MatchZoo Model Reference¶
DenseBaseline¶
Model Documentation¶
A simple densely connected baseline model.
- Examples:
>>> model = DenseBaseline() >>> model.params['mlp_num_layers'] = 2 >>> model.params['mlp_num_units'] = 300 >>> model.params['mlp_num_fan_out'] = 128 >>> model.params['mlp_activation_func'] = 'relu' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.dense_baseline.DenseBaseline’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
| 8 | mlp_num_units | Number of units in first mlp_num_layers layers. | 256 | quantitative uniform distribution in [16, 512), with a step size of 1 |
| 9 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 5), with a step size of 1 |
| 10 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 64 | quantitative uniform distribution in [4, 128), with a step size of 4 |
| 11 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu |
DSSM¶
Model Documentation¶
Deep structured semantic model.
- Examples:
>>> model = DSSM() >>> model.params['mlp_num_layers'] = 3 >>> model.params['mlp_num_units'] = 300 >>> model.params['mlp_num_fan_out'] = 128 >>> model.params['mlp_activation_func'] = 'relu' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.dssm.DSSM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
| 3 | mlp_num_units | Number of units in first mlp_num_layers layers. | 128 | quantitative uniform distribution in [8, 256), with a step size of 8 |
| 4 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
| 5 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 64 | quantitative uniform distribution in [4, 128), with a step size of 4 |
| 6 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
| 7 | vocab_size | Size of vocabulary. | 379 |
CDSSM¶
Model Documentation¶
CDSSM Model implementation.
Learning Semantic Representations Using Convolutional Neural Networks for Web Search. (2014a) A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. (2014b)
- Examples:
>>> import matchzoo as mz >>> model = CDSSM() >>> model.params['task'] = mz.tasks.Ranking() >>> model.params['vocab_size'] = 4 >>> model.params['filters'] = 32 >>> model.params['kernel_size'] = 3 >>> model.params['conv_activation_func'] = 'relu' >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.cdssm.CDSSM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
| 3 | mlp_num_units | Number of units in first mlp_num_layers layers. | 128 | quantitative uniform distribution in [8, 256), with a step size of 8 |
| 4 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
| 5 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 64 | quantitative uniform distribution in [4, 128), with a step size of 4 |
| 6 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
| 7 | vocab_size | Size of vocabulary. | 379 | |
| 8 | filters | Number of filters in the 1D convolution layer. | 3 | |
| 9 | kernel_size | Number of kernel size in the 1D convolution layer. | 3 | |
| 10 | strides | Strides in the 1D convolution layer. | 1 | |
| 11 | padding | The padding mode in the convolution layer. It should be one of same, valid, and causal. | 0 | |
| 12 | conv_activation_func | Activation function in the convolution layer. | relu | |
| 13 | dropout_rate | The dropout rate. | 0.3 |
DRMM¶
Model Documentation¶
DRMM Model.
- Examples:
>>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.drmm.DRMM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
| 8 | mlp_num_units | Number of units in first mlp_num_layers layers. | 128 | quantitative uniform distribution in [8, 256), with a step size of 8 |
| 9 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
| 10 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 1 | quantitative uniform distribution in [4, 128), with a step size of 4 |
| 11 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
| 12 | mask_value | The value to be masked from inputs. | 0 | |
| 13 | hist_bin_size | The number of bin size of the histogram. | 30 |
DRMMTKS¶
Model Documentation¶
DRMMTKS Model.
- Examples:
>>> model = DRMMTKS() >>> model.params['top_k'] = 10 >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.drmmtks.DRMMTKS’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
| 8 | mlp_num_units | Number of units in first mlp_num_layers layers. | 128 | quantitative uniform distribution in [8, 256), with a step size of 8 |
| 9 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
| 10 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 1 | quantitative uniform distribution in [4, 128), with a step size of 4 |
| 11 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
| 12 | mask_value | The value to be masked from inputs. | 0 | |
| 13 | top_k | Size of top-k pooling layer. | 10 | quantitative uniform distribution in [2, 100), with a step size of 1 |
ESIM¶
Model Documentation¶
ESIM Model.
- Examples:
>>> model = ESIM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.esim.ESIM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | mask_value | The value to be masked from inputs. | 0 | |
| 8 | dropout | Dropout rate. | 0.2 | |
| 9 | hidden_size | Hidden size. | 200 | |
| 10 | lstm_layer | Number of LSTM layers | 1 | |
| 11 | drop_lstm | Whether dropout LSTM. | False | |
| 12 | concat_lstm | Whether concat intermediate outputs. | True | |
| 13 | rnn_type | Choose rnn type, lstm or gru. | lstm |
KNRM¶
Model Documentation¶
KNRM Model.
- Examples:
>>> model = KNRM() >>> model.params['kernel_num'] = 11 >>> model.params['sigma'] = 0.1 >>> model.params['exact_sigma'] = 0.001 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.knrm.KNRM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | kernel_num | The number of RBF kernels. | 11 | quantitative uniform distribution in [5, 20), with a step size of 1 |
| 8 | sigma | The sigma defines the kernel width. | 0.1 | quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 |
| 9 | exact_sigma | The exact_sigma denotes the sigma for exact match. | 0.001 |
ConvKNRM¶
Model Documentation¶
ConvKNRM Model.
- Examples:
>>> model = ConvKNRM() >>> model.params['filters'] = 128 >>> model.params['conv_activation_func'] = 'tanh' >>> model.params['max_ngram'] = 3 >>> model.params['use_crossmatch'] = True >>> model.params['kernel_num'] = 11 >>> model.params['sigma'] = 0.1 >>> model.params['exact_sigma'] = 0.001 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.conv_knrm.ConvKNRM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | filters | The filter size in the convolution layer. | 128 | |
| 8 | conv_activation_func | The activation function in the convolution layer. | relu | |
| 9 | max_ngram | The maximum length of n-grams for the convolution layer. | 3 | |
| 10 | use_crossmatch | Whether to match left n-grams and right n-grams of different lengths | True | |
| 11 | kernel_num | The number of RBF kernels. | 11 | quantitative uniform distribution in [5, 20), with a step size of 1 |
| 12 | sigma | The sigma defines the kernel width. | 0.1 | quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 |
| 13 | exact_sigma | The exact_sigma denotes the sigma for exact match. | 0.001 |
BiMPM¶
Model Documentation¶
BiMPM Model.
Reference: - https://github.com/galsang/BIMPM-pytorch/blob/master/model/BIMPM.py
- Examples:
>>> model = BiMPM() >>> model.params['num_perspective'] = 4 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.bimpm.BiMPM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | mask_value | The value to be masked from inputs. | 0 | |
| 8 | dropout | Dropout rate. | 0.2 | |
| 9 | hidden_size | Hidden size. | 100 | quantitative uniform distribution in [100, 300), with a step size of 100 |
| 10 | num_perspective | num_perspective | 20 | quantitative uniform distribution in [20, 100), with a step size of 20 |
MatchLSTM¶
Model Documentation¶
MatchLSTM Model.
https://github.com/shuohangwang/mprc/blob/master/qa/rankerReader.lua.
- Examples:
>>> model = MatchLSTM() >>> model.params['dropout'] = 0.2 >>> model.params['hidden_size'] = 200 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.matchlstm.MatchLSTM’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | mask_value | The value to be masked from inputs. | 0 | |
| 8 | dropout | Dropout rate. | 0.2 | |
| 9 | hidden_size | Hidden size. | 200 | |
| 10 | lstm_layer | Number of LSTM layers | 1 | |
| 11 | drop_lstm | Whether dropout LSTM. | False | |
| 12 | concat_lstm | Whether concat intermediate outputs. | True | |
| 13 | rnn_type | Choose rnn type, lstm or gru. | lstm |
ArcII¶
Model Documentation¶
ArcII Model.
Examples: >>> model = ArcII() >>> model.params[‘embedding_output_dim’] = 300 >>> model.params[‘kernel_1d_count’] = 32 >>> model.params[‘kernel_1d_size’] = 3 >>> model.params[‘kernel_2d_count’] = [16, 32] >>> model.params[‘kernel_2d_size’] = [[3, 3], [3, 3]] >>> model.params[‘pool_2d_size’] = [[2, 2], [2, 2]] >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Model Hyper Parameters¶
| Name | Description | Default Value | Default Hyper-Space | |
|---|---|---|---|---|
| 0 | model_class | Model class. Used internally for save/load. Changing this may cause unexpected behaviors. | <class ‘matchzoo.models.arcii.ArcII’> | |
| 1 | task | Decides model output shape, loss, and metrics. | ||
| 2 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
| 3 | embedding | FloatTensor containing weights for the Embedding. | ||
| 4 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
| 5 | embedding_output_dim | Should be set manually. | ||
| 6 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
| 7 | left_length | Length of left input. | 8 | |
| 8 | right_length | Length of right input. | 10 | |
| 9 | kernel_1d_count | Kernel count of 1D convolution layer. | 32 | |
| 10 | kernel_1d_size | Kernel size of 1D convolution layer. | 3 | |
| 11 | kernel_2d_count | Kernel count of 2D convolution layer ineach block | [32] | |
| 12 | kernel_2d_size | Kernel size of 2D convolution layer in each block. | [(3, 3)] | |
| 13 | activation | Activation function. | relu | |
| 14 | pool_2d_size | Size of pooling layer in each block. | [(2, 2)] | |
| 15 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |