class Config(BasePairwiseModel.Config):
"""
Attributes:
encode_relations (bool): if `false`, return the concatenation of the two
representations; if `true`, also concatenate their pairwise absolute
difference and pairwise elementwise product (à la arXiv:1705.02364).
Default: `true`.
tied_representation: whether to use the same representation, with
tied weights, for all the input subrepresentations. Default: `true`.
"""
class ModelInput(BasePairwiseModel.Config.ModelInput):
tokens1: TokenTensorizer.Config = TokenTensorizer.Config(column="text1")
tokens2: TokenTensorizer.Config = TokenTensorizer.Config(column="text2")
labels: LabelTensorizer.Config = LabelTensorizer.Config()
# for metric reporter
raw_text: JoinStringTensorizer.Config = JoinStringTensorizer.Config(
columns=["text1", "text2"]
)
inputs: ModelInput = ModelInput()
embedding: WordEmbedding.Config = WordEmbedding.Config()
representation: Union[
BiLSTMDocAttention.Config, DocNNRepresentation.Config
] = BiLSTMDocAttention.Config()
shared_representations: bool = True
class Config(ConfigBase):
representation: Union[
PureDocAttention.Config, BiLSTMDocAttention.Config,
DocNNRepresentation.Config, ] = BiLSTMDocAttention.Config()
decoder: MLPDecoder.Config = MLPDecoder.Config()
output_layer: ClassificationOutputLayer.Config = (
ClassificationOutputLayer.Config())
class Config(Model.Config):
class ModelInput(Model.Config.ModelInput):
tokens: TokenTensorizer.Config = TokenTensorizer.Config()
labels: LabelTensorizer.Config = LabelTensorizer.Config()
inputs: ModelInput = ModelInput()
embedding: WordEmbedding.Config = WordEmbedding.Config()
representation: Union[
PureDocAttention.Config, BiLSTMDocAttention.Config,
DocNNRepresentation.Config, ] = BiLSTMDocAttention.Config()
decoder: MLPDecoder.Config = MLPDecoder.Config()
output_layer: ClassificationOutputLayer.Config = (
ClassificationOutputLayer.Config())
def _create_dummy_model(self):
return create_model(
DocModel_Deprecated.Config(
representation=BiLSTMDocAttention.Config(
save_path=self.representation_path),
decoder=MLPDecoder.Config(save_path=self.decoder_path),
),
FeatureConfig(
word_feat=WordEmbedding.Config(
embed_dim=300, save_path=self.word_embedding_path),
save_path=self.embedding_path,
),
self._create_dummy_meta_data(),
)
def test_load_save(self):
text_field_meta = FieldMeta()
text_field_meta.vocab = VocabStub()
text_field_meta.vocab_size = 4
text_field_meta.unk_token_idx = 1
text_field_meta.pad_token_idx = 0
text_field_meta.pretrained_embeds_weight = None
label_meta = FieldMeta()
label_meta.vocab = VocabStub()
label_meta.vocab_size = 3
metadata = CommonMetadata()
metadata.features = {DatasetFieldName.TEXT_FIELD: text_field_meta}
metadata.target = label_meta
saved_model = create_model(
DocModel.Config(
representation=BiLSTMDocAttention.Config(
save_path=self.representation_path),
decoder=MLPDecoder.Config(save_path=self.decoder_path),
),
FeatureConfig(save_path=self.embedding_path),
metadata,
)
saved_model.save_modules()
loaded_model = create_model(
DocModel.Config(
representation=BiLSTMDocAttention.Config(
load_path=self.representation_path),
decoder=MLPDecoder.Config(load_path=self.decoder_path),
),
FeatureConfig(load_path=self.embedding_path),
metadata,
)
random_model = create_model(
DocModel.Config(representation=BiLSTMDocAttention.Config(),
decoder=MLPDecoder.Config()),
FeatureConfig(),
metadata,
)
# Loaded and saved modules should be equal. Neither should be equal to
# a randomly initialised model.
for p1, p2, p3 in itertools.zip_longest(
saved_model.embedding.parameters(),
loaded_model.embedding.parameters(),
random_model.embedding.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))
for p1, p2, p3 in itertools.zip_longest(
saved_model.representation.parameters(),
loaded_model.representation.parameters(),
random_model.representation.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))
for p1, p2, p3 in itertools.zip_longest(
saved_model.decoder.parameters(),
loaded_model.decoder.parameters(),
random_model.decoder.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))