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.

Parameter	Description
data	Optional data object returned from prepare_data() function. data object can be None, in case where someone wants to use a Hugging Face Transformer model fine-tuned on entity-recognition task. In this case the model should be used directly for inference.
lang	Optional string. Language-specific code, named according to the language’s ISO code The default value is ‘en’ for English.
backbone	Optional string. Specify spacy, mistral or the HuggingFace transformer model name to be used to train the entity recognizer model. Default set to spacy. Entity recognition via spaCy is based on <https://spacy.io/api/entityrecognizer> To learn more about the available transformer models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about the available transformer models fine-tuned on Named Entity Recognition Task, kindly visit:- https://huggingface.co/models?pipeline_tag=token-classification To learn more about mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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 use to describe the task and guardrails for the task.
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

Parameter	Description
architecture	Required string. name of the architecture or ‘llm’ one wishes to use. To learn more about the available models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about llm and mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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)

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

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.

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)

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

Loads a saved EntityRecognizer model from disk.

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

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

lr_find(allow_plot=True)

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	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False. Boolean zip_files if True, it will create the Deep Learning Package (DLPK) file while saving the model.

show_results(ds_type='valid')

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

Parameter	Description
ds_type	Optional string, defaults to valid.

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.

TextClassifier

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

Parameter	Description
data	Optional data object returned from prepare_textdata function. data object can be None, in case where someone wants to use a Hugging Face Transformer model fine-tuned on classification task. In this case the model should be used directly for inference.
backbone	Optional string. Specify gpt or the HuggingFace transformer model name to be used to train the classifier. Default set to bert-base-cased. To learn more about the available models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about the available transformer models fine-tuned on Text Classification Task, kindly visit:- https://huggingface.co/models?pipeline_tag=text-classification To learn more about mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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 use to describe the task and guardrails for the task.
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

Parameter	Description
architecture	Required string. name of the transformer or llm backbone one wish to use. To learn more about the available models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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. ===================== =========================================== Parameter Description ——————— ——————————————- emd_path Required string. Path to Deep Learning Package

(DLPK) or Esri Model Definition(EMD) file.

——————— ——————————————- data Required fastai Databunch or None. Returned data

object from prepare_textdata function or None for inferencing.

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.

Parameter	Description
backbone	Required string. Specify the Hugging Face Transformer backbone name fine-tuned on Text Classification task. To get more details on available transformer models fine-tuned on Text Classification Task, kindly visit:- https://huggingface.co/models?pipeline_tag=text-classification

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)

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

Loads a saved TextClassifier model from disk.

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

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

lr_find(allow_plot=True)

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)

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

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

SequenceToSequence

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.

Parameter	Description
data	Required text data object, returned from prepare_textdata function.
backbone	Optional string. Specifying the HuggingFace transformer model name to be used to train the model. Default set to ‘t5-base’. To learn more about the available models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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 use 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

Parameter	Description
architecture	Required string. name of the transformer backbone one wish to use. To learn more about the available models or choose models that are suitable for your dataset, kindly visit:- https://huggingface.co/transformers/pretrained_models.html To learn more about mistral https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

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

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)

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

Loads a saved SequenceToSequence model from disk.

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

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

lr_find(allow_plot=True)

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)

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

Returns

list of tuples(input , predicted output strings).

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

Inference Only Models