Labeling Models

Table of Contents

• Labeling Models

  • Bidirectional LSTM Model

  • Bidirectional GRU Model

  • Bidirectional LSTM CRF Model

  • Bidirectional GRU CRF Model

  • Bidirectional CNN LSTM Model

Bidirectional LSTM Model

class kashgari.tasks.labeling.BiLSTM_Model(embedding=None, sequence_length=None, hyper_parameters=None)

Bases: kashgari.tasks.labeling.abc_model.ABCLabelingModel

__init__(embedding=None, sequence_length=None, hyper_parameters=None)

Parameters

• embedding (kashgari.embeddings.abc_embedding.ABCEmbedding) – embedding object

• sequence_length (int) – target sequence length

• hyper_parameters (Dict[str, Dict[str, Any]]) – hyper_parameters to overwrite

build_model(x_data, y_data)

Build Model with x_data and y_data

This function will setup a CorpusGenerator,

then call ABCClassificationModel.build_model_gen() for preparing processor and model

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

Return type

None

Returns:

build_model_arc()

Return type

None

build_model_generator(generators)

Parameters

generators (List[kashgari.generators.CorpusGenerator]) –

Return type

None

compile_model(loss=None, optimizer=None, metrics=None, **kwargs)

Configures the model for training. call tf.keras.Model.predict() to compile model with custom loss, optimizer and metrics

Examples

>>> model = BiLSTM_Model()
# Build model with corpus
>>> model.build_model(train_x, train_y)
# Compile model with custom loss, optimizer and metrics
>>> model.compile(loss='categorical_crossentropy', optimizer='rsm', metrics = ['accuracy'])

Parameters

• loss (Any) – name of objective function, objective function or tf.keras.losses.Loss instance.

• optimizer (Any) – name of optimizer or optimizer instance.

• metrics (object) – List of metrics to be evaluated by the model during training and testing.

• kwargs (Any) – additional params passed to tf.keras.Model.predict`().

Return type

None

classmethod default_hyper_parameters()

The default hyper parameters of the model dict, all models must implement this function.

You could easily change model’s hyper-parameters.

For example, change the LSTM unit in BiLSTM_Model from 128 to 32.

>>> from kashgari.tasks.classification import BiLSTM_Model
>>> hyper = BiLSTM_Model.default_hyper_parameters()
>>> print(hyper)
{'layer_bi_lstm': {'units': 128, 'return_sequences': False}, 'layer_output': {}}
>>> hyper['layer_bi_lstm']['units'] = 32
>>> model = BiLSTM_Model(hyper_parameters=hyper)

Returns

hyper params dict

Return type

Dict[str, Dict[str, Any]]

evaluate(x_data, y_data, batch_size=32, digits=4, truncating=False)

Build a text report showing the main labeling metrics.

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

• batch_size (int) –

• digits (int) –

• truncating (bool) –

Returns

A report dict

Return type

Dict

Example

>>> from kashgari.tasks.labeling import BiGRU_Model
>>> model = BiGRU_Model()
>>> model.fit(train_x, train_y, valid_x, valid_y)
>>> report = model.evaluate(test_x, test_y)
           precision    recall  f1-score   support

          ORG     0.0665    0.1108    0.0831       984
          LOC     0.1870    0.2086    0.1972      1951
          PER     0.1685    0.0882    0.1158       884

    micro avg     0.1384    0.1555    0.1465      3819
    macro avg     0.1516    0.1555    0.1490      3819

>>> print(report)
    {
     'f1-score': 0.14895159934887792,
     'precision': 0.1516294012813676,
     'recall': 0.15553809897879026,
     'support': 3819,
     'detail': {'LOC': {'f1-score': 0.19718992248062014,
                        'precision': 0.18695452457510336,
                        'recall': 0.20861096873398258,
                        'support': 1951},
                'ORG': {'f1-score': 0.08307926829268293,
                        'precision': 0.06646341463414634,
                        'recall': 0.11077235772357724,
                        'support': 984},
                'PER': {'f1-score': 0.11581291759465479,
                        'precision': 0.16846652267818574,
                        'recall': 0.08823529411764706,
                        'support': 884}},
    }

fit(x_train, y_train, x_validate=None, y_validate=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data set list.

Parameters

• x_train (List[List[str]]) – Array of train feature data (if the model has a single input), or tuple of train feature data array (if the model has multiple inputs)

• y_train (List[List[str]]) – Array of train label data

• x_validate (List[List[str]]) – Array of validation feature data (if the model has a single input), or tuple of validation feature data array (if the model has multiple inputs)

• y_validate (List[List[str]]) – Array of validation label data

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tensorflow.python.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

fit_generator(train_sample_gen, valid_sample_gen=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data generator.

Data generator must be the subclass of CorpusGenerator

Parameters

• train_sample_gen (kashgari.generators.CorpusGenerator) – train data generator.

• valid_sample_gen (kashgari.generators.CorpusGenerator) – valid data generator.

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tf.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

classmethod load_model(model_path, custom_objects=None, encoding='utf-8')

Parameters

• model_path (str) –

• custom_objects (Dict) –

• encoding (str) –

Return type

Union[ABCLabelingModel, ABCClassificationModel]

predict(x_data, *, batch_size=32, truncating=False, predict_kwargs=None)

Generates output predictions for the input samples.

Computation is done in batches.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

array(s) of predictions.

Return type

List[List[str]]

predict_entities(x_data, batch_size=32, join_chunk=' ', truncating=False, predict_kwargs=None)

Gets entities from sequence.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• join_chunk (str) – str or False,

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

list of entity.

Return type

list

save(model_path, encoding='utf-8')

Parameters

• model_path (str) –

• encoding (str) –

Return type

str

to_dict()

Return type

Dict[str, Any]

Bidirectional GRU Model

class kashgari.tasks.labeling.BiGRU_Model(embedding=None, sequence_length=None, hyper_parameters=None)

Bases: kashgari.tasks.labeling.abc_model.ABCLabelingModel

__init__(embedding=None, sequence_length=None, hyper_parameters=None)

Parameters

• embedding (kashgari.embeddings.abc_embedding.ABCEmbedding) – embedding object

• sequence_length (int) – target sequence length

• hyper_parameters (Dict[str, Dict[str, Any]]) – hyper_parameters to overwrite

build_model(x_data, y_data)

Build Model with x_data and y_data

This function will setup a CorpusGenerator,

then call ABCClassificationModel.build_model_gen() for preparing processor and model

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

Return type

None

Returns:

build_model_arc()

Return type

None

build_model_generator(generators)

Parameters

generators (List[kashgari.generators.CorpusGenerator]) –

Return type

None

compile_model(loss=None, optimizer=None, metrics=None, **kwargs)

Configures the model for training. call tf.keras.Model.predict() to compile model with custom loss, optimizer and metrics

Examples

>>> model = BiLSTM_Model()
# Build model with corpus
>>> model.build_model(train_x, train_y)
# Compile model with custom loss, optimizer and metrics
>>> model.compile(loss='categorical_crossentropy', optimizer='rsm', metrics = ['accuracy'])

Parameters

• loss (Any) – name of objective function, objective function or tf.keras.losses.Loss instance.

• optimizer (Any) – name of optimizer or optimizer instance.

• metrics (object) – List of metrics to be evaluated by the model during training and testing.

• kwargs (Any) – additional params passed to tf.keras.Model.predict`().

Return type

None

classmethod default_hyper_parameters()

The default hyper parameters of the model dict, all models must implement this function.

You could easily change model’s hyper-parameters.

For example, change the LSTM unit in BiLSTM_Model from 128 to 32.

>>> from kashgari.tasks.classification import BiLSTM_Model
>>> hyper = BiLSTM_Model.default_hyper_parameters()
>>> print(hyper)
{'layer_bi_lstm': {'units': 128, 'return_sequences': False}, 'layer_output': {}}
>>> hyper['layer_bi_lstm']['units'] = 32
>>> model = BiLSTM_Model(hyper_parameters=hyper)

Returns

hyper params dict

Return type

Dict[str, Dict[str, Any]]

evaluate(x_data, y_data, batch_size=32, digits=4, truncating=False)

Build a text report showing the main labeling metrics.

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

• batch_size (int) –

• digits (int) –

• truncating (bool) –

Returns

A report dict

Return type

Dict

Example

>>> from kashgari.tasks.labeling import BiGRU_Model
>>> model = BiGRU_Model()
>>> model.fit(train_x, train_y, valid_x, valid_y)
>>> report = model.evaluate(test_x, test_y)
           precision    recall  f1-score   support

          ORG     0.0665    0.1108    0.0831       984
          LOC     0.1870    0.2086    0.1972      1951
          PER     0.1685    0.0882    0.1158       884

    micro avg     0.1384    0.1555    0.1465      3819
    macro avg     0.1516    0.1555    0.1490      3819

>>> print(report)
    {
     'f1-score': 0.14895159934887792,
     'precision': 0.1516294012813676,
     'recall': 0.15553809897879026,
     'support': 3819,
     'detail': {'LOC': {'f1-score': 0.19718992248062014,
                        'precision': 0.18695452457510336,
                        'recall': 0.20861096873398258,
                        'support': 1951},
                'ORG': {'f1-score': 0.08307926829268293,
                        'precision': 0.06646341463414634,
                        'recall': 0.11077235772357724,
                        'support': 984},
                'PER': {'f1-score': 0.11581291759465479,
                        'precision': 0.16846652267818574,
                        'recall': 0.08823529411764706,
                        'support': 884}},
    }

fit(x_train, y_train, x_validate=None, y_validate=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data set list.

Parameters

• x_train (List[List[str]]) – Array of train feature data (if the model has a single input), or tuple of train feature data array (if the model has multiple inputs)

• y_train (List[List[str]]) – Array of train label data

• x_validate (List[List[str]]) – Array of validation feature data (if the model has a single input), or tuple of validation feature data array (if the model has multiple inputs)

• y_validate (List[List[str]]) – Array of validation label data

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tensorflow.python.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

fit_generator(train_sample_gen, valid_sample_gen=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data generator.

Data generator must be the subclass of CorpusGenerator

Parameters

• train_sample_gen (kashgari.generators.CorpusGenerator) – train data generator.

• valid_sample_gen (kashgari.generators.CorpusGenerator) – valid data generator.

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tf.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

classmethod load_model(model_path, custom_objects=None, encoding='utf-8')

Parameters

• model_path (str) –

• custom_objects (Dict) –

• encoding (str) –

Return type

Union[ABCLabelingModel, ABCClassificationModel]

predict(x_data, *, batch_size=32, truncating=False, predict_kwargs=None)

Generates output predictions for the input samples.

Computation is done in batches.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

array(s) of predictions.

Return type

List[List[str]]

predict_entities(x_data, batch_size=32, join_chunk=' ', truncating=False, predict_kwargs=None)

Gets entities from sequence.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• join_chunk (str) – str or False,

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

list of entity.

Return type

list

save(model_path, encoding='utf-8')

Parameters

• model_path (str) –

• encoding (str) –

Return type

str

to_dict()

Return type

Dict[str, Any]

Bidirectional LSTM CRF Model

class kashgari.tasks.labeling.BiLSTM_CRF_Model(embedding=None, sequence_length=None, hyper_parameters=None)

Bases: kashgari.tasks.labeling.abc_model.ABCLabelingModel

__init__(embedding=None, sequence_length=None, hyper_parameters=None)

Parameters

• embedding (kashgari.embeddings.abc_embedding.ABCEmbedding) – embedding object

• sequence_length (int) – target sequence length

• hyper_parameters (Dict[str, Dict[str, Any]]) – hyper_parameters to overwrite

build_model(x_data, y_data)

Build Model with x_data and y_data

This function will setup a CorpusGenerator,

then call ABCClassificationModel.build_model_gen() for preparing processor and model

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

Return type

None

Returns:

build_model_arc()

Return type

None

build_model_generator(generators)

Parameters

generators (List[kashgari.generators.CorpusGenerator]) –

Return type

None

compile_model(loss=None, optimizer=None, metrics=None, **kwargs)

Configures the model for training. call tf.keras.Model.predict() to compile model with custom loss, optimizer and metrics

Examples

>>> model = BiLSTM_Model()
# Build model with corpus
>>> model.build_model(train_x, train_y)
# Compile model with custom loss, optimizer and metrics
>>> model.compile(loss='categorical_crossentropy', optimizer='rsm', metrics = ['accuracy'])

Parameters

• loss (Any) – name of objective function, objective function or tf.keras.losses.Loss instance.

• optimizer (Any) – name of optimizer or optimizer instance.

• metrics (object) – List of metrics to be evaluated by the model during training and testing.

• kwargs (Any) – additional params passed to tf.keras.Model.predict`().

Return type

None

classmethod default_hyper_parameters()

The default hyper parameters of the model dict, all models must implement this function.

You could easily change model’s hyper-parameters.

For example, change the LSTM unit in BiLSTM_Model from 128 to 32.

>>> from kashgari.tasks.classification import BiLSTM_Model
>>> hyper = BiLSTM_Model.default_hyper_parameters()
>>> print(hyper)
{'layer_bi_lstm': {'units': 128, 'return_sequences': False}, 'layer_output': {}}
>>> hyper['layer_bi_lstm']['units'] = 32
>>> model = BiLSTM_Model(hyper_parameters=hyper)

Returns

hyper params dict

Return type

Dict[str, Dict[str, Any]]

evaluate(x_data, y_data, batch_size=32, digits=4, truncating=False)

Build a text report showing the main labeling metrics.

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

• batch_size (int) –

• digits (int) –

• truncating (bool) –

Returns

A report dict

Return type

Dict

Example

>>> from kashgari.tasks.labeling import BiGRU_Model
>>> model = BiGRU_Model()
>>> model.fit(train_x, train_y, valid_x, valid_y)
>>> report = model.evaluate(test_x, test_y)
           precision    recall  f1-score   support

          ORG     0.0665    0.1108    0.0831       984
          LOC     0.1870    0.2086    0.1972      1951
          PER     0.1685    0.0882    0.1158       884

    micro avg     0.1384    0.1555    0.1465      3819
    macro avg     0.1516    0.1555    0.1490      3819

>>> print(report)
    {
     'f1-score': 0.14895159934887792,
     'precision': 0.1516294012813676,
     'recall': 0.15553809897879026,
     'support': 3819,
     'detail': {'LOC': {'f1-score': 0.19718992248062014,
                        'precision': 0.18695452457510336,
                        'recall': 0.20861096873398258,
                        'support': 1951},
                'ORG': {'f1-score': 0.08307926829268293,
                        'precision': 0.06646341463414634,
                        'recall': 0.11077235772357724,
                        'support': 984},
                'PER': {'f1-score': 0.11581291759465479,
                        'precision': 0.16846652267818574,
                        'recall': 0.08823529411764706,
                        'support': 884}},
    }

fit(x_train, y_train, x_validate=None, y_validate=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data set list.

Parameters

• x_train (List[List[str]]) – Array of train feature data (if the model has a single input), or tuple of train feature data array (if the model has multiple inputs)

• y_train (List[List[str]]) – Array of train label data

• x_validate (List[List[str]]) – Array of validation feature data (if the model has a single input), or tuple of validation feature data array (if the model has multiple inputs)

• y_validate (List[List[str]]) – Array of validation label data

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tensorflow.python.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

fit_generator(train_sample_gen, valid_sample_gen=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data generator.

Data generator must be the subclass of CorpusGenerator

Parameters

• train_sample_gen (kashgari.generators.CorpusGenerator) – train data generator.

• valid_sample_gen (kashgari.generators.CorpusGenerator) – valid data generator.

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tf.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

classmethod load_model(model_path, custom_objects=None, encoding='utf-8')

Parameters

• model_path (str) –

• custom_objects (Dict) –

• encoding (str) –

Return type

Union[ABCLabelingModel, ABCClassificationModel]

predict(x_data, *, batch_size=32, truncating=False, predict_kwargs=None)

Generates output predictions for the input samples.

Computation is done in batches.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

array(s) of predictions.

Return type

List[List[str]]

predict_entities(x_data, batch_size=32, join_chunk=' ', truncating=False, predict_kwargs=None)

Gets entities from sequence.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• join_chunk (str) – str or False,

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

list of entity.

Return type

list

save(model_path, encoding='utf-8')

Parameters

• model_path (str) –

• encoding (str) –

Return type

str

to_dict()

Return type

Dict[str, Any]

Bidirectional GRU CRF Model

class kashgari.tasks.labeling.BiGRU_CRF_Model(embedding=None, sequence_length=None, hyper_parameters=None)

Bases: kashgari.tasks.labeling.abc_model.ABCLabelingModel

__init__(embedding=None, sequence_length=None, hyper_parameters=None)

Parameters

• embedding (kashgari.embeddings.abc_embedding.ABCEmbedding) – embedding object

• sequence_length (int) – target sequence length

• hyper_parameters (Dict[str, Dict[str, Any]]) – hyper_parameters to overwrite

build_model(x_data, y_data)

Build Model with x_data and y_data

This function will setup a CorpusGenerator,

then call ABCClassificationModel.build_model_gen() for preparing processor and model

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

Return type

None

Returns:

build_model_arc()

Return type

None

build_model_generator(generators)

Parameters

generators (List[kashgari.generators.CorpusGenerator]) –

Return type

None

compile_model(loss=None, optimizer=None, metrics=None, **kwargs)

Configures the model for training. call tf.keras.Model.predict() to compile model with custom loss, optimizer and metrics

Examples

>>> model = BiLSTM_Model()
# Build model with corpus
>>> model.build_model(train_x, train_y)
# Compile model with custom loss, optimizer and metrics
>>> model.compile(loss='categorical_crossentropy', optimizer='rsm', metrics = ['accuracy'])

Parameters

• loss (Any) – name of objective function, objective function or tf.keras.losses.Loss instance.

• optimizer (Any) – name of optimizer or optimizer instance.

• metrics (object) – List of metrics to be evaluated by the model during training and testing.

• kwargs (Any) – additional params passed to tf.keras.Model.predict`().

Return type

None

classmethod default_hyper_parameters()

The default hyper parameters of the model dict, all models must implement this function.

You could easily change model’s hyper-parameters.

For example, change the LSTM unit in BiLSTM_Model from 128 to 32.

>>> from kashgari.tasks.classification import BiLSTM_Model
>>> hyper = BiLSTM_Model.default_hyper_parameters()
>>> print(hyper)
{'layer_bi_lstm': {'units': 128, 'return_sequences': False}, 'layer_output': {}}
>>> hyper['layer_bi_lstm']['units'] = 32
>>> model = BiLSTM_Model(hyper_parameters=hyper)

Returns

hyper params dict

Return type

Dict[str, Dict[str, Any]]

evaluate(x_data, y_data, batch_size=32, digits=4, truncating=False)

Build a text report showing the main labeling metrics.

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

• batch_size (int) –

• digits (int) –

• truncating (bool) –

Returns

A report dict

Return type

Dict

Example

>>> from kashgari.tasks.labeling import BiGRU_Model
>>> model = BiGRU_Model()
>>> model.fit(train_x, train_y, valid_x, valid_y)
>>> report = model.evaluate(test_x, test_y)
           precision    recall  f1-score   support

          ORG     0.0665    0.1108    0.0831       984
          LOC     0.1870    0.2086    0.1972      1951
          PER     0.1685    0.0882    0.1158       884

    micro avg     0.1384    0.1555    0.1465      3819
    macro avg     0.1516    0.1555    0.1490      3819

>>> print(report)
    {
     'f1-score': 0.14895159934887792,
     'precision': 0.1516294012813676,
     'recall': 0.15553809897879026,
     'support': 3819,
     'detail': {'LOC': {'f1-score': 0.19718992248062014,
                        'precision': 0.18695452457510336,
                        'recall': 0.20861096873398258,
                        'support': 1951},
                'ORG': {'f1-score': 0.08307926829268293,
                        'precision': 0.06646341463414634,
                        'recall': 0.11077235772357724,
                        'support': 984},
                'PER': {'f1-score': 0.11581291759465479,
                        'precision': 0.16846652267818574,
                        'recall': 0.08823529411764706,
                        'support': 884}},
    }

fit(x_train, y_train, x_validate=None, y_validate=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data set list.

Parameters

• x_train (List[List[str]]) – Array of train feature data (if the model has a single input), or tuple of train feature data array (if the model has multiple inputs)

• y_train (List[List[str]]) – Array of train label data

• x_validate (List[List[str]]) – Array of validation feature data (if the model has a single input), or tuple of validation feature data array (if the model has multiple inputs)

• y_validate (List[List[str]]) – Array of validation label data

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tensorflow.python.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

fit_generator(train_sample_gen, valid_sample_gen=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data generator.

Data generator must be the subclass of CorpusGenerator

Parameters

• train_sample_gen (kashgari.generators.CorpusGenerator) – train data generator.

• valid_sample_gen (kashgari.generators.CorpusGenerator) – valid data generator.

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tf.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

classmethod load_model(model_path, custom_objects=None, encoding='utf-8')

Parameters

• model_path (str) –

• custom_objects (Dict) –

• encoding (str) –

Return type

Union[ABCLabelingModel, ABCClassificationModel]

predict(x_data, *, batch_size=32, truncating=False, predict_kwargs=None)

Generates output predictions for the input samples.

Computation is done in batches.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

array(s) of predictions.

Return type

List[List[str]]

predict_entities(x_data, batch_size=32, join_chunk=' ', truncating=False, predict_kwargs=None)

Gets entities from sequence.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• join_chunk (str) – str or False,

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

list of entity.

Return type

list

save(model_path, encoding='utf-8')

Parameters

• model_path (str) –

• encoding (str) –

Return type

str

to_dict()

Return type

Dict[str, Any]

Bidirectional CNN LSTM Model

class kashgari.tasks.labeling.CNN_LSTM_Model(embedding=None, sequence_length=None, hyper_parameters=None)

Bases: kashgari.tasks.labeling.abc_model.ABCLabelingModel

__init__(embedding=None, sequence_length=None, hyper_parameters=None)

Parameters

• embedding (kashgari.embeddings.abc_embedding.ABCEmbedding) – embedding object

• sequence_length (int) – target sequence length

• hyper_parameters (Dict[str, Dict[str, Any]]) – hyper_parameters to overwrite

build_model(x_data, y_data)

Build Model with x_data and y_data

This function will setup a CorpusGenerator,

then call ABCClassificationModel.build_model_gen() for preparing processor and model

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

Return type

None

Returns:

build_model_arc()

Return type

None

build_model_generator(generators)

Parameters

generators (List[kashgari.generators.CorpusGenerator]) –

Return type

None

compile_model(loss=None, optimizer=None, metrics=None, **kwargs)

Configures the model for training. call tf.keras.Model.predict() to compile model with custom loss, optimizer and metrics

Examples

>>> model = BiLSTM_Model()
# Build model with corpus
>>> model.build_model(train_x, train_y)
# Compile model with custom loss, optimizer and metrics
>>> model.compile(loss='categorical_crossentropy', optimizer='rsm', metrics = ['accuracy'])

Parameters

• loss (Any) – name of objective function, objective function or tf.keras.losses.Loss instance.

• optimizer (Any) – name of optimizer or optimizer instance.

• metrics (object) – List of metrics to be evaluated by the model during training and testing.

• kwargs (Any) – additional params passed to tf.keras.Model.predict`().

Return type

None

classmethod default_hyper_parameters()

The default hyper parameters of the model dict, all models must implement this function.

You could easily change model’s hyper-parameters.

For example, change the LSTM unit in BiLSTM_Model from 128 to 32.

>>> from kashgari.tasks.classification import BiLSTM_Model
>>> hyper = BiLSTM_Model.default_hyper_parameters()
>>> print(hyper)
{'layer_bi_lstm': {'units': 128, 'return_sequences': False}, 'layer_output': {}}
>>> hyper['layer_bi_lstm']['units'] = 32
>>> model = BiLSTM_Model(hyper_parameters=hyper)

Returns

hyper params dict

Return type

Dict[str, Dict[str, Any]]

evaluate(x_data, y_data, batch_size=32, digits=4, truncating=False)

Build a text report showing the main labeling metrics.

Parameters

• x_data (List[List[str]]) –

• y_data (List[List[str]]) –

• batch_size (int) –

• digits (int) –

• truncating (bool) –

Returns

A report dict

Return type

Dict

Example

>>> from kashgari.tasks.labeling import BiGRU_Model
>>> model = BiGRU_Model()
>>> model.fit(train_x, train_y, valid_x, valid_y)
>>> report = model.evaluate(test_x, test_y)
           precision    recall  f1-score   support

          ORG     0.0665    0.1108    0.0831       984
          LOC     0.1870    0.2086    0.1972      1951
          PER     0.1685    0.0882    0.1158       884

    micro avg     0.1384    0.1555    0.1465      3819
    macro avg     0.1516    0.1555    0.1490      3819

>>> print(report)
    {
     'f1-score': 0.14895159934887792,
     'precision': 0.1516294012813676,
     'recall': 0.15553809897879026,
     'support': 3819,
     'detail': {'LOC': {'f1-score': 0.19718992248062014,
                        'precision': 0.18695452457510336,
                        'recall': 0.20861096873398258,
                        'support': 1951},
                'ORG': {'f1-score': 0.08307926829268293,
                        'precision': 0.06646341463414634,
                        'recall': 0.11077235772357724,
                        'support': 984},
                'PER': {'f1-score': 0.11581291759465479,
                        'precision': 0.16846652267818574,
                        'recall': 0.08823529411764706,
                        'support': 884}},
    }

fit(x_train, y_train, x_validate=None, y_validate=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data set list.

Parameters

• x_train (List[List[str]]) – Array of train feature data (if the model has a single input), or tuple of train feature data array (if the model has multiple inputs)

• y_train (List[List[str]]) – Array of train label data

• x_validate (List[List[str]]) – Array of validation feature data (if the model has a single input), or tuple of validation feature data array (if the model has multiple inputs)

• y_validate (List[List[str]]) – Array of validation label data

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tensorflow.python.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

fit_generator(train_sample_gen, valid_sample_gen=None, batch_size=64, epochs=5, callbacks=None, fit_kwargs=None)

Trains the model for a given number of epochs with given data generator.

Data generator must be the subclass of CorpusGenerator

Parameters

• train_sample_gen (kashgari.generators.CorpusGenerator) – train data generator.

• valid_sample_gen (kashgari.generators.CorpusGenerator) – valid data generator.

• batch_size (int) – Number of samples per gradient update, default to 64.

• epochs (int) – Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.

• callbacks (List[tf.keras.callbacks.Callback]) – List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

• fit_kwargs (Dict) – fit_kwargs: additional arguments passed to tf.keras.Model.fit()

Returns

A tf.keras.callback.History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Return type

tensorflow.python.keras.callbacks.History

classmethod load_model(model_path, custom_objects=None, encoding='utf-8')

Parameters

• model_path (str) –

• custom_objects (Dict) –

• encoding (str) –

Return type

Union[ABCLabelingModel, ABCClassificationModel]

predict(x_data, *, batch_size=32, truncating=False, predict_kwargs=None)

Generates output predictions for the input samples.

Computation is done in batches.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

array(s) of predictions.

Return type

List[List[str]]

predict_entities(x_data, batch_size=32, join_chunk=' ', truncating=False, predict_kwargs=None)

Gets entities from sequence.

Parameters

• x_data (List[List[str]]) – The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).

• batch_size (int) – Integer. If unspecified, it will default to 32.

• truncating (bool) – remove values from sequences larger than model.embedding.sequence_length

• join_chunk (str) – str or False,

• predict_kwargs (Dict) – arguments passed to tf.keras.Model.predict()

Returns

list of entity.

Return type

list

save(model_path, encoding='utf-8')

Parameters

• model_path (str) –

• encoding (str) –

Return type

str

to_dict()

Return type

Dict[str, Any]