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Add example code
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Hironsan committed Nov 25, 2017
1 parent aea21e6 commit 5dd743b
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17 changes: 17 additions & 0 deletions examples/download_model.py
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import os

import anago
from anago.utils import download
from anago.reader import load_data_and_labels

dir_path = 'test_dir'
url = 'https://storage.googleapis.com/chakki/datasets/public/models.zip'
DATA_ROOT = os.path.join(os.path.dirname(__file__), '../data/conll2003/en/ner')

test_path = os.path.join(DATA_ROOT, 'test.txt')
x_test, y_test = load_data_and_labels(test_path)

download(url, dir_path)

model = anago.Sequence.load(dir_path)
model.eval(x_test, y_test)
22 changes: 22 additions & 0 deletions examples/ner_glove.py
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import os

import anago
from anago.reader import load_data_and_labels, load_glove

DATA_ROOT = os.path.join(os.path.dirname(__file__), '../data/conll2003/en/ner')
EMBEDDING_PATH = 'glove.6B.100d.txt'

train_path = os.path.join(DATA_ROOT, 'train.txt')
valid_path = os.path.join(DATA_ROOT, 'valid.txt')

print('Loading data...')
x_train, y_train = load_data_and_labels(train_path)
x_valid, y_valid = load_data_and_labels(valid_path)
print(len(x_train), 'train sequences')
print(len(x_valid), 'valid sequences')

embeddings = load_glove(EMBEDDING_PATH)

# Use pre-trained word embeddings
model = anago.Sequence(max_epoch=1, embeddings=embeddings)
model.train(x_train, y_train, x_valid, y_valid)
24 changes: 24 additions & 0 deletions examples/ner_word2vec.py
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import os

from gensim.models.keyedvectors import KeyedVectors

import anago
from anago.reader import load_data_and_labels

DATA_ROOT = os.path.join(os.path.dirname(__file__), '../data/conll2003/en/ner')
EMBEDDING_PATH = 'model.txt'

train_path = os.path.join(DATA_ROOT, 'train.txt')
valid_path = os.path.join(DATA_ROOT, 'valid.txt')

print('Loading data...')
x_train, y_train = load_data_and_labels(train_path)
x_valid, y_valid = load_data_and_labels(valid_path)
print(len(x_train), 'train sequences')
print(len(x_valid), 'valid sequences')

embeddings = KeyedVectors.load_word2vec_format(EMBEDDING_PATH).wv

# Use pre-trained word embeddings
model = anago.Sequence(max_epoch=1, embeddings=embeddings)
model.train(x_train, y_train, x_valid, y_valid)

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