mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-25 05:01:02 +03:00
222 lines
7.5 KiB
Python
222 lines
7.5 KiB
Python
"""This script is experimental.
|
|
|
|
Try pre-training the CNN component of the text categorizer using a cheap
|
|
language modelling-like objective. Specifically, we load pretrained vectors
|
|
(from something like word2vec, GloVe, FastText etc), and use the CNN to
|
|
predict the tokens' pretrained vectors. This isn't as easy as it sounds:
|
|
we're not merely doing compression here, because heavy dropout is applied,
|
|
including over the input words. This means the model must often (50% of the time)
|
|
use the context in order to predict the word.
|
|
|
|
To evaluate the technique, we're pre-training with the 50k texts from the IMDB
|
|
corpus, and then training with only 100 labels. Note that it's a bit dirty to
|
|
pre-train with the development data, but also not *so* terrible: we're not using
|
|
the development labels, after all --- only the unlabelled text.
|
|
"""
|
|
import plac
|
|
import random
|
|
import spacy
|
|
import thinc.extra.datasets
|
|
from spacy.util import minibatch, use_gpu, compounding
|
|
from spacy._ml import Tok2Vec
|
|
from spacy.pipeline import TextCategorizer
|
|
import numpy
|
|
|
|
|
|
def load_texts(limit=0):
|
|
train, dev = thinc.extra.datasets.imdb()
|
|
train_texts, train_labels = zip(*train)
|
|
dev_texts, dev_labels = zip(*train)
|
|
train_texts = list(train_texts)
|
|
dev_texts = list(dev_texts)
|
|
random.shuffle(train_texts)
|
|
random.shuffle(dev_texts)
|
|
if limit >= 1:
|
|
return train_texts[:limit]
|
|
else:
|
|
return list(train_texts) + list(dev_texts)
|
|
|
|
|
|
def load_textcat_data(limit=0):
|
|
"""Load data from the IMDB dataset."""
|
|
# Partition off part of the train data for evaluation
|
|
train_data, eval_data = thinc.extra.datasets.imdb()
|
|
random.shuffle(train_data)
|
|
train_data = train_data[-limit:]
|
|
texts, labels = zip(*train_data)
|
|
eval_texts, eval_labels = zip(*eval_data)
|
|
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
|
|
eval_cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in eval_labels]
|
|
return (texts, cats), (eval_texts, eval_cats)
|
|
|
|
|
|
def prefer_gpu():
|
|
used = spacy.util.use_gpu(0)
|
|
if used is None:
|
|
return False
|
|
else:
|
|
import cupy.random
|
|
|
|
cupy.random.seed(0)
|
|
return True
|
|
|
|
|
|
def build_textcat_model(tok2vec, nr_class, width):
|
|
from thinc.v2v import Model, Softmax, Maxout
|
|
from thinc.api import flatten_add_lengths, chain
|
|
from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
|
|
from thinc.misc import Residual, LayerNorm
|
|
from spacy._ml import logistic, zero_init
|
|
|
|
with Model.define_operators({">>": chain}):
|
|
model = (
|
|
tok2vec
|
|
>> flatten_add_lengths
|
|
>> Pooling(mean_pool)
|
|
>> Softmax(nr_class, width)
|
|
)
|
|
model.tok2vec = tok2vec
|
|
return model
|
|
|
|
|
|
def block_gradients(model):
|
|
from thinc.api import wrap
|
|
|
|
def forward(X, drop=0.0):
|
|
Y, _ = model.begin_update(X, drop=drop)
|
|
return Y, None
|
|
|
|
return wrap(forward, model)
|
|
|
|
|
|
def create_pipeline(width, embed_size, vectors_model):
|
|
print("Load vectors")
|
|
nlp = spacy.load(vectors_model)
|
|
print("Start training")
|
|
textcat = TextCategorizer(
|
|
nlp.vocab,
|
|
labels=["POSITIVE", "NEGATIVE"],
|
|
model=build_textcat_model(
|
|
Tok2Vec(width=width, embed_size=embed_size), 2, width
|
|
),
|
|
)
|
|
|
|
nlp.add_pipe(textcat)
|
|
return nlp
|
|
|
|
|
|
def train_tensorizer(nlp, texts, dropout, n_iter):
|
|
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
|
|
import tqdm
|
|
|
|
tensorizer = nlp.create_pipe("tensorizer")
|
|
nlp.add_pipe(tensorizer)
|
|
optimizer = nlp.begin_training()
|
|
for i in range(n_iter):
|
|
losses = {}
|
|
for i, batch in enumerate(minibatch(tqdm.tqdm(texts))):
|
|
docs = [nlp.make_doc(text) for text in batch]
|
|
tensorizer.update(docs, None, losses=losses, sgd=optimizer, drop=dropout)
|
|
print(losses)
|
|
return optimizer
|
|
|
|
|
|
def train_textcat(nlp, n_texts, n_iter=10):
|
|
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
|
|
import tqdm
|
|
|
|
textcat = nlp.get_pipe("textcat")
|
|
tok2vec_weights = textcat.model.tok2vec.to_bytes()
|
|
(train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
|
|
print(
|
|
"Using {} examples ({} training, {} evaluation)".format(
|
|
n_texts, len(train_texts), len(dev_texts)
|
|
)
|
|
)
|
|
train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
|
|
|
|
# get names of other pipes to disable them during training
|
|
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
|
|
with nlp.disable_pipes(*other_pipes): # only train textcat
|
|
optimizer = nlp.begin_training()
|
|
textcat.model.tok2vec.from_bytes(tok2vec_weights)
|
|
print("Training the model...")
|
|
print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
|
|
for i in range(n_iter):
|
|
losses = {"textcat": 0.0}
|
|
# batch up the examples using spaCy's minibatch
|
|
batches = minibatch(tqdm.tqdm(train_data), size=2)
|
|
for batch in batches:
|
|
texts, annotations = zip(*batch)
|
|
nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
|
|
with textcat.model.use_params(optimizer.averages):
|
|
# evaluate on the dev data split off in load_data()
|
|
scores = evaluate_textcat(nlp.tokenizer, textcat, dev_texts, dev_cats)
|
|
print(
|
|
"{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}".format( # print a simple table
|
|
losses["textcat"],
|
|
scores["textcat_p"],
|
|
scores["textcat_r"],
|
|
scores["textcat_f"],
|
|
)
|
|
)
|
|
|
|
|
|
def evaluate_textcat(tokenizer, textcat, texts, cats):
|
|
docs = (tokenizer(text) for text in texts)
|
|
tp = 1e-8
|
|
fp = 1e-8
|
|
tn = 1e-8
|
|
fn = 1e-8
|
|
for i, doc in enumerate(textcat.pipe(docs)):
|
|
gold = cats[i]
|
|
for label, score in doc.cats.items():
|
|
if label not in gold:
|
|
continue
|
|
if score >= 0.5 and gold[label] >= 0.5:
|
|
tp += 1.0
|
|
elif score >= 0.5 and gold[label] < 0.5:
|
|
fp += 1.0
|
|
elif score < 0.5 and gold[label] < 0.5:
|
|
tn += 1
|
|
elif score < 0.5 and gold[label] >= 0.5:
|
|
fn += 1
|
|
precision = tp / (tp + fp)
|
|
recall = tp / (tp + fn)
|
|
f_score = 2 * (precision * recall) / (precision + recall)
|
|
return {"textcat_p": precision, "textcat_r": recall, "textcat_f": f_score}
|
|
|
|
|
|
@plac.annotations(
|
|
width=("Width of CNN layers", "positional", None, int),
|
|
embed_size=("Embedding rows", "positional", None, int),
|
|
pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
|
|
train_iters=("Number of iterations to pretrain", "option", "tn", int),
|
|
train_examples=("Number of labelled examples", "option", "eg", int),
|
|
vectors_model=("Name or path to vectors model to learn from"),
|
|
)
|
|
def main(
|
|
width,
|
|
embed_size,
|
|
vectors_model,
|
|
pretrain_iters=30,
|
|
train_iters=30,
|
|
train_examples=1000,
|
|
):
|
|
random.seed(0)
|
|
numpy.random.seed(0)
|
|
use_gpu = prefer_gpu()
|
|
print("Using GPU?", use_gpu)
|
|
|
|
nlp = create_pipeline(width, embed_size, vectors_model)
|
|
print("Load data")
|
|
texts = load_texts(limit=0)
|
|
print("Train tensorizer")
|
|
optimizer = train_tensorizer(nlp, texts, dropout=0.2, n_iter=pretrain_iters)
|
|
print("Train textcat")
|
|
train_textcat(nlp, train_examples, n_iter=train_iters)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
plac.call(main)
|