class Config(BasePairwiseClassificationModel.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(BasePairwiseClassificationModel.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 decoder: MLPDecoder.Config = MLPDecoder.Config() # TODO: will need to support different output layer for contrastive loss output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config()) encode_relations: 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(ConfigBase): representation: PairRepresentation.Config = PairRepresentation.Config() decoder: MLPDecoder.Config = MLPDecoder.Config() # TODO: will need to support different output layer for contrastive loss 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())
class Config(BaseModel.Config): class InputConfig(ConfigBase): tokens: RoBERTaTensorizer.Config = RoBERTaTensorizer.Config() right_dense: FloatListTensorizer.Config = None left_dense: FloatListTensorizer.Config = None labels: LabelTensorizer.Config = LabelTensorizer.Config() inputs: InputConfig = InputConfig() encoder: RoBERTaEncoderBase.Config = RoBERTaEncoder.Config() decoder: MLPDecoderTwoTower.Config = MLPDecoderTwoTower.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config())
class Config(BaseModel.Config): class EncoderModelInput(BaseModel.Config.ModelInput): tokens: Tensorizer.Config = Tensorizer.Config() dense: Optional[FloatListTensorizer.Config] = None labels: LabelTensorizer.Config = LabelTensorizer.Config() # for metric reporter num_tokens: NtokensTensorizer.Config = NtokensTensorizer.Config( names=["tokens"], indexes=[2]) inputs: EncoderModelInput = EncoderModelInput() encoder: RepresentationBase.Config decoder: MLPDecoder.Config = MLPDecoder.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config())
class Config(BaseModel.Config): class BertModelInput(BaseModel.Config.ModelInput): tokens: BERTTensorizer.Config = BERTTensorizer.Config( max_seq_len=128) dense: Optional[FloatListTensorizer.Config] = None labels: LabelTensorizer.Config = LabelTensorizer.Config() # for metric reporter num_tokens: NtokensTensorizer.Config = NtokensTensorizer.Config( names=["tokens"], indexes=[2]) inputs: BertModelInput = BertModelInput() encoder: TransformerSentenceEncoderBase.Config = ( HuggingFaceBertSentenceEncoder.Config()) decoder: MLPDecoder.Config = MLPDecoder.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config())
class Config(BaseModel.Config): class InputConfig(ConfigBase): right_tokens: RoBERTaTensorizer.Config = RoBERTaTensorizer.Config() left_tokens: RoBERTaTensorizer.Config = RoBERTaTensorizer.Config() right_dense: Optional[FloatListTensorizer.Config] = None left_dense: Optional[FloatListTensorizer.Config] = None labels: LabelTensorizer.Config = LabelTensorizer.Config() inputs: InputConfig = InputConfig() right_encoder: RoBERTaEncoderBase.Config = RoBERTaEncoder.Config() left_encoder: RoBERTaEncoderBase.Config = RoBERTaEncoder.Config() decoder: MLPDecoderTwoTower.Config = MLPDecoderTwoTower.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config()) use_shared_encoder: Optional[bool] = False
class Config(BaseModel.Config): class InputConfig(ConfigBase): right_tokens: RoBERTaTensorizer.Config = RoBERTaTensorizer.Config() left_tokens: RoBERTaTensorizer.Config = RoBERTaTensorizer.Config() right_dense: Optional[FloatListTensorizer.Config] = None left_dense: Optional[FloatListTensorizer.Config] = None labels: LabelTensorizer.Config = LabelTensorizer.Config() inputs: InputConfig = InputConfig() right_encoder: RoBERTaEncoderBase.Config = RoBERTaEncoder.Config() left_encoder: RoBERTaEncoderBase.Config = RoBERTaEncoder.Config() decoder: MLPDecoderTwoTower.Config = MLPDecoderTwoTower.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config()) use_shared_encoder: Optional[bool] = False use_shared_embedding: Optional[bool] = False vocab_size: Optional[int] = 250002 hidden_dim: Optional[int] = 768 padding_idx: Optional[int] = 1
class Config(ConfigBase): representation: SeqRepresentation.Config = SeqRepresentation.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config()) decoder: MLPDecoder.Config = MLPDecoder.Config()
class Config(BaseModel.Config): decoder: MLPDecoder.Config = MLPDecoder.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config() ) encode_relations: bool = True
class Config(BaseModel.Config): decoder: MLPDecoder.Config = MLPDecoder.Config() output_layer: Union[ ClassificationOutputLayer.Config, PairwiseCosineDistanceOutputLayer.Config ] = ClassificationOutputLayer.Config() encode_relations: bool = True