def parser(vocab):
vocab.strings.add("ROOT")
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(vocab, model, **config)
parser.cfg["token_vector_width"] = 4
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.initialize(lambda: [_parser_example(parser)])
sgd = Adam(0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=["a", "b", "c", "d"])
example = Example.from_dict(doc, {
"heads": [1, 1, 3, 3],
"deps": ["left", "ROOT", "left", "ROOT"]
})
parser.update([example], sgd=sgd, losses=losses)
return parser
def test_issue3830_with_subtok():
"""Test that the parser does have subtok label if learn_tokens=True."""
config = {
"learn_tokens": True,
}
model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.initialize(lambda: [_parser_example(parser)])
assert "subtok" in parser.labels
def parser(en_vocab):
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
"beam_width": 1,
"beam_update_prob": 1.0,
"beam_density": 0.0,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(en_vocab, model, **config)
parser.add_label("nsubj")
return parser
def test_issue3830_no_subtok():
"""Test that the parser doesn't have subtok label if not learn_tokens"""
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
model = registry.resolve({"model": DEFAULT_PARSER_MODEL},
validate=True)["model"]
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.initialize(lambda: [_parser_example(parser)])
assert "subtok" not in parser.labels
def parser(vocab):
parser = DependencyParser(vocab)
parser.cfg["token_vector_width"] = 4
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.begin_training([], **parser.cfg)
sgd = Adam(NumpyOps(), 0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=["a", "b", "c", "d"])
gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
parser.update([doc], [gold], sgd=sgd, losses=losses)
return parser
def parser(vocab):
parser = DependencyParser(vocab)
parser.cfg["token_vector_width"] = 4
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.begin_training([], **parser.cfg)
sgd = Adam(NumpyOps(), 0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=["a", "b", "c", "d"])
gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
parser.update([doc], [gold], sgd=sgd, losses=losses)
return parser
def parser(en_vocab):
parser = DependencyParser(en_vocab)
parser.add_label("nsubj")
parser.model, cfg = parser.Model(parser.moves.n_moves)
parser.cfg.update(cfg)
return parser
def parser(en_vocab):
parser = DependencyParser(en_vocab)
parser.add_label("nsubj")
parser.model, cfg = parser.Model(parser.moves.n_moves)
parser.cfg.update(cfg)
return parser
