model-X 0.1.2

This package contains collection of models

Model_X

Model_X package is a collection of different NLP architecture models.

Implementation

1. BiLSTM+BiGRU Architectures

a. BiLSTMGRUSpatialDropout1D

input_shape = (100,)
model_input = Input(shape=input_shape)
bilstm_layers = BiLSTMGRUSpatialDropout1D(10, 100)(model_input)
dense_layers = DenseLayerModel()(bilstm_layers)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())

b. BiLSTMGRUSelfAttention

input_shape = (100,)
model_input = Input(shape=input_shape)
bilstm_layers = BiLSTMGRUSelfAttention(10, 100)(model_input)
dense_layers = DenseLayerModel()(bilstm_layers)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())

c. BiLSTMGRUMultiHeadAttention

input_shape = (100,)
model_input = Input(shape=input_shape)
bilstm_layers = BiLSTMGRUMultiHeadAttention(10, 100)(model_input)
dense_layers = DenseLayerModel()(bilstm_layers)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())

d. SplitBiLSTMGRUSpatialDropout1D

input_shape = (100,)
model_input = Input(shape=input_shape)
bilstm_layers = SplitBiLSTMGRUSpatialDropout1D(10, 100)(model_input)
dense_layers = DenseLayerModel()(bilstm_layers)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())

e. SplitBiLSTMGRU

input_shape = (100,)
model_input = Input(shape=input_shape)
bilstm_layers = SplitBiLSTMGRU(10, 100)(model_input)
dense_layers = DenseLayerModel()(bilstm_layers)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())

2. Dense Architectures

a. DenseLayerModel

input_shape = (100,)
model_input = Input(shape=input_shape)
dense_layers = DenseLayerModel()(model_input)
output = Dense(3, activation='softmax')(dense_layers)
full_model = Model(inputs=model_input, outputs=output)
print(full_model.summary())