def setUp(self):
super(TestTrainer, self).setUp()
dataset = SequenceTaggingDatasetReader().read(
'tests/fixtures/data/sequence_tagging.tsv')
vocab = Vocabulary.from_instances(dataset)
self.vocab = vocab
dataset.index_instances(vocab)
self.dataset = dataset
self.model_params = Params({
"text_field_embedder": {
"tokens": {
"type": "embedding",
"embedding_dim": 5
}
},
"stacked_encoder": {
"type": "lstm",
"input_size": 5,
"hidden_size": 7,
"num_layers": 2
}
})
self.model = SimpleTagger.from_params(self.vocab, self.model_params)
self.optimizer = torch.optim.SGD(self.model.parameters(), 0.01)
self.iterator = BasicIterator(batch_size=2)
def setUp(self):
super(SimpleTaggerTest, self).setUp()
dataset = SequenceTaggingDatasetReader().read(
'tests/fixtures/data/sequence_tagging.tsv')
vocab = Vocabulary.from_dataset(dataset)
self.vocab = vocab
dataset.index_instances(vocab)
self.dataset = dataset
params = Params({
"text_field_embedder": {
"tokens": {
"type": "embedding",
"embedding_dim": 5
}
},
"stacked_encoder": {
"type": "lstm",
"input_size": 5,
"hidden_size": 7,
"num_layers": 2
}
})
self.model = SimpleTagger.from_params(self.vocab, params)
def setUp(self):
super(SimpleTaggerTest, self).setUp()
self.write_sequence_tagging_data()
dataset = SequenceTaggingDatasetReader().read(self.TRAIN_FILE)
vocab = Vocabulary.from_dataset(dataset)
self.vocab = vocab
dataset.index_instances(vocab)
self.dataset = dataset
params = Params({
"text_field_embedder": {
"tokens": {
"type": "embedding",
"embedding_dim": 5
}
},
"hidden_size": 7,
"num_layers": 2
})
self.model = SimpleTagger.from_params(self.vocab, params)