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- import unittest
-
- from fastNLP.models.biaffine_parser import BiaffineParser, ParserLoss, ParserMetric
- from .model_runner import *
-
-
- def prepare_parser_data():
- index = 'index'
- ds = DataSet({index: list(range(N_SAMPLES))})
- ds.apply_field(lambda x: RUNNER.gen_var_seq(MAX_LEN, VOCAB_SIZE),
- field_name=index, new_field_name=C.INPUTS(0),
- is_input=True)
- ds.apply_field(lambda x: RUNNER.gen_seq(len(x), NUM_CLS),
- field_name=C.INPUTS(0), new_field_name=C.INPUTS(1),
- is_input=True)
- # target1 is heads, should in range(0, len(words))
- ds.apply_field(lambda x: RUNNER.gen_seq(len(x), len(x)),
- field_name=C.INPUTS(0), new_field_name=C.TARGETS(0),
- is_target=True)
- ds.apply_field(lambda x: RUNNER.gen_seq(len(x), NUM_CLS),
- field_name=C.INPUTS(0), new_field_name=C.TARGETS(1),
- is_target=True)
- ds.apply_field(len, field_name=C.INPUTS(0), new_field_name=C.INPUT_LEN,
- is_input=True, is_target=True)
- return ds
-
-
- class TestBiaffineParser(unittest.TestCase):
- def test_train(self):
- model = BiaffineParser(init_embed=(VOCAB_SIZE, 10),
- pos_vocab_size=VOCAB_SIZE, pos_emb_dim=10,
- rnn_hidden_size=10,
- arc_mlp_size=10,
- label_mlp_size=10,
- num_label=NUM_CLS, encoder='var-lstm')
- ds = prepare_parser_data()
- RUNNER.run_model(model, ds, loss=ParserLoss(), metrics=ParserMetric())
-
- def test_train2(self):
- model = BiaffineParser(init_embed=(VOCAB_SIZE, 10),
- pos_vocab_size=VOCAB_SIZE, pos_emb_dim=10,
- rnn_hidden_size=16,
- arc_mlp_size=10,
- label_mlp_size=10,
- num_label=NUM_CLS, encoder='transformer')
- ds = prepare_parser_data()
- RUNNER.run_model(model, ds, loss=ParserLoss(), metrics=ParserMetric())
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