mirror of
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90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
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# coding: utf-8
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from __future__ import unicode_literals
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import json
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from tempfile import TemporaryDirectory, NamedTemporaryFile
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from ...cli.train import train
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def test_cli_trained_model_can_be_saved():
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cmd = None
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lang = 'nl'
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with TemporaryDirectory() as output_dir:
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train_file = NamedTemporaryFile('wb', dir=output_dir, delete=False)
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train_corpus = [
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{
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"id": "identifier_0",
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"paragraphs": [
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{
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"raw": "Jan houdt van Marie.\n",
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"sentences": [
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{
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"tokens": [
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{
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"id": 0,
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"dep": "nsubj",
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"head": 1,
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"tag": "NOUN",
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"orth": "Jan",
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"ner": "B-PER"
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},
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{
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"id": 1,
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"dep": "ROOT",
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"head": 0,
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"tag": "VERB",
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"orth": "houdt",
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"ner": "O"
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},
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{
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"id": 2,
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"dep": "case",
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"head": 1,
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"tag": "ADP",
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"orth": "van",
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"ner": "O"
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},
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{
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"id": 3,
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"dep": "obj",
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"head": -2,
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"tag": "NOUN",
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"orth": "Marie",
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"ner": "B-PER"
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},
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{
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"id": 4,
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"dep": "punct",
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"head": -3,
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"tag": "PUNCT",
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"orth": ".",
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"ner": "O"
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},
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{
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"id": 5,
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"dep": "",
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"head": -1,
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"tag": "SPACE",
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"orth": "\n",
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"ner": "O"
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}
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],
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"brackets": []
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}
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]
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}
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]
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}
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]
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train_file.write(json.dumps(train_corpus).encode('utf-8'))
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train_file.close()
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train_data = train_file.name
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dev_data = train_data
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# spacy train -n 1 -g -1 nl output_nl training_corpus.json training \
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# corpus.json
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train(cmd, lang, output_dir, train_data, dev_data, n_iter=1)
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assert True
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