This article is the 9th day article of Unique Vision Co., Ltd. Advent Calendar 2019.
This article is a sequel to Learn document categorization with spaCy CLI. Last time, we dealt with English documents, but in this article, we will study categorization using spaCy's CLI for Japanese documents.
Like last time, the execution environment uses the GPU runtime of google Colab, and the GPU is Tesla P100. Also, the version of the library is
is. GiNZA is installed according to the following procedure.
$ pip install "https://github.com/megagonlabs/ginza/releases/download/latest/ginza-latest.tar.gz"
This time, we will run it on Google Colab, so we will also execute the following magic.
import pkg_resources, imp
imp.reload(pkg_resources)
Use the Livedoor News Corpus for the dataset. Download the data in advance as follows.
$ wget https://www.rondhuit.com/download/ldcc-20140209.tar.gz
$ tar -xvf ldcc-20140209.tar.gz
The data structure is the same as last time, so the save process remains the same.
import spacy
import srsly
from spacy.gold import docs_to_json
def save_to_json(model, data, targets, target_names, output_file, n_texts=0):
def get_categories(target):
return dict([(key, int(target == i)) for i, key in enumerate(target_names)])
nlp = spacy.load(model)
nlp.disable_pipes(*nlp.pipe_names)
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer, first=True)
docs = []
count = 0
for i, doc in enumerate(nlp.pipe(data)):
doc.cats = get_categories(targets[i])
docs.append(doc)
if n_texts > 0 and count == n_texts:
break
count += 1
srsly.write_json(output_file, [docs_to_json(docs)])
return count
Load the data so that you can use this function.
from pathlib import Path
data = []
targets = []
target_names = []
for target, target_name in enumerate([p for p in Path('text').iterdir() if p.is_dir()]):
target_names.append(target_name.name)
for news in target_name.iterdir():
if 'LICENSE' in news.name:
continue
with open(news) as f:
s = '\n'.join(f.read().splitlines()[2:])
data.append(s)
targets.append(target)
Save it in the file as follows.
from sklearn.model_selection import train_test_split
X_train, X_dev, y_train, y_dev = train_test_split(data, targets, test_size=0.20, random_state=42)
save_to_json(
'ja_ginza',
X_train,
y_train,
target_names,
'train.json'
)
save_to_json(
'ja_ginza',
X_dev,
y_dev,
target_names,
'dev.json'
)
Note that unlike the previous article, we specified ja_ginza for the model.
If you create a JSON file by this method, non-ASCII characters will be Unicode escaped and output, but there is no problem because it will make it easier to read.
If you learn in the same way as last time, you will get the following error.
$ !time python -m spacy train ja output train.json dev.json -v ja_ginza -p textcat -ta simple_cnn -g 0
Training pipeline: ['textcat']
Starting with blank model 'ja'
Loading vector from model 'ja_ginza'
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/spacy/__main__.py", line 33, in <module>
plac.call(commands[command], sys.argv[1:])
File "/usr/local/lib/python3.6/dist-packages/plac_core.py", line 328, in call
cmd, result = parser.consume(arglist)
File "/usr/local/lib/python3.6/dist-packages/plac_core.py", line 207, in consume
return cmd, self.func(*(args + varargs + extraopts), **kwargs)
File "/usr/local/lib/python3.6/dist-packages/spacy/cli/train.py", line 213, in train
_load_vectors(nlp, vectors)
File "/usr/local/lib/python3.6/dist-packages/spacy/cli/train.py", line 530, in _load_vectors
util.load_model(vectors, vocab=nlp.vocab)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 162, in load_model
return load_model_from_link(name, **overrides)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 179, in load_model_from_link
return cls.load(**overrides)
File "/usr/local/lib/python3.6/dist-packages/spacy/data/ja_ginza/__init__.py", line 12, in load
return load_model_from_init_py(__file__, **overrides)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 228, in load_model_from_init_py
return load_model_from_path(data_path, meta, **overrides)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 211, in load_model_from_path
return nlp.from_disk(model_path)
File "/usr/local/lib/python3.6/dist-packages/spacy/language.py", line 941, in from_disk
util.from_disk(path, deserializers, exclude)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 654, in from_disk
reader(path / key)
File "/usr/local/lib/python3.6/dist-packages/spacy/language.py", line 936, in <lambda>
p, exclude=["vocab"]
File "nn_parser.pyx", line 665, in spacy.syntax.nn_parser.Parser.from_disk
File "nn_parser.pyx", line 77, in spacy.syntax.nn_parser.Parser.Model
File "/usr/local/lib/python3.6/dist-packages/spacy/_ml.py", line 323, in Tok2Vec
return _legacy_tok2vec.Tok2Vec(width, embed_size, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/spacy/ml/_legacy_tok2vec.py", line 44, in Tok2Vec
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
File "/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/static_vectors.py", line 43, in __init__
vectors = self.get_vectors()
File "/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/static_vectors.py", line 55, in get_vectors
return get_vectors(self.ops, self.lang)
File "/usr/local/lib/python3.6/dist-packages/thinc/extra/load_nlp.py", line 26, in get_vectors
nlp = get_spacy(lang)
File "/usr/local/lib/python3.6/dist-packages/thinc/extra/load_nlp.py", line 14, in get_spacy
SPACY_MODELS[lang] = spacy.load(lang, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/spacy/__init__.py", line 30, in load
return util.load_model(name, **overrides)
File "/usr/local/lib/python3.6/dist-packages/spacy/util.py", line 169, in load_model
raise IOError(Errors.E050.format(name=name))
OSError: [E050] Can't find model 'ja_nopn.vectors'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory.
real 0m1.610s
user 0m1.299s
sys 0m0.394s
When I read the error message, it seems that there is a problem with _load_vectors, so I will try to reproduce the situation with the following code.
from spacy.util import get_lang_class, load_model
lang_cls = get_lang_class('ja')
nlp = lang_cls()
load_model('ja_ginza', vocab=nlp.vocab)
Then this will succeed. I'm a little exhausted here, so I'll start learning from an empty model.
Just remove -v ja_ginza from the above command.
$ !time python -m spacy train ja output train.json dev.json -p textcat -ta simple_cnn -g 0
Training pipeline: ['textcat']
Starting with blank model 'ja'
Counting training words (limit=0)
tcmalloc: large alloc 2128887808 bytes == 0x629bc000 @ 0x7f7744def1e7 0x5acd6b 0x7f773a41a5db 0x7f773a41abf0 0x7f773a41ae36 0x7f773a4185c1 0x50ac25 0x50c5b9 0x7f76e032ab20 0x7f76e032f98f 0x7f76e03226c5 0x7f76e0371c47 0x7f76e31d372a 0x7f76e035efce 0x7f76e31d372a 0x7f76e038b1e7 0x7f76e31d372a 0x7f76e0352148 0x7f76e035c24b 0x59509c 0x54a8a5 0x551b81 0x5aa6ec 0x50abb3 0x50d390 0x508245 0x589471 0x5a067e 0x50d966 0x508245 0x50a080
Textcat evaluation score: F1-score macro-averaged across the labels 'peachy,
smax, it-life-hack, sports-watch, movie-enter, livedoor-homme, dokujo-tsushin,
kaden-channel, topic-news'
Itn Textcat Loss Textcat Token % CPU WPS GPU WPS
--- ------------ ------- ------- ------- -------
1 1312.086 84.850 99.995 32519 119886
2 181.481 89.898 99.995 32879 119157
3 119.681 91.490 99.995 32741 122003
4 87.429 92.677 99.995 32618 119988
5 66.618 92.674 99.995 32005 122189
6 45.137 92.484 99.995 32293 113857
Apparently I can learn, but it is very heavy compared to en. It is a level of weight that does not feel like letting you learn casually.
I tried categorizing Japanese documents using the CLI of spaCy. The bottom line is failure.
First of all, I think we have to load the base model and solve the heavy problem.
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