explosion/spaCy
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Merge pull request #5062 from svlandeg/bugfix/merge-conflicts

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Fix sync between master and develop
This commit is contained in:
Ines Montani 2020-02-26 13:41:16 +01:00 committed by GitHub
commit f39ddda193
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2 changed files with 4 additions and 22 deletions
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4
bin/wiki_entity_linking/wikidata_train_entity_linker.py
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@ -175,12 +175,10 @@ def main(
kb=kb, kb=kb,
labels_discard=labels_discard, labels_discard=labels_discard,
) )
docs, golds = zip(*train_batch)
try: try:
with nlp.disable_pipes(*other_pipes): with nlp.disable_pipes(*other_pipes):
nlp.update( nlp.update(
docs=docs, examples=train_batch,
golds=golds,
sgd=optimizer, sgd=optimizer,
drop=dropout, drop=dropout,
losses=losses, losses=losses,

22
spacy/cli/train.py
View File

@ -28,13 +28,6 @@ def train(
pipeline: ("Comma-separated names of pipeline components", "option", "p", str) = "tagger,parser,ner", pipeline: ("Comma-separated names of pipeline components", "option", "p", str) = "tagger,parser,ner",
vectors: ("Model to load vectors from", "option", "v", str) = None, vectors: ("Model to load vectors from", "option", "v", str) = None,
replace_components: ("Replace components from base model", "flag", "R", bool) = False, replace_components: ("Replace components from base model", "flag", "R", bool) = False,
width: ("Width of CNN layers of Tok2Vec component", "option", "cw", int) = 96,
conv_depth: ("Depth of CNN layers of Tok2Vec component", "option", "cd", int) = 4,
cnn_window: ("Window size for CNN layers of Tok2Vec component", "option", "cW", int) = 1,
cnn_pieces: ("Maxout size for CNN layers of Tok2Vec component. 1 for Mish", "option", "cP", int) = 3,
use_chars: ("Whether to use character-based embedding of Tok2Vec component", "flag", "chr", bool) = False,
bilstm_depth: ("Depth of BiLSTM layers of Tok2Vec component (requires PyTorch)", "option", "lstm", int) = 0,
embed_rows: ("Number of embedding rows of Tok2Vec component", "option", "er", int) = 2000,
n_iter: ("Number of iterations", "option", "n", int) = 30, n_iter: ("Number of iterations", "option", "n", int) = 30,
n_early_stopping: ("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int) = None, n_early_stopping: ("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int) = None,
n_examples: ("Number of examples", "option", "ns", int) = 0, n_examples: ("Number of examples", "option", "ns", int) = 0,
@ -232,14 +225,7 @@ def train(
else: else:
# Start with a blank model, call begin_training # Start with a blank model, call begin_training
cfg = {"device": use_gpu} cfg = {"device": use_gpu}
cfg["conv_depth"] = conv_depth optimizer = nlp.begin_training(lambda: corpus.train_examples, **cfg)
cfg["token_vector_width"] = width
cfg["bilstm_depth"] = bilstm_depth
cfg["cnn_maxout_pieces"] = cnn_pieces
cfg["embed_size"] = embed_rows
cfg["conv_window"] = cnn_window
cfg["subword_features"] = not use_chars
optimizer = nlp.begin_training(lambda: corpus.train_tuples, **cfg)
nlp._optimizer = None nlp._optimizer = None
# Load in pretrained weights # Load in pretrained weights
@ -362,11 +348,9 @@ def train(
for batch in util.minibatch_by_words(train_data, size=batch_sizes): for batch in util.minibatch_by_words(train_data, size=batch_sizes):
if not batch: if not batch:
continue continue
docs, golds = zip(*batch)
try: try:
nlp.update( nlp.update(
docs, batch,
golds,
sgd=optimizer, sgd=optimizer,
drop=next(dropout_rates), drop=next(dropout_rates),
losses=losses, losses=losses,
@ -609,7 +593,7 @@ def _get_metrics(component):
elif component == "tagger": elif component == "tagger":
return ("tags_acc",) return ("tags_acc",)
elif component == "ner": elif component == "ner":
return ("ents_f", "ents_p", "ents_r", "enty_per_type") return ("ents_f", "ents_p", "ents_r", "ents_per_type")
elif component == "sentrec": elif component == "sentrec":
return ("sent_f", "sent_p", "sent_r") return ("sent_f", "sent_p", "sent_r")
elif component == "textcat": elif component == "textcat":