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Improve pretrain textcat example

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This commit is contained in:
Matthew Honnibal 2018-11-04 00:17:09 +00:00
parent 3e7a96f99d
commit bc8cda818c
1 changed files with 8 additions and 11 deletions
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19
examples/training/pretrain_textcat.py
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@ -47,8 +47,8 @@ def load_textcat_data(limit=0):
train_data = train_data[-limit:] train_data = train_data[-limit:]
texts, labels = zip(*train_data) texts, labels = zip(*train_data)
eval_texts, eval_labels = zip(*eval_data) eval_texts, eval_labels = zip(*eval_data)
cats = [{'POSITIVE': bool(y)} for y in labels] cats = [{'POSITIVE': bool(y), 'NEGATIVE': not bool(y)} for y in labels]
eval_cats = [{'POSITIVE': bool(y)} for y in eval_labels] eval_cats = [{'POSITIVE': bool(y), 'NEGATIVE': not bool(y)} for y in eval_labels]
return (texts, cats), (eval_texts, eval_cats) return (texts, cats), (eval_texts, eval_cats)
@ -61,9 +61,9 @@ def prefer_gpu():
def build_textcat_model(tok2vec, nr_class, width): def build_textcat_model(tok2vec, nr_class, width):
from thinc.v2v import Model, Affine, Maxout from thinc.v2v import Model, Softmax, Maxout
from thinc.api import flatten_add_lengths, chain from thinc.api import flatten_add_lengths, chain
from thinc.t2v import Pooling, sum_pool, max_pool from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
from thinc.misc import Residual, LayerNorm from thinc.misc import Residual, LayerNorm
from spacy._ml import logistic, zero_init from spacy._ml import logistic, zero_init
@ -71,11 +71,8 @@ def build_textcat_model(tok2vec, nr_class, width):
model = ( model = (
tok2vec tok2vec
>> flatten_add_lengths >> flatten_add_lengths
>> Pooling(sum_pool, max_pool) >> Pooling(mean_pool)
>> Residual(LayerNorm(Maxout(width*2, width*2, pieces=3))) >> Softmax(nr_class, width)
>> Residual(LayerNorm(Maxout(width*2, width*2, pieces=3)))
>> zero_init(Affine(nr_class, width*2, drop_factor=0.0))
>> logistic
) )
model.tok2vec = tok2vec model.tok2vec = tok2vec
return model return model
@ -92,9 +89,9 @@ def create_pipeline(width, embed_size, vectors_model):
nlp = spacy.load(vectors_model) nlp = spacy.load(vectors_model)
print("Start training") print("Start training")
textcat = TextCategorizer(nlp.vocab, textcat = TextCategorizer(nlp.vocab,
labels=['POSITIVE'], labels=['POSITIVE', 'NEGATIVE'],
model=build_textcat_model( model=build_textcat_model(
Tok2Vec(width=width, embed_size=embed_size), 1, width)) Tok2Vec(width=width, embed_size=embed_size), 2, width))
nlp.add_pipe(textcat) nlp.add_pipe(textcat)
return nlp return nlp