightsC get_c_weights(model) except *:
|
|||
output.feat_bias = <const float*>state2vec.bias.data
|
||||
cdef np.ndarray vec2scores_W = model.vec2scores.W
|
||||
cdef np.ndarray vec2scores_b = model.vec2scores.b
|
||||
cdef np.ndarray class_mask = model._class_mask
|
||||
output.hidden_weights = <const float*>vec2scores_W.data
|
||||
output.hidden_bias = <const float*>vec2scores_b.data
|
||||
output.seen_classes = <const float*>class_mask.data
|
||||
return output
|
||||
|
||||
|
||||
|
|
@ -115,6 +117,16 @@ cdef void predict_states(ActivationsC* A, StateC** states,
|
|||
for i in range(n.states):
|
||||
VecVec.add_i(&A.scores[i*n.classes],
|
||||
W.hidden_bias, 1., n.classes)
|
||||
# Set unseen classes to minimum value
|
||||
i = 0
|
||||
min_ = A.scores[0]
|
||||
for i in range(1, n.states * n.classes):
|
||||
if A.scores[i] < min_:
|
||||
min_ = A.scores[i]
|
||||
for i in range(n.states):
|
||||
for j in range(n.classes):
|
||||
if not W.seen_classes[j]:
|
||||
A.scores[i*n.classes+j] = min_
|
||||
|
||||
|
||||
cdef void sum_state_features(float* output,
|
||||
|
|
@ -189,12 +201,17 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no
|
|||
|
||||
|
||||
class ParserModel(Model):
|
||||
def __init__(self, tok2vec, lower_model, upper_model):
|
||||
def __init__(self, tok2vec, lower_model, upper_model, unseen_classes=None):
|
||||
Model.__init__(self)
|
||||
self._layers = [tok2vec, lower_model, upper_model]
|
||||
self.unseen_classes = set()
|
||||
if unseen_classes:
|
||||
for class_ in unseen_classes:
|
||||
self.unseen_classes.add(class_)
|
||||
|
||||
def begin_update(self, docs, drop=0.):
|
||||
step_model = ParserStepModel(docs, self._layers, drop=drop)
|
||||
step_model = ParserStepModel(docs, self._layers, drop=drop,
|
||||
unseen_classes=self.unseen_classes)
|
||||
def finish_parser_update(golds, sgd=None):
|
||||
step_model.make_updates(sgd)
|
||||
return None
|
||||
|
|
@ -207,9 +224,8 @@ class ParserModel(Model):
|
|||
|
||||
with Model.use_device('cpu'):
|
||||
larger = Affine(new_output, smaller.nI)
|
||||
# Set nan as value for unseen classes, to prevent prediction.
|
||||
larger.W.fill(self.ops.xp.nan)
|
||||
larger.b.fill(self.ops.xp.nan)
|
||||
larger.W.fill(0.0)
|
||||
larger.b.fill(0.0)
|
||||
# It seems very unhappy if I pass these as smaller.W?
|
||||
# Seems to segfault. Maybe it's a descriptor protocol thing?
|
||||
smaller_W = smaller.W
|
||||
|
|
@ -221,6 +237,8 @@ class ParserModel(Model):
|
|||
larger_W[:smaller.nO] = smaller_W
|
||||
larger_b[:smaller.nO] = smaller_b
|
||||
self._layers[-1] = larger
|
||||
for i in range(smaller.nO, new_output):
|
||||
self.unseen_classes.add(i)
|
||||
|
||||
def begin_training(self, X, y=None):
|
||||
self.lower.begin_training(X, y=y)
|
||||
|
|
@ -239,18 +257,32 @@ class ParserModel(Model):
|
|||
|
||||
|
||||
class ParserStepModel(Model):
|
||||
def __init__(self, docs, layers, drop=0.):
|
||||
def __init__(self, docs, layers, unseen_classes=None, drop=0.):
|
||||
self.tokvecs, self.bp_tokvecs = layers[0].begin_update(docs, drop=drop)
|
||||
self.state2vec = precompute_hiddens(len(docs), self.tokvecs, layers[1],
|
||||
drop=drop)
|
||||
self.vec2scores = layers[-1]
|
||||
self.cuda_stream = util.get_cuda_stream()
|
||||
self.backprops = []
|
||||
self._class_mask = numpy.zeros((self.vec2scores.nO,), dtype='f')
|
||||
self._class_mask.fill(1)
|
||||
if unseen_classes is not None:
|
||||
for class_ in unseen_classes:
|
||||
self._class_mask[class_] = 0.
|
||||
|
||||
@property
|
||||
def nO(self):
|
||||
return self.state2vec.nO
|
||||
|
||||
def class_is_unseen(self, class_):
|
||||
return self._class_mask[class_]
|
||||
|
||||
def mark_class_unseen(self, class_):
|
||||
self._class_mask[class_] = 0
|
||||
|
||||
def mark_class_seen(self, class_):
|
||||
self._class_mask[class_] = 1
|
||||
|
||||
def begin_update(self, states, drop=0.):
|
||||
token_ids = self.get_token_ids(states)
|
||||
vector, get_d_tokvecs = self.state2vec.begin_update(token_ids, drop=0.0)
|
||||
|
|
@ -258,24 +290,12 @@ class ParserStepModel(Model):
|
|||
if mask is not None:
|
||||
vector *= mask
|
||||
scores, get_d_vector = self.vec2scores.begin_update(vector, drop=drop)
|
||||
# We can have nans from unseen classes.
|
||||
# For backprop purposes, we want to treat unseen classes as having the
|
||||
# lowest score.
|
||||
# numpy's nan_to_num function doesn't take a value, and nan is replaced
|
||||
# by 0...-inf is replaced by minimum, so we go via that. Ugly to the max.
|
||||
# Note that scores is always a numpy array! Should fix #3112
|
||||
scores[numpy.isnan(scores)] = -numpy.inf
|
||||
numpy.nan_to_num(scores, copy=False)
|
||||
# If the class is unseen, make sure its score is minimum
|
||||
scores[:, self._class_mask == 0] = numpy.nanmin(scores)
|
||||
|
||||
def backprop_parser_step(d_scores, sgd=None):
|
||||
# If we have a non-zero gradient for a previously unseen class,
|
||||
# replace the weight with 0.
|
||||
new_classes = self.vec2scores.ops.xp.logical_and(
|
||||
self.vec2scores.ops.xp.isnan(self.vec2scores.b),
|
||||
d_scores.any(axis=0)
|
||||
)
|
||||
self.vec2scores.b[new_classes] = 0.
|
||||
self.vec2scores.W[new_classes] = 0.
|
||||
# Zero vectors for unseen classes
|
||||
d_scores *= self._class_mask
|
||||
d_vector = get_d_vector(d_scores, sgd=sgd)
|
||||
if mask is not None:
|
||||
d_vector *= mask
|
||||
|
|
|
|||
|
|
@ -163,6 +163,8 @@ cdef class Parser:
|
|||
added = self.moves.add_action(action, label)
|
||||
if added:
|
||||
resized = True
|
||||
if resized and "nr_class" in self.cfg:
|
||||
self.cfg["nr_class"] = self.moves.n_moves
|
||||
if self.model not in (True, False, None) and resized:
|
||||
self.model.resize_output(self.moves.n_moves)
|
||||
|
||||
|
|
@ -435,22 +437,22 @@ cdef class Parser:
|
|||
if self._rehearsal_model is None:
|
||||
return None
|
||||
losses.setdefault(self.name, 0.)
|
||||
|
||||
states = self.moves.init_batch(docs)
|
||||
# This is pretty dirty, but the NER can resize itself in init_batch,
|
||||
# if labels are missing. We therefore have to check whether we need to
|
||||
# expand our model output.
|
||||
self.model.resize_output(self.moves.n_moves)
|
||||
self._rehearsal_model.resize_output(self.moves.n_moves)
|
||||
# Prepare the stepwise model, and get the callback for finishing the batch
|
||||
tutor = self._rehearsal_model(docs)
|
||||
tutor, _ = self._rehearsal_model.begin_update(docs, drop=0.0)
|
||||
model, finish_update = self.model.begin_update(docs, drop=0.0)
|
||||
n_scores = 0.
|
||||
loss = 0.
|
||||
non_zeroed_classes = self._rehearsal_model.upper.W.any(axis=1)
|
||||
while states:
|
||||
targets, _ = tutor.begin_update(states)
|
||||
guesses, backprop = model.begin_update(states)
|
||||
d_scores = (targets - guesses) / targets.shape[0]
|
||||
d_scores *= non_zeroed_classes
|
||||
targets, _ = tutor.begin_update(states, drop=0.)
|
||||
guesses, backprop = model.begin_update(states, drop=0.)
|
||||
d_scores = (guesses - targets) / targets.shape[0]
|
||||
# If all weights for an output are 0 in the original model, don't
|
||||
# supervise that output. This allows us to add classes.
|
||||
loss += (d_scores**2).sum()
|
||||
|
|
@ -543,6 +545,9 @@ cdef class Parser:
|
|||
memset(is_valid, 0, self.moves.n_moves * sizeof(int))
|
||||
memset(costs, 0, self.moves.n_moves * sizeof(float))
|
||||
self.moves.set_costs(is_valid, costs, state, gold)
|
||||
for j in range(self.moves.n_moves):
|
||||
if costs[j] <= 0.0 and j in self.model.unseen_classes:
|
||||
self.model.unseen_classes.remove(j)
|
||||
cpu_log_loss(c_d_scores,
|
||||
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
||||
c_d_scores += d_scores.shape[1]
|
||||
|
|
|
|||
|
|
@ -147,6 +147,8 @@ cdef class TransitionSystem:
|
|||
def initialize_actions(self, labels_by_action, min_freq=None):
|
||||
self.labels = {}
|
||||
self.n_moves = 0
|
||||
added_labels = []
|
||||
added_actions = {}
|
||||
for action, label_freqs in sorted(labels_by_action.items()):
|
||||
action = int(action)
|
||||
# Make sure we take a copy here, and that we get a Counter
|
||||
|
|
@ -157,6 +159,15 @@ cdef class TransitionSystem:
|
|||
sorted_labels.sort()
|
||||
sorted_labels.reverse()
|
||||
for freq, label_str in sorted_labels:
|
||||
if freq < 0:
|
||||
added_labels.append((freq, label_str))
|
||||
added_actions.setdefault(label_str, []).append(action)
|
||||
else:
|
||||
self.add_action(int(action), label_str)
|
||||
self.labels[action][label_str] = freq
|
||||
added_labels.sort(reverse=True)
|
||||
for freq, label_str in added_labels:
|
||||
for action in added_actions[label_str]:
|
||||
self.add_action(int(action), label_str)
|
||||
self.labels[action][label_str] = freq
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ import pytest
|
|||
import numpy
|
||||
from spacy.tokens import Doc
|
||||
from spacy.vocab import Vocab
|
||||
from spacy.attrs import LEMMA
|
||||
from spacy.errors import ModelsWarning
|
||||
|
||||
from ..util import get_doc
|
||||
|
|
@ -139,81 +138,6 @@ def test_doc_api_set_ents(en_tokenizer):
|
|||
assert tokens.ents[0].end == 4
|
||||
|
||||
|
||||
def test_doc_api_merge(en_tokenizer):
|
||||
text = "WKRO played songs by the beach boys all night"
|
||||
attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
||||
# merge both with bulk merge
|
||||
doc = en_tokenizer(text)
|
||||
assert len(doc) == 9
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
retokenizer.merge(doc[7:9], attrs=attrs)
|
||||
assert len(doc) == 6
|
||||
assert doc[4].text == "the beach boys"
|
||||
assert doc[4].text_with_ws == "the beach boys "
|
||||
assert doc[4].tag_ == "NAMED"
|
||||
assert doc[5].text == "all night"
|
||||
assert doc[5].text_with_ws == "all night"
|
||||
assert doc[5].tag_ == "NAMED"
|
||||
|
||||
|
||||
def test_doc_api_merge_children(en_tokenizer):
|
||||
"""Test that attachments work correctly after merging."""
|
||||
text = "WKRO played songs by the beach boys all night"
|
||||
attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
||||
doc = en_tokenizer(text)
|
||||
assert len(doc) == 9
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
for word in doc:
|
||||
if word.i < word.head.i:
|
||||
assert word in list(word.head.lefts)
|
||||
elif word.i > word.head.i:
|
||||
assert word in list(word.head.rights)
|
||||
|
||||
|
||||
def test_doc_api_merge_hang(en_tokenizer):
|
||||
text = "through North and South Carolina"
|
||||
doc = en_tokenizer(text)
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[3:5], attrs={"lemma": "", "ent_type": "ORG"})
|
||||
retokenizer.merge(doc[1:2], attrs={"lemma": "", "ent_type": "ORG"})
|
||||
|
||||
|
||||
def test_doc_api_retokenizer(en_tokenizer):
|
||||
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7])
|
||||
assert len(doc) == 7
|
||||
assert doc[4].text == "the beach boys"
|
||||
|
||||
|
||||
def test_doc_api_retokenizer_attrs(en_tokenizer):
|
||||
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
||||
# test both string and integer attributes and values
|
||||
attrs = {LEMMA: "boys", "ENT_TYPE": doc.vocab.strings["ORG"]}
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
assert len(doc) == 7
|
||||
assert doc[4].text == "the beach boys"
|
||||
assert doc[4].lemma_ == "boys"
|
||||
assert doc[4].ent_type_ == "ORG"
|
||||
|
||||
|
||||
@pytest.mark.xfail
|
||||
def test_doc_api_retokenizer_lex_attrs(en_tokenizer):
|
||||
"""Test that lexical attributes can be changed (see #2390)."""
|
||||
doc = en_tokenizer("WKRO played beach boys songs")
|
||||
assert not any(token.is_stop for token in doc)
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[2:4], attrs={"LEMMA": "boys", "IS_STOP": True})
|
||||
assert doc[2].text == "beach boys"
|
||||
assert doc[2].lemma_ == "boys"
|
||||
assert doc[2].is_stop
|
||||
new_doc = Doc(doc.vocab, words=["beach boys"])
|
||||
assert new_doc[0].is_stop
|
||||
|
||||
|
||||
def test_doc_api_sents_empty_string(en_tokenizer):
|
||||
doc = en_tokenizer("")
|
||||
doc.is_parsed = True
|
||||
|
|
|
|||
|
|
@ -1,14 +1,89 @@
|
|||
# coding: utf-8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import pytest
|
||||
from spacy.attrs import LEMMA
|
||||
from spacy.vocab import Vocab
|
||||
from spacy.tokens import Doc
|
||||
import pytest
|
||||
|
||||
from ..util import get_doc
|
||||
|
||||
|
||||
def test_spans_merge_tokens(en_tokenizer):
|
||||
def test_doc_retokenize_merge(en_tokenizer):
|
||||
text = "WKRO played songs by the beach boys all night"
|
||||
attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
||||
doc = en_tokenizer(text)
|
||||
assert len(doc) == 9
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
retokenizer.merge(doc[7:9], attrs=attrs)
|
||||
assert len(doc) == 6
|
||||
assert doc[4].text == "the beach boys"
|
||||
assert doc[4].text_with_ws == "the beach boys "
|
||||
assert doc[4].tag_ == "NAMED"
|
||||
assert doc[5].text == "all night"
|
||||
assert doc[5].text_with_ws == "all night"
|
||||
assert doc[5].tag_ == "NAMED"
|
||||
|
||||
|
||||
def test_doc_retokenize_merge_children(en_tokenizer):
|
||||
"""Test that attachments work correctly after merging."""
|
||||
text = "WKRO played songs by the beach boys all night"
|
||||
attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
||||
doc = en_tokenizer(text)
|
||||
assert len(doc) == 9
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
for word in doc:
|
||||
if word.i < word.head.i:
|
||||
assert word in list(word.head.lefts)
|
||||
elif word.i > word.head.i:
|
||||
assert word in list(word.head.rights)
|
||||
|
||||
|
||||
def test_doc_retokenize_merge_hang(en_tokenizer):
|
||||
text = "through North and South Carolina"
|
||||
doc = en_tokenizer(text)
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[3:5], attrs={"lemma": "", "ent_type": "ORG"})
|
||||
retokenizer.merge(doc[1:2], attrs={"lemma": "", "ent_type": "ORG"})
|
||||
|
||||
|
||||
def test_doc_retokenize_retokenizer(en_tokenizer):
|
||||
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7])
|
||||
assert len(doc) == 7
|
||||
assert doc[4].text == "the beach boys"
|
||||
|
||||
|
||||
def test_doc_retokenize_retokenizer_attrs(en_tokenizer):
|
||||
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
||||
# test both string and integer attributes and values
|
||||
attrs = {LEMMA: "boys", "ENT_TYPE": doc.vocab.strings["ORG"]}
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[4:7], attrs=attrs)
|
||||
assert len(doc) == 7
|
||||
assert doc[4].text == "the beach boys"
|
||||
assert doc[4].lemma_ == "boys"
|
||||
assert doc[4].ent_type_ == "ORG"
|
||||
|
||||
|
||||
@pytest.mark.xfail
|
||||
def test_doc_retokenize_lex_attrs(en_tokenizer):
|
||||
"""Test that lexical attributes can be changed (see #2390)."""
|
||||
doc = en_tokenizer("WKRO played beach boys songs")
|
||||
assert not any(token.is_stop for token in doc)
|
||||
with doc.retokenize() as retokenizer:
|
||||
retokenizer.merge(doc[2:4], attrs={"LEMMA": "boys", "IS_STOP": True})
|
||||
assert doc[2].text == "beach boys"
|
||||
assert doc[2].lemma_ == "boys"
|
||||
assert doc[2].is_stop
|
||||
new_doc = Doc(doc.vocab, words=["beach boys"])
|
||||
assert new_doc[0].is_stop
|
||||
|
||||
|
||||
def test_doc_retokenize_spans_merge_tokens(en_tokenizer):
|
||||
text = "Los Angeles start."
|
||||
heads = [1, 1, 0, -1]
|
||||
tokens = en_tokenizer(text)
|
||||
|
|
@ -25,7 +100,7 @@ def test_spans_merge_tokens(en_tokenizer):
|
|||
assert doc[0].ent_type_ == "GPE"
|
||||
|
||||
|
||||
def test_spans_merge_heads(en_tokenizer):
|
||||
def test_doc_retokenize_spans_merge_heads(en_tokenizer):
|
||||
text = "I found a pilates class near work."
|
||||
heads = [1, 0, 2, 1, -3, -1, -1, -6]
|
||||
tokens = en_tokenizer(text)
|
||||
|
|
@ -43,7 +118,7 @@ def test_spans_merge_heads(en_tokenizer):
|
|||
assert doc[5].head.i == 4
|
||||
|
||||
|
||||
def test_spans_merge_non_disjoint(en_tokenizer):
|
||||
def test_doc_retokenize_spans_merge_non_disjoint(en_tokenizer):
|
||||
text = "Los Angeles start."
|
||||
doc = en_tokenizer(text)
|
||||
with pytest.raises(ValueError):
|
||||
|
|
@ -58,7 +133,7 @@ def test_spans_merge_non_disjoint(en_tokenizer):
|
|||
)
|
||||
|
||||
|
||||
def test_span_np_merges(en_tokenizer):
|
||||
def test_doc_retokenize_span_np_merges(en_tokenizer):
|
||||
text = "displaCy is a parse tool built with Javascript"
|
||||
heads = [1, 0, 2, 1, -3, -1, -1, -1]
|
||||
tokens = en_tokenizer(text)
|
||||
|
|
@ -87,7 +162,7 @@ def test_span_np_merges(en_tokenizer):
|
|||
retokenizer.merge(ent)
|
||||
|
||||
|
||||
def test_spans_entity_merge(en_tokenizer):
|
||||
def test_doc_retokenize_spans_entity_merge(en_tokenizer):
|
||||
# fmt: off
|
||||
text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale.\n"
|
||||
heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2, -13, -1]
|
||||
|
|
@ -108,7 +183,7 @@ def test_spans_entity_merge(en_tokenizer):
|
|||
assert len(doc) == 15
|
||||
|
||||
|
||||
def test_spans_entity_merge_iob():
|
||||
def test_doc_retokenize_spans_entity_merge_iob():
|
||||
# Test entity IOB stays consistent after merging
|
||||
words = ["a", "b", "c", "d", "e"]
|
||||
doc = Doc(Vocab(), words=words)
|
||||
|
|
@ -147,7 +222,7 @@ def test_spans_entity_merge_iob():
|
|||
assert doc[4].ent_iob_ == "I"
|
||||
|
||||
|
||||
def test_spans_sentence_update_after_merge(en_tokenizer):
|
||||
def test_doc_retokenize_spans_sentence_update_after_merge(en_tokenizer):
|
||||
# fmt: off
|
||||
text = "Stewart Lee is a stand up comedian. He lives in England and loves Joe Pasquale."
|
||||
heads = [1, 1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2, -7]
|
||||
|
|
@ -155,7 +230,6 @@ def test_spans_sentence_update_after_merge(en_tokenizer):
|
|||
'punct', 'nsubj', 'ROOT', 'prep', 'pobj', 'cc', 'conj',
|
||||
'compound', 'dobj', 'punct']
|
||||
# fmt: on
|
||||
|
||||
tokens = en_tokenizer(text)
|
||||
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
||||
sent1, sent2 = list(doc.sents)
|
||||
|
|
@ -169,7 +243,7 @@ def test_spans_sentence_update_after_merge(en_tokenizer):
|
|||
assert len(sent2) == init_len2 - 1
|
||||
|
||||
|
||||
def test_spans_subtree_size_check(en_tokenizer):
|
||||
def test_doc_retokenize_spans_subtree_size_check(en_tokenizer):
|
||||
# fmt: off
|
||||
text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale"
|
||||
heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2]
|
||||
|
|
@ -177,7 +251,6 @@ def test_spans_subtree_size_check(en_tokenizer):
|
|||
"nsubj", "relcl", "prep", "pobj", "cc", "conj", "compound",
|
||||
"dobj"]
|
||||
# fmt: on
|
||||
|
||||
tokens = en_tokenizer(text)
|
||||
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
||||
sent1 = list(doc.sents)[0]
|
||||
|
|
@ -8,7 +8,7 @@ from spacy.tokens import Doc
|
|||
from ..util import get_doc
|
||||
|
||||
|
||||
def test_doc_split(en_vocab):
|
||||
def test_doc_retokenize_split(en_vocab):
|
||||
words = ["LosAngeles", "start", "."]
|
||||
heads = [1, 1, 0]
|
||||
doc = get_doc(en_vocab, words=words, heads=heads)
|
||||
|
|
@ -41,7 +41,7 @@ def test_doc_split(en_vocab):
|
|||
assert len(str(doc)) == 19
|
||||
|
||||
|
||||
def test_split_dependencies(en_vocab):
|
||||
def test_doc_retokenize_split_dependencies(en_vocab):
|
||||
doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
|
||||
dep1 = doc.vocab.strings.add("amod")
|
||||
dep2 = doc.vocab.strings.add("subject")
|
||||
|
|
@ -56,7 +56,7 @@ def test_split_dependencies(en_vocab):
|
|||
assert doc[1].dep == dep2
|
||||
|
||||
|
||||
def test_split_heads_error(en_vocab):
|
||||
def test_doc_retokenize_split_heads_error(en_vocab):
|
||||
doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
|
||||
# Not enough heads
|
||||
with pytest.raises(ValueError):
|
||||
|
|
@ -69,7 +69,7 @@ def test_split_heads_error(en_vocab):
|
|||
retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1], doc[1], doc[1]])
|
||||
|
||||
|
||||
def test_spans_entity_merge_iob():
|
||||
def test_doc_retokenize_spans_entity_split_iob():
|
||||
# Test entity IOB stays consistent after merging
|
||||
words = ["abc", "d", "e"]
|
||||
doc = Doc(Vocab(), words=words)
|
||||
|
|
@ -84,7 +84,7 @@ def test_spans_entity_merge_iob():
|
|||
assert doc[3].ent_iob_ == "I"
|
||||
|
||||
|
||||
def test_spans_sentence_update_after_merge(en_vocab):
|
||||
def test_doc_retokenize_spans_sentence_update_after_split(en_vocab):
|
||||
# fmt: off
|
||||
words = ["StewartLee", "is", "a", "stand", "up", "comedian", ".", "He",
|
||||
"lives", "in", "England", "and", "loves", "JoePasquale", "."]
|
||||
|
|
@ -114,7 +114,7 @@ def test_spans_sentence_update_after_merge(en_vocab):
|
|||
assert len(sent2) == init_len2 + 1
|
||||
|
||||
|
||||
def test_split_orths_mismatch(en_vocab):
|
||||
def test_doc_retokenize_split_orths_mismatch(en_vocab):
|
||||
"""Test that the regular retokenizer.split raises an error if the orths
|
||||
don't match the original token text. There might still be a method that
|
||||
allows this, but for the default use cases, merging and splitting should
|
||||
|
|
@ -1,7 +1,6 @@
|
|||
# coding: utf8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import pytest
|
||||
from spacy.matcher import Matcher
|
||||
from spacy.tokens import Token, Doc
|
||||
|
||||
|
|
@ -28,7 +27,7 @@ def test_issue1971(en_vocab):
|
|||
def test_issue_1971_2(en_vocab):
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern1 = [{"ORTH": "EUR", "LOWER": {"IN": ["eur"]}}, {"LIKE_NUM": True}]
|
||||
pattern2 = [{"LIKE_NUM": True}, {"ORTH": "EUR"}] #{"IN": ["EUR"]}}]
|
||||
pattern2 = [{"LIKE_NUM": True}, {"ORTH": "EUR"}] # {"IN": ["EUR"]}}]
|
||||
doc = Doc(en_vocab, words=["EUR", "10", "is", "10", "EUR"])
|
||||
matcher.add("TEST1", None, pattern1, pattern2)
|
||||
matches = matcher(doc)
|
||||
|
|
@ -59,6 +58,5 @@ def test_issue_1971_4(en_vocab):
|
|||
pattern = [{"_": {"ext_a": "str_a", "ext_b": "str_b"}}] * 3
|
||||
matcher.add("TEST", None, pattern)
|
||||
matches = matcher(doc)
|
||||
# Interesting: uncommenting this causes a segmentation fault, so there's
|
||||
# definitely something going on here
|
||||
# assert len(matches) == 1
|
||||
# Uncommenting this caused a segmentation fault
|
||||
assert len(matches) == 1
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
# coding: utf-8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import pytest
|
||||
import numpy
|
||||
from spacy import displacy
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
# coding: utf-8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import pytest
|
||||
from spacy.lang.en import English
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -315,6 +315,11 @@ def read_regex(path):
|
|||
|
||||
|
||||
def compile_prefix_regex(entries):
|
||||
"""Compile a list of prefix rules into a regex object.
|
||||
|
||||
entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
|
||||
RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
|
||||
"""
|
||||
if "(" in entries:
|
||||
# Handle deprecated data
|
||||
expression = "|".join(
|
||||
|
|
@ -327,11 +332,21 @@ def compile_prefix_regex(entries):
|
|||
|
||||
|
||||
def compile_suffix_regex(entries):
|
||||
"""Compile a list of suffix rules into a regex object.
|
||||
|
||||
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
|
||||
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
|
||||
"""
|
||||
expression = "|".join([piece + "$" for piece in entries if piece.strip()])
|
||||
return re.compile(expression)
|
||||
|
||||
|
||||
def compile_infix_regex(entries):
|
||||
"""Compile a list of infix rules into a regex object.
|
||||
|
||||
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
|
||||
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
|
||||
"""
|
||||
expression = "|".join([piece for piece in entries if piece.strip()])
|
||||
return re.compile(expression)
|
||||
|
||||
|
|
|
|||
|
|
@ -504,6 +504,57 @@ an error if key doesn't match `ORTH` values.
|
|||
| `*addition_dicts` | dicts | Exception dictionaries to add to the base exceptions, in order. |
|
||||
| **RETURNS** | dict | Combined tokenizer exceptions. |
|
||||
|
||||
### util.compile_prefix_regex {#util.compile_prefix_regex tag="function"}
|
||||
|
||||
Compile a sequence of prefix rules into a regex object.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> prefixes = ("§", "%", "=", r"\+")
|
||||
> prefix_regex = util.compile_prefix_regex(prefixes)
|
||||
> nlp.tokenizer.prefix_search = prefix_regex.search
|
||||
> ```
|
||||
|
||||
| Name | Type | Description |
|
||||
| ----------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `entries` | tuple | The prefix rules, e.g. [`lang.punctuation.TOKENIZER_PREFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
|
||||
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.prefix_search`](/api/tokenizer#attributes). |
|
||||
|
||||
### util.compile_suffix_regex {#util.compile_suffix_regex tag="function"}
|
||||
|
||||
Compile a sequence of suffix rules into a regex object.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> suffixes = ("'s", "'S", r"(?<=[0-9])\+")
|
||||
> suffix_regex = util.compile_suffix_regex(suffixes)
|
||||
> nlp.tokenizer.suffix_search = suffix_regex.search
|
||||
> ```
|
||||
|
||||
| Name | Type | Description |
|
||||
| ----------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `entries` | tuple | The suffix rules, e.g. [`lang.punctuation.TOKENIZER_SUFFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
|
||||
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.suffix_search`](/api/tokenizer#attributes). |
|
||||
|
||||
### util.compile_infix_regex {#util.compile_infix_regex tag="function"}
|
||||
|
||||
Compile a sequence of infix rules into a regex object.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> infixes = ("…", "-", "—", r"(?<=[0-9])[+\-\*^](?=[0-9-])")
|
||||
> infix_regex = util.compile_infix_regex(infixes)
|
||||
> nlp.tokenizer.infix_finditer = infix_regex.finditer
|
||||
> ```
|
||||
|
||||
| Name | Type | Description |
|
||||
| ----------- | ------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `entries` | tuple | The infix rules, e.g. [`lang.punctuation.TOKENIZER_INFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
|
||||
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.infix_finditer`](/api/tokenizer#attributes). |
|
||||
|
||||
### util.minibatch {#util.minibatch tag="function" new="2"}
|
||||
|
||||
Iterate over batches of items. `size` may be an iterator, so that batch-size can
|
||||
|
|
|
|||
|
|
@ -812,6 +812,40 @@ only be applied at the **end of a token**, so your expression should end with a
|
|||
|
||||
</Infobox>
|
||||
|
||||
#### Adding to existing rule sets {#native-tokenizer-additions}
|
||||
|
||||
In many situations, you don't necessarily need entirely custom rules. Sometimes
|
||||
you just want to add another character to the prefixes, suffixes or infixes. The
|
||||
default prefix, suffix and infix rules are available via the `nlp` object's
|
||||
`Defaults` and the [`Tokenizer.suffix_search`](/api/tokenizer#attributes)
|
||||
attribute is writable, so you can overwrite it with a compiled regular
|
||||
expression object using of the modified default rules. spaCy ships with utility
|
||||
functions to help you compile the regular expressions – for example,
|
||||
[`compile_suffix_regex`](/api/top-level#util.compile_suffix_regex):
|
||||
|
||||
```python
|
||||
suffixes = nlp.Defaults.suffixes + (r'''-+$''',)
|
||||
suffix_regex = spacy.util.compile_suffix_regex(suffixes)
|
||||
nlp.tokenizer.suffix_search = suffix_regex.search
|
||||
```
|
||||
|
||||
For an overview of the default regular expressions, see
|
||||
[`lang/punctuation.py`](https://github.com/explosion/spaCy/blob/master/spacy/lang/punctuation.py).
|
||||
The `Tokenizer.suffix_search` attribute should be a function which takes a
|
||||
unicode string and returns a **regex match object** or `None`. Usually we use
|
||||
the `.search` attribute of a compiled regex object, but you can use some other
|
||||
function that behaves the same way.
|
||||
|
||||
<Infobox title="Important note" variant="warning">
|
||||
|
||||
If you're using a statistical model, writing to the `nlp.Defaults` or
|
||||
`English.Defaults` directly won't work, since the regular expressions are read
|
||||
from the model and will be compiled when you load it. You'll only see the effect
|
||||
if you call [`spacy.blank`](/api/top-level#spacy.blank) or
|
||||
`Defaults.create_tokenizer()`.
|
||||
|
||||
</Infobox>
|
||||
|
||||
### Hooking an arbitrary tokenizer into the pipeline {#custom-tokenizer}
|
||||
|
||||
The tokenizer is the first component of the processing pipeline and the only one
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user