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EntityRecognizer | ArcGIS API for Python

class arcgis.learn.text.EntityRecognizer(data=None, lang='en', backbone='spacy', **kwargs)

Creates an entity recognition model to extract text entities from unstructured text documents.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

kwargs

Parameter

Description

verbose

Optional string. Default set to error. The log level you want to set. It means the amount of information you want to display while training or calling the various methods of this class. Allowed values are - debug, info, warning, error and critical. Applicable only for models with HuggingFace transformer backbones.

seq_len

Optional Integer. Default set to 512. Maximum sequence length (at sub-word level after tokenization) of the training data to be considered for training the model. Applicable only for models with HuggingFace transformer backbones.

mixed_precision

Optional Bool. Default set to False. If set True, then mixed precision training is used to train the model. Applicable only for models with HuggingFace transformer backbones.

pretrained_path

Optional String. Path where pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

prompt

Optional String. This parameter is applicable if the selected model backbone is from the LLM family.

This parameter outlines the task and its corresponding guardrails.

examples

Optional List. The list comprises tuple(s) where the first element denotes the text for entity extraction, while the second element is a dictionary used for mapping named entities.

This parameter is applicable if the selected model backbone is from the LLM family.

Pydantic Schema: List[Tuple[str, Dict[str, List]]]

Example: [(“Jim stays in London”, {“name”: [“Jim”], “location”: [“London”]})]

If examples are not supplied, a data object must be provided.

Returns:

EntityRecognizer Object

classmethod available_backbone_models(architecture)

Get available models for the given entity recognition backbone

Returns:

a tuple containing the available models for the given entity recognition backbone

property available_metrics

List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

extract_entities(text_list, drop=True, batch_size=4, show_progress=True, **kwargs) → DataFrame | FeatureSet

Extracts the entities from [documents in the mentioned path or text_list].

Field defined as ‘address_tag’ in prepare_data() function’s class mapping attribute will be treated as a location. In cases where trained model extracts multiple locations from a single document, that document will be replicated for each location in the resulting dataframe.

Parameter

Description

text_list

Required string(path) or list(documents). List of documents for entity extraction OR path to the documents.

drop

Optional bool. If documents without address needs to be dropped from the results. Default is set to True.

batch_size

Optional integer. Number of items to process at once. (Reduce it if getting CUDA Out of Memory Errors). Default is set to 4. Not applicable for models with spaCy backbone.

show_progress

Optional Bool. If set to True, will display a progress bar depicting the items processed so far. Applicable only when a list of text is passed.

kwargs

Parameter

Description

input_field

Optional string. Input field name in the feature set. Supported in model extension. Default value: input_str

Returns:

Pandas DataFrame

f1_score()

Calculate F1 score of the trained model

fit(epochs=20, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

epochs

Required integer. Number of cycles of training on the data. Increase it if underfitting.

lr

Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model.

Note

Passing slice of floats as lr value is not supported for models with spaCy backbone.

one_cycle

Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.

Note

Not applicable for models with spaCy backbone

early_stopping

Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs.

Note

Not applicable for models with spaCy backbone

checkpoint

Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.

Note

Not applicable for models with spaCy backbone

tensorboard

Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1

The default value is ‘False’.

Note

Not applicable for Text Models

monitor

Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

Note

Not applicable for models with spaCy backbone

freeze()

Freeze up to last layer group to train only the last layer group of the model.

This method is not supported when the backbone is configured as llm/mistral.

classmethod from_model(emd_path, data=None, **kwargs)

Creates an EntityRecognizer model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using this method.

Returns:

EntityRecognizer Object

classmethod from_pretrained(backbone, **kwargs)

Creates an EntityRecognizer model object from an already fine-tuned Hugging Face Transformer backbone.

This method is not supported when the backbone is configured as llm/mistral.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

Parameter

Description

backbone

Required string. Specify the Hugging Face Transformer backbone name fine-tuned on Named Entity Recognition(NER)/ Token Classification task.

To get more details on available transformer models fine-tuned on Named Entity Recognition(NER) Task, kindly visit:- https://huggingface.co/models?pipeline_tag=token-classification

kwargs

Parameter

Description

verbose

Optional string. Default set to error. The log level you want to set. It means the amount of information you want to display while calling the various methods of this class. Allowed values are - debug, info, warning, error and critical.

Returns:

EntityRecognizer Object

load(name_or_path)

Loads a saved EntityRecognizer model from disk.

This method is not supported when the backbone is configured as llm/mistral.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

Parameter

Description

name_or_path

Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True, **kwargs)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

allow_plot

Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

metrics_per_label()

Calculate precision, recall & F1 scores per labels/entities for which the model was trained on

plot_losses(show=True)

Plot training and validation losses.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

show

Optional bool. Defaults to True If set to False, figure will not be plotted but will be returned, when set to True function will plot the figure and return nothing.

Returns:

matplotlib.figure.Figure

precision_score()

Calculate precision score of the trained model

recall_score()

Calculate recall score of the trained model

save(name_or_path, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter

Description

name_or_path

Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.

publish

Optional boolean. Publishes the DLPK as an item. Default is set to False.

gis

Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken.

compute_metrics

Optional boolean. Used for computing model metrics. Default is set to True.

save_optimizer

Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False Not applicable for models with spaCy backbone.

kwargs

Parameter

Description

overwrite

Optional boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

zip_files

Optional boolean zip_files if True, it will create the Deep Learning Package (DLPK) file while saving the model.

show_results(rows=5, ds_type='valid')

Runs entity extraction on a random batch from the mentioned ds_type.

Parameter

Description

ds_type

Optional string, defaults to valid.

rows

Optional integer, defaults to 5. Number of rows to print.

Returns:

Pandas DataFrame

supported_backbones = ['spacy', 'BERT', 'RoBERTa', 'DistilBERT', 'ALBERT', 'CamemBERT', 'MobileBERT', 'XLNet', 'XLM', 'XLM-RoBERTa', 'FlauBERT', 'ELECTRA', 'Longformer', 'Funnel', 'LLM']

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

This method is not supported when the backbone is configured as llm/mistral.

class arcgis.learn.text.TextClassifier(data, backbone='bert-base-cased', **kwargs)

Creates a TextClassifier Object.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

Based on the Hugging Face transformers library

kwargs

Parameter

Description

verbose

Optional string. Default set to error. The log level you want to set. It means the amount of information you want to display while training or calling the various methods of this class. Allowed values are - debug, info, warning, error and critical.

seq_len

Optional Integer. Default set to 512. Maximum sequence length (at sub-word level after tokenization) of the training data to be considered for training the model.

thresh

Optional Float. This parameter is used to set the threshold value to pick labels in case of multi-label text classification problem. Default value is set to 0.25

mixed_precision

Optional Bool. Default set to False. If set True, then mixed precision training is used to train the model

pretrained_path

Optional String. Path where pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

prompt

Optional String. This parameter is applicable if the selected model backbone is from the LLM family.

This parameter outlines the task and its corresponding guardrails.

examples

Optional dictionary. The dictionary’s keys represent labels or classes, with the corresponding values being lists of sentences belonging to each class.

This parameter is applicable if the selected model backbone is from the LLM family.

Pydantic notation

Optional[Dict[str, List]]

Example:

{

“Label_1” :[example 1, example 2],

“Label_2” : [example 1, example 2]

}

If examples are not supplied, a data object must be provided.

Returns:

TextClassifier Object

accuracy()

Calculates the following metric:

• accuracy: the number of correctly predicted labels in the validation set divided by the total number of items in the validation set

Returns:

a floating point number depicting the accuracy of the classification model.

classmethod available_backbone_models(architecture)

Get available models for the given transformer backbone

Returns:

a tuple containing the available models for the given transformer backbone

property available_metrics

List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

epochs

Required integer. Number of cycles of training on the data. Increase it if underfitting.

lr

Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model.

one_cycle

Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.

early_stopping

Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.

checkpoint

Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.

tensorboard

Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1

The default value is ‘False’.

Note

Not applicable for Text Models

monitor

Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

freeze()

Freeze up to last layer group to train only the last layer group of the model.

This method is not supported when the backbone is configured as llm/mistral.

classmethod from_model(emd_path, data=None, **kwargs)

Creates an TextClassifier model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using this method.

Returns:

TextClassifier model Object

classmethod from_pretrained(backbone, **kwargs)

Creates an TextClassifier model object from an already fine-tuned Hugging Face Transformer backbone.

This method is not supported when the backbone is configured as llm/mistral.

Returns:

TextClassifier Object

get_misclassified_records()

This method is not supported when the backbone is configured as llm/mistral.

Returns:

get misclassified records for this classification model.

load(name_or_path)

Loads a saved TextClassifier model from disk.

This method is not supported when the backbone is configured as llm/mistral and model extension.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

Parameter

Description

name_or_path

Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True, **kwargs)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

allow_plot

Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

metrics_per_label()

Returns:

precision, recall and f1 score for each label in the classification model.

plot_losses()

Plot validation and training losses after fitting the model.

This method is not supported when the backbone is configured as llm/mistral.

predict(text_or_list, show_progress=True, thresh=None, explain=False, explain_index=None, batch_size=64, **kwargs) → List[Tuple] | FeatureSet

Predicts the class label(s) for the input text

Parameter

Description

text_or_list

Required String or List. text or a list of texts for which we wish to find the class label(s).

prompt

Optional String. This parameter is applicable if the selected model backbone is from the LLM family.

This parameter use to describe the task and guardrails for the task.

show_progress

Optional Bool. If set to True, will display a progress bar depicting the items processed so far. Applicable only when a list of text is passed

thresh

Optional Float. The threshold value set to get the class label(s). Applicable only for multi-label classification task. Default is the value set during the model creation time, otherwise the value of 0.25 is set.

explain

Optional Bool. If set to True it shall generate SHAP based explanation. Kindly visit:- https://shap.readthedocs.io/en/latest/

explain_index

Optional List. Index of the rows for which explanation is required. If the value is None, it will generate an explanation for every row.

batch_size

Optional integer. Number of inputs to be processed at once. Try reducing the batch size in case of out of memory errors. Default value : 64

kwargs

Parameter

Description

input_field

Optional string. Input field name in the feature set. Supported in model extension. Default value: input_str

Returns:

• In case of single label classification problem, a tuple containing the text, its predicted class label and the confidence score.

• In case of multi label classification problem, a tuple containing the text, its predicted class labels, a list containing 1’s for the predicted labels, 0’s otherwise and list containing a score for each label

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment.

Parameter

Description

name_or_path

Required string. Folder path to save the model.

framework

Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument.

Framework choice: ‘PyTorch’ and ‘TF-ONNX’

publish

Optional boolean. Publishes the DLPK as an item.

gis

Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken.

compute_metrics

Optional boolean. Used for computing model metrics.

save_optimizer

Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False.

kwargs

Parameter

Description

overwrite

Optional boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

zip_files

Optional boolean zip_files if True, it will create the Deep Learning Package (DLPK) file while saving the model.

Returns:

the qualified path at which the model is saved

show_results(rows=5, **kwargs)

Prints the rows of the dataframe with target and prediction columns.

Parameter

Description

rows

Optional Integer. Number of rows to print.

Returns:

dataframe

supported_backbones = ['BERT', 'RoBERTa', 'DistilBERT', 'ALBERT', 'FlauBERT', 'CamemBERT', 'XLNet', 'XLM', 'XLM-RoBERTa', 'Bart', 'ELECTRA', 'Longformer', 'MobileBERT', 'Funnel', 'LLM']

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

This method is not supported when the backbone is configured as llm/mistral.

class arcgis.learn.text.SequenceToSequence(data, backbone='t5-base', **kwargs)

Creates a SequenceToSequence Object. Based on the Hugging Face transformers library

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

kwargs

Parameter

Description

verbose

Optional string. Default set to error. The log level you want to set. It means the amount of information you want to display while training or calling the various methods of this class. Allowed values are - debug, info, warning, error and critical.

seq_len

Optional Integer. Default set to 512. Maximum sequence length (at sub-word level after tokenization) of the training data to be considered for training the model.

mixed_precision

Optional Bool. Default set to False. If set True, then mixed precision training is used to train the model

pretrained_path

Optional String. Path where pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

prompt

Optional String. This parameter is applicable if the selected model backbone is from the LLM family.

This parameter used to describe the task and guardrails for the task.

examples

Optional List of Tuples. It contains List of tuples. Each of the tuples has two elements which represents input and target sentence

This parameter is applicable if the selected model backbone is from the LLM family.

Pydantic notation

Optional[List[List[str, str]]]

Example:

[[“input_1”, “output_1”]

, [“input_2”, “output_2”]

]

If examples are not supplied, a data object must be provided.

Returns:

SequenceToSequence model object for sequence_translation task.

classmethod available_backbone_models(architecture)

Get available models for the given transformer backbone

Returns:

a tuple containing the available models for the given transformer backbone

property available_metrics

List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

epochs

Required integer. Number of cycles of training on the data. Increase it if underfitting.

lr

Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model.

one_cycle

Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.

early_stopping

Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.

checkpoint

Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.

tensorboard

Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1

The default value is ‘False’.

Note

Not applicable for Text Models

monitor

Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

freeze()

Freeze up to last layer group to train only the last layer group of the model.

This method is not supported when the backbone is configured as llm/mistral.

classmethod from_model(emd_path, data=None, **kwargs)

Creates an SequenceToSequence model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using this method.

Parameter

Description

emd_path

Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

data

Optional fastai Databunch. Returned data object from prepare_textdata function or None for inferencing. Default value: None

Returns:

SequenceToSequence Object

get_model_metrics()

Calculates the following metrics:

• accuracy: the number of correctly predicted labels in the validation set divided by the total number of items in the validation set

• bleu-score This value indicates the similarity between model predictions and the ground truth text. Maximum value is 1

Returns:

a dictionary containing the metrics for classification model.

load(name_or_path)

Loads a saved SequenceToSequence model from disk.

This method is not supported when the backbone is configured as llm/mistral.

To load a custom DLPK using the model extensibility support, instantiate an object of the class using from_model.

Parameter

Description

name_or_path

Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True, **kwargs)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

allow_plot

Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(show=True)

Plot training and validation losses.

This method is not supported when the backbone is configured as llm/mistral.

Parameter

Description

show

Optional bool. Defaults to True If set to False, figure will not be plotted but will be returned, when set to True function will plot the figure and return nothing.

Returns:

matplotlib.figure.Figure

predict(text_or_list, batch_size=64, show_progress=True, explain=False, explain_index=None, **kwargs) → List[Tuple] | FeatureSet

Predicts the translated outcome.

Parameter

Description

text_or_list

Required input string or list of input strings.

batch_size

Optional integer. Number of inputs to be processed at once. Try reducing the batch size in case of out of memory errors. Default value : 64

show_progress

Optional bool. To show or not to show the progress of prediction task. Default value : True

explain

Optional bool. To enable shap based importance Default value : False

explain_index

Optional list. Index of the input rows for which the importance score will be generated Default value : None

kwargs

Parameter

Description

num_beams

Optional integer. Number of beams for beam search. 1 means no beam search. Default value is set to 1.

max_length

Optional integer. The maximum length of the sequence to be generated. Default value is set to 20.

min_length

Optional integer. The minimum length of the sequence to be generated. Default value is set to 10.

input_field

Optional string. Input field name in the feature set. Supported in model extension. Default value: input_str

Returns:

list of tuples(input , predicted output strings) or FeatureSet.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment.

Parameter

Description

name_or_path

Required string. Folder path to save the model.

framework

Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument.

Framework choice: ‘PyTorch’ and ‘TF-ONNX’

publish

Optional boolean. Publishes the DLPK as an item.

gis

Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken.

compute_metrics

Optional boolean. Used for computing model metrics.

save_optimizer

Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False.

kwargs

Parameter

Description

overwrite

Optional boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

zip_files

Optional boolean zip_files if True, it will create the Deep Learning Package (DLPK) file while saving the model.

Returns:

the qualified path at which the model is saved

show_results(rows=5, **kwargs)

Prints the rows of the dataframe with target and prediction columns.

Parameter

Description

rows

Optional Integer. Number of rows to print.

Returns:

dataframe

supported_backbones = ['T5', 'Bart', 'Marian', 'LLM']

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

This method is not supported when the backbone is configured as llm/mistral.

class arcgis.learn.text.FillMask(backbone=None, **kwargs)

Creates a FillMask Object. Based on the Hugging Face transformers library

Parameter

Description

backbone

Optional string. Specify the HuggingFace transformer model name which will be used to generate the suggestion token.

To learn more about the available models for fill-mask task, kindly visit:- https://huggingface.co/models?pipeline_tag=fill-mask

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

FillMask Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

predict_token(text_or_list, num_suggestions=5, show_progress=True)

Summarize the given text or list of text

Parameter

Description

text_or_list

Required string or list. A text/sentence or a list of texts/sentences for which on wishes to generate the recommendations for masked-token.

num_suggestions

Optional Integer. The number of suggestions to return. The maximum number of suggestion that can be generated for a missing-token is 10.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

Returns:

A list or a list of list of dict: Each result comes as list of dictionaries with the following keys:

• sequence (str) – The corresponding input with the mask token prediction.

• score (float) – The corresponding probability.

• token_str (str) – The predicted token (to replace the masked one).

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

supported_backbones = ['Albert', 'Bart', 'Bert', 'BigBird', 'Camembert', 'ConvBert', 'Data2VecText', 'Deberta', 'DebertaV2', 'DistilBert', 'Electra', 'Ernie', 'Esm', 'Flaubert', 'FNet', 'Funnel', 'IBert', 'LayoutLM', 'Longformer', 'Luke', 'MBart', 'Mega', 'MegatronBert', 'MobileBert', 'MPNet', 'Mra', 'Mvp', 'Nezha', 'Nystromformer', 'Perceiver', 'QDQBert', 'Reformer', 'RemBert', 'Roberta', 'RobertaPreLayerNorm', 'RoCBert', 'RoFormer', 'SqueezeBert', 'Tapas', 'Wav2Vec2', 'XLM', 'XLMRoberta', 'XLMRobertaXL', 'Xmod', 'Yoso']

supported transformer architectures

class arcgis.learn.text.QuestionAnswering(backbone=None, **kwargs)

Creates a QuestionAnswering Object. Based on the Hugging Face transformers library

Parameter

Description

backbone

Optional string. Specify the HuggingFace transformer model name which will be used to extract the answers from a given passage/context.

To learn more about the available models for question-answering task, kindly visit:- https://huggingface.co/models?pipeline_tag=question-answering

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

QuestionAnswering Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

get_answer(text_or_list, context, show_progress=True, explain=False, explain_start_word=True, explain_index=None, **kwargs)

Find answers for the asked questions from the given passage/context

Parameter

Description

text_or_list

Required string or list. Questions or a list of questions one wishes to seek an answer for.

context

Required string. The context associated with the question(s) which contains the answers.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

explain

optional Bool. If set to True, will generate a shap based explanation

explain_start_word

optional Bool. E.g. Context: Point cloud datasets are typically collected using Lidar sensors ( light detection and ranging ) Question: “How is Point cloud dataset collected?” Answer: Lidar Sensors

If set to True, will generate a shap based explanation for start word. if set to False, will generate explanation for last word of the answer.

In the above example, if the value of explain_start_word is True, it will generate the importance of different context words that leads to selection of “Lidar” as a starting word of the span. If explain_start_word is set to False then it will generate explanation for the word sensors

explain_index

optional List. Index of the question for which answer needs to be generated

kwargs

Parameter

Description

num_answers

Optional integer. The number of answers to return. The answers will be chosen by order of likelihood. Default value is set to 1.

max_answer_length

Optional integer. The maximum length of the predicted answers. Default value is set to 15.

max_question_length

Optional integer. The maximum length of the question after tokenization. Questions will be truncated if needed. Default value is set to 64.

impossible_answer

Optional bool. Whether or not we accept impossible as an answer. Default value is set to False

Returns:

a list or a list of list containing the answer(s) for the input question(s)

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

supported_backbones = ['Albert', 'Bart', 'Bert', 'BigBird', 'BigBirdPegasus', 'Bloom', 'Camembert', 'Canine', 'ConvBert', 'Data2VecText', 'Deberta', 'DebertaV2', 'DistilBert', 'Electra', 'Ernie', 'ErnieM', 'Falcon', 'Flaubert', 'FNet', 'Funnel', 'GPT2', 'GPTNeo', 'GPTNeoX', 'GPTJ', 'IBert', 'LayoutLMv2', 'LayoutLMv3', 'LED', 'Lilt', 'Longformer', 'Luke', 'Lxmert', 'MarkupLM', 'MBart', 'Mega', 'MegatronBert', 'MobileBert', 'MPNet', 'Mpt', 'Mra', 'MT5', 'Mvp', 'Nezha', 'Nystromformer', 'OPT', 'QDQBert', 'Reformer', 'RemBert', 'Roberta', 'RobertaPreLayerNorm', 'RoCBert', 'RoFormer', 'Splinter', 'SqueezeBert', 'T5', 'UMT5', 'XLM', 'XLMRoberta', 'XLMRobertaXL', 'XLNet', 'Xmod', 'Yoso']

supported transformer architectures

class arcgis.learn.text.TextGenerator(backbone=None, **kwargs)

Creates a TextGenerator Object. Based on the Hugging Face transformers library

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

TextGenerator Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

generate_text(text_or_list, show_progress=True, **kwargs)

Generate text(s) for a text or a list of incomplete sentence(s)

Parameter

Description

text_or_list

Required string or list. A text/sentence or a list of texts/sentences to complete.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

kwargs

Parameter

Description

min_length

Optional integer. The minimum length of the sequence to be generated. Default value is set to to min_length parameter of the model config.

max_length

Optional integer. The maximum length of the sequence to be generated. Default value is set to max_length parameter of the model config.

num_return_sequences

Optional integer. The number of independently computed returned sequences for each element in the batch. Default value is set to 1.

num_beams

Optional integer. Number of beams for beam search. 1 means no beam search. Default value is set to 1.

length_penalty

Optional float. Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences. Default value is set to 1.0.

early_stopping

Optional bool. Whether to stop the beam search when at least num_beams sentences are finished per batch or not. Default value is set to False.

Returns:

a list or a list of list containing the generated text for the input prompt(s) / sentence(s)

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

supported_backbones = ['Bart', 'Bert', 'BertGeneration', 'BigBird', 'BigBirdPegasus', 'BioGpt', 'Blenderbot', 'BlenderbotSmall', 'Bloom', 'Camembert', 'Llama', 'CodeGen', 'CpmAnt', 'CTRL', 'Data2VecText', 'Electra', 'Ernie', 'Falcon', 'Fuyu', 'Git', 'GPT2', 'GPT2', 'GPTBigCode', 'GPTNeo', 'GPTNeoX', 'GPTNeoXJapanese', 'GPTJ', 'Llama', 'Marian', 'MBart', 'Mega', 'MegatronBert', 'Mistral', 'Mixtral', 'Mpt', 'Musicgen', 'Mvp', 'OpenLlama', 'OpenAIGPT', 'OPT', 'Pegasus', 'Persimmon', 'Phi', 'PLBart', 'ProphetNet', 'QDQBert', 'Reformer', 'RemBert', 'Roberta', 'RobertaPreLayerNorm', 'RoCBert', 'RoFormer', 'Rwkv', 'Speech2Text2', 'TransfoXL', 'TrOCR', 'Whisper', 'XGLM', 'XLM', 'XLMProphetNet', 'XLMRoberta', 'XLMRobertaXL', 'XLNet', 'Xmod']

supported transformer architectures

class arcgis.learn.text.TextSummarizer(backbone=None, **kwargs)

Creates a TextSummarizer Object. Based on the Hugging Face transformers library

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

TextSummarizer Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

summarize(text_or_list, show_progress=True, **kwargs)

Summarize the given text or list of text

Parameter

Description

text_or_list

Required string or list. A text/passage or a list of texts/passages to generate the summary for.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

kwargs

Parameter

Description

min_length

Optional integer. The minimum length of the sequence to be generated. Default value is set to to min_length parameter of the model config.

max_length

Optional integer. The maximum length of the sequence to be generated. Default value is set to to max_length parameter of the model config.

num_return_sequences

Optional integer. The number of independently computed returned sequences for each element in the batch. Default value is set to 1.

num_beams

Optional integer. Number of beams for beam search. 1 means no beam search. Default value is set to 1.

length_penalty

Optional float. Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences. Default value is set to 1.0.

early_stopping

Optional bool. Whether to stop the beam search when at least num_beams sentences are finished per batch or not. Default value is set to False.

Returns:

a list or a list of list containing the summary/summaries for the input prompt(s) / sentence(s)

supported_backbones = ['Bart', 'BigBirdPegasus', 'Blenderbot', 'BlenderbotSmall', 'EncoderDecoder', 'FSMT', 'GPTSanJapanese', 'LED', 'LongT5', 'M2M100', 'Marian', 'MBart', 'MT5', 'Mvp', 'NllbMoe', 'Pegasus', 'PegasusX', 'PLBart', 'ProphetNet', 'SeamlessM4T', 'SeamlessM4Tv2', 'SwitchTransformers', 'T5', 'UMT5', 'XLMProphetNet']

supported transformer architectures

class arcgis.learn.text.TextTranslator(source_language='es', target_language='en', **kwargs)

Creates a TextTranslator Object. Based on the Hugging Face transformers library To learn more about the available models for translation task, kindly visit:- https://huggingface.co/models?pipeline_tag=translation&search=Helsinki

Parameter

Description

source_language

Optional string. Specify the language of the text you would like to get the translation of. Default value is ‘es’ (Spanish)

target_language

Optional string. The language into which one wishes to translate the input text. Default value is ‘en’ (English)

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

TextTranslator Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

supported_backbones = ['MarianMT']

supported transformer architectures

translate(text_or_list, show_progress=True, **kwargs)

Translate the given text or list of text into the target language

Parameter

Description

text_or_list

Required string or list. A text/passage or a list of texts/passages to translate.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

kwargs

Parameter

Description

min_length

Optional integer. The minimum length of the sequence to be generated. Default value is set to to min_length parameter of the model config.

max_length

Optional integer. The maximum length of the sequence to be generated. Default value is set to to max_length parameter of the model config.

num_return_sequences

Optional integer. The number of independently computed returned sequences for each element in the batch. Default value is set to 1.

num_beams

Optional integer. Number of beams for beam search. 1 means no beam search. Default value is set to 1.

length_penalty

Optional float. Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences. Default value is set to 1.0.

early_stopping

Optional bool. Whether to stop the beam search when at least num_beams sentences are finished per batch or not. Default value is set to False.

Returns:

a list or a list of list containing the translation of the input prompt(s) / sentence(s) to the target language

class arcgis.learn.text.ZeroShotClassifier(backbone=None, **kwargs)

Creates a ZeroShotClassifier Object. Based on the Hugging Face transformers library

kwargs

Parameter

Description

pretrained_path

Option str. Path to a directory, where pretrained model files are saved. If pretrained_path is provided, the model is loaded from that path on the local disk.

working_dir

Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns:

ZeroShotClassifier Object

classmethod from_model(emd_path, **kwargs)

Creates an SequenceToSequence model object from an Esri Model Definition (EMD) file.

Parameter

Description

emd_path

Required string. Path to Esri Model Definition(EMD) file or the folder with saved model files.

Returns:

SequenceToSequence Object

predict(text_or_list, candidate_labels, show_progress=True, **kwargs)

Predicts the class label(s) for the input text

Parameter

Description

text_or_list

Required string or list. The sequence or a list of sequences to classify.

candidate_labels

Required string or list. The set of possible class labels to classify each sequence into. Can be a single label, a string of comma-separated labels, or a list of labels.

show_progress

optional Bool. If set to True, will display a progress bar depicting the items processed so far.

kwargs

Parameter

Description

multi_class

Optional boolean. Whether or not multiple candidate labels can be true. Default value is set to False.

hypothesis

Optional string. The template used to turn each label into an NLI-style hypothesis. This template must include a {} or similar syntax for the candidate label to be inserted into the template. Default value is set to “This example is {}.”.

Returns:

a list of dict: Each result comes as a dictionary with the following keys:

• sequence (str) – The sequence for which this is the output.

• labels (List[str]) – The labels sorted by order of likelihood.

• scores (List[float]) – The probabilities for each of the labels.

save(name_or_path)

Saves the translator model files on a specified path on the local disk.

Parameter

Description

name_or_path

Required string. Path to save model files on the local disk.

Returns:

Absolute path for the saved model

supported_backbones = ['Albert', 'Bart', 'Bert', 'BigBird', 'BigBirdPegasus', 'BioGpt', 'Bloom', 'Camembert', 'Canine', 'Llama', 'ConvBert', 'CTRL', 'Data2VecText', 'Deberta', 'DebertaV2', 'DistilBert', 'Electra', 'Ernie', 'ErnieM', 'Esm', 'Falcon', 'Flaubert', 'FNet', 'Funnel', 'GPT2', 'GPT2', 'GPTBigCode', 'GPTNeo', 'GPTNeoX', 'GPTJ', 'IBert', 'LayoutLM', 'LayoutLMv2', 'LayoutLMv3', 'LED', 'Lilt', 'Llama', 'Longformer', 'Luke', 'MarkupLM', 'MBart', 'Mega', 'MegatronBert', 'Mistral', 'Mixtral', 'MobileBert', 'MPNet', 'Mpt', 'Mra', 'MT5', 'Mvp', 'Nezha', 'Nystromformer', 'OpenLlama', 'OpenAIGPT', 'OPT', 'Perceiver', 'Persimmon', 'Phi', 'PLBart', 'QDQBert', 'Reformer', 'RemBert', 'Roberta', 'RobertaPreLayerNorm', 'RoCBert', 'RoFormer', 'SqueezeBert', 'T5', 'Tapas', 'TransfoXL', 'UMT5', 'XLM', 'XLMRoberta', 'XLMRobertaXL', 'XLNet', 'Xmod', 'Yoso']

supported transformer architectures

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