two version of R2R are here HEAD master

10 files changed, 1716 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py
new file mode 100644
index 00000000..9be7b483
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py
@@ -0,0 +1,25 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from .automl_nlp_job import AutoMLNLPJob
+from .nlp_featurization_settings import NlpFeaturizationSettings
+from .nlp_fixed_parameters import NlpFixedParameters
+from .nlp_limit_settings import NlpLimitSettings
+from .nlp_search_space import NlpSearchSpace
+from .nlp_sweep_settings import NlpSweepSettings
+from .text_classification_job import TextClassificationJob
+from .text_classification_multilabel_job import TextClassificationMultilabelJob
+from .text_ner_job import TextNerJob
+
+__all__ = [
+    "AutoMLNLPJob",
+    "NlpFeaturizationSettings",
+    "NlpFixedParameters",
+    "NlpLimitSettings",
+    "NlpSearchSpace",
+    "NlpSweepSettings",
+    "TextClassificationJob",
+    "TextClassificationMultilabelJob",
+    "TextNerJob",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py
new file mode 100644
index 00000000..f0b3baa8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py
@@ -0,0 +1,467 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from abc import ABC
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    LogVerbosity,
+    NlpLearningRateScheduler,
+    SamplingAlgorithmType,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.automl_vertical import AutoMLVertical
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_search_space import NlpSearchSpace
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.utils import cast_to_specific_search_space
+from azure.ai.ml.entities._job.sweep.early_termination_policy import EarlyTerminationPolicy
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+# pylint: disable=too-many-instance-attributes,protected-access
+class AutoMLNLPJob(AutoMLVertical, ABC):
+    """Base class for AutoML NLP jobs.
+
+    You should not instantiate this class directly. Instead you should
+    create classes for specific NLP Jobs.
+
+    :param task_type: NLP task type, must be one of 'TextClassification',
+        'TextClassificationMultilabel', or 'TextNER'
+    :type task_type: str
+    :param primary_metric: Primary metric to display from NLP job
+    :type primary_metric: str
+    :param training_data: Training data
+    :type training_data: Input
+    :param validation_data: Validation data
+    :type validation_data: Input
+    :param target_column_name: Column name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param log_verbosity: The degree of verbosity used in logging, defaults to None,
+        must be one of 'NotSet', 'Debug', 'Info', 'Warning', 'Error', 'Critical', or None
+    :type log_verbosity: Optional[str]
+    :param featurization: Featurization settings used for NLP job, defaults to None
+    :type featurization: Optional[~azure.ai.ml.automl.NlpFeaturizationSettings]
+    :param limits: Limit settings for NLP jobs, defaults to None
+    :type limits: Optional[~azure.ai.ml.automl.NlpLimitSettings]
+    :param sweep: Sweep settings used for NLP job, defaults to None
+    :type sweep: Optional[~azure.ai.ml.automl.NlpSweepSettings]
+    :param training_parameters: Fixed parameters for the training of all candidates.
+        , defaults to None
+    :type training_parameters: Optional[~azure.ai.ml.automl.NlpFixedParameters]
+    :param search_space: Search space(s) to sweep over for NLP sweep jobs, defaults to None
+    :type search_space: Optional[List[~azure.ai.ml.automl.NlpSearchSpace]]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        primary_metric: str,
+        training_data: Optional[Input],
+        validation_data: Optional[Input],
+        target_column_name: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        featurization: Optional[NlpFeaturizationSettings] = None,
+        limits: Optional[NlpLimitSettings] = None,
+        sweep: Optional[NlpSweepSettings] = None,
+        training_parameters: Optional[NlpFixedParameters] = None,
+        search_space: Optional[List[NlpSearchSpace]] = None,
+        **kwargs: Any,
+    ):
+        self._training_parameters: Optional[NlpFixedParameters] = None
+
+        super().__init__(
+            task_type, training_data=training_data, validation_data=validation_data, **kwargs  # type: ignore
+        )
+        self.log_verbosity = log_verbosity
+        self._primary_metric: str = ""
+        self.primary_metric = primary_metric
+
+        self.target_column_name = target_column_name
+
+        self._featurization = featurization
+        self._limits = limits or NlpLimitSettings()
+        self._sweep = sweep
+        self.training_parameters = training_parameters  # via setter method.
+        self._search_space = search_space
+
+    @property
+    def training_parameters(self) -> Optional[NlpFixedParameters]:
+        """Parameters that are used for all submitted jobs.
+
+        :return: fixed training parameters for NLP jobs
+        :rtype: ~azure.ai.ml.automl.NlpFixedParameters
+        """
+        return self._training_parameters
+
+    @training_parameters.setter
+    def training_parameters(self, value: Union[Dict, NlpFixedParameters]) -> None:
+        if value is None:
+            self._training_parameters = None
+        elif isinstance(value, NlpFixedParameters):
+            self._training_parameters = value
+            # Convert parameters from snake case to enum.
+            self.set_training_parameters(learning_rate_scheduler=value.learning_rate_scheduler)
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for nlp training parameters."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_training_parameters(**value)
+
+    @property
+    def search_space(self) -> Optional[List[NlpSearchSpace]]:
+        """Search space(s) to sweep over for NLP sweep jobs
+
+        :return: list of search spaces to sweep over for NLP jobs
+        :rtype: List[~azure.ai.ml.automl.NlpSearchSpace]
+        """
+        return self._search_space
+
+    @search_space.setter
+    def search_space(self, value: Union[List[dict], List[SearchSpace]]) -> None:
+        if not isinstance(value, list):
+            msg = "Expected a list for search space."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        all_dict_type = all(isinstance(item, dict) for item in value)
+        all_search_space_type = all(isinstance(item, SearchSpace) for item in value)
+
+        if not (all_search_space_type or all_dict_type):
+            msg = "Expected all items in the list to be either dictionaries or SearchSpace objects."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        self._search_space = [
+            cast_to_specific_search_space(item, NlpSearchSpace, self.task_type) for item in value  # type: ignore
+        ]
+
+    @property
+    def primary_metric(self) -> str:
+        """Primary metric to display from NLP job
+
+        :return: primary metric to display
+        :rtype: str
+        """
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: str) -> None:
+        self._primary_metric = value
+
+    @property
+    def log_verbosity(self) -> LogVerbosity:
+        """Log verbosity configuration
+
+        :return: the degree of verbosity used in logging
+        :rtype: ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        """
+        return self._log_verbosity
+
+    @log_verbosity.setter
+    def log_verbosity(self, value: Union[str, LogVerbosity]) -> None:
+        self._log_verbosity = None if value is None else LogVerbosity[camel_to_snake(value).upper()]
+
+    @property
+    def limits(self) -> NlpLimitSettings:
+        """Limit settings for NLP jobs
+
+        :return: limit configuration for NLP job
+        :rtype: ~azure.ai.ml.automl.NlpLimitSettings
+        """
+        return self._limits
+
+    @limits.setter
+    def limits(self, value: Union[Dict, NlpLimitSettings]) -> None:
+        if isinstance(value, NlpLimitSettings):
+            self._limits = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for limit settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_limits(**value)
+
+    @property
+    def sweep(self) -> Optional[NlpSweepSettings]:
+        """Sweep settings used for NLP job
+
+        :return: sweep settings
+        :rtype: ~azure.ai.ml.automl.NlpSweepSettings
+        """
+        return self._sweep
+
+    @sweep.setter
+    def sweep(self, value: Union[Dict, NlpSweepSettings]) -> None:
+        if isinstance(value, NlpSweepSettings):
+            self._sweep = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for sweep settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_sweep(**value)
+
+    @property
+    def featurization(self) -> Optional[NlpFeaturizationSettings]:
+        """Featurization settings used for NLP job
+
+        :return: featurization settings
+        :rtype: ~azure.ai.ml.automl.NlpFeaturizationSettings
+        """
+        return self._featurization
+
+    @featurization.setter
+    def featurization(self, value: Union[Dict, NlpFeaturizationSettings]) -> None:
+        if isinstance(value, NlpFeaturizationSettings):
+            self._featurization = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for featurization settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_featurization(**value)
+
+    def set_data(self, *, training_data: Input, target_column_name: str, validation_data: Input) -> None:
+        """Define data configuration for NLP job
+
+        :keyword training_data: Training data
+        :type training_data: ~azure.ai.ml.Input
+        :keyword target_column_name: Column name of the target column.
+        :type target_column_name: str
+        :keyword validation_data: Validation data
+        :type validation_data: ~azure.ai.ml.Input
+        """
+        # Properties for NlpVerticalDataSettings
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+        self.validation_data = validation_data
+
+    def set_limits(
+        self,
+        *,
+        max_trials: int = 1,
+        max_concurrent_trials: int = 1,
+        max_nodes: int = 1,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ) -> None:
+        """Define limit configuration for AutoML NLP job
+
+        :keyword max_trials: Maximum number of AutoML iterations, defaults to 1
+        :type max_trials: int, optional
+        :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations, defaults to 1
+        :type max_concurrent_trials: int, optional
+        :keyword max_nodes: Maximum number of nodes used for sweep, defaults to 1
+        :type max_nodes: int, optional
+        :keyword timeout_minutes: Timeout for the AutoML job, defaults to None
+        :type timeout_minutes: Optional[int]
+        :keyword trial_timeout_minutes: Timeout for each AutoML trial, defaults to None
+        :type trial_timeout_minutes: Optional[int]
+        """
+        self._limits = NlpLimitSettings(
+            max_trials=max_trials,
+            max_concurrent_trials=max_concurrent_trials,
+            max_nodes=max_nodes,
+            timeout_minutes=timeout_minutes,
+            trial_timeout_minutes=trial_timeout_minutes,
+        )
+
+    def set_sweep(
+        self,
+        *,
+        sampling_algorithm: Union[str, SamplingAlgorithmType],
+        early_termination: Optional[EarlyTerminationPolicy] = None,
+    ) -> None:
+        """Define sweep configuration for AutoML NLP job
+
+        :keyword sampling_algorithm: Required. Specifies type of hyperparameter sampling algorithm.
+            Possible values include: "Grid", "Random", and "Bayesian".
+        :type sampling_algorithm: Union[str, ~azure.ai.ml.automl.SamplingAlgorithmType]
+        :keyword early_termination: Optional. early termination policy to end poorly performing training candidates,
+            defaults to None.
+        :type early_termination: Optional[~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy]
+        """
+        if self._sweep:
+            self._sweep.sampling_algorithm = sampling_algorithm
+        else:
+            self._sweep = NlpSweepSettings(sampling_algorithm=sampling_algorithm)
+
+        self._sweep.early_termination = early_termination or self._sweep.early_termination
+
+    def set_training_parameters(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, NlpLearningRateScheduler]] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_ratio: Optional[float] = None,
+        weight_decay: Optional[float] = None,
+    ) -> None:
+        """Fix certain training parameters throughout the training procedure for all candidates.
+
+        :keyword gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+            pass. This must be a positive integer., defaults to None
+        :type gradient_accumulation_steps: Optional[int]
+        :keyword learning_rate: initial learning rate. Must be a float in (0, 1)., defaults to None
+        :type learning_rate: Optional[float]
+        :keyword learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+            'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup'., defaults to None
+        :type learning_rate_scheduler: Optional[Union[str, ~azure.ai.ml.automl.NlpLearningRateScheduler]]
+        :keyword model_name: the model name to use during training. Must choose from 'bert-base-cased',
+            'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+            'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+            'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased'.,
+            defaults to None
+        :type model_name: Optional[str]
+        :keyword number_of_epochs: the number of epochs to train with. Must be a positive integer., defaults to None
+        :type number_of_epochs: Optional[int]
+        :keyword training_batch_size: the batch size during training. Must be a positive integer., defaults to None
+        :type training_batch_size: Optional[int]
+        :keyword validation_batch_size: the batch size during validation. Must be a positive integer., defaults to None
+        :type validation_batch_size: Optional[int]
+        :keyword warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1]., defaults to None
+        :type warmup_ratio: Optional[float]
+        :keyword weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1]., defaults to None
+        :type weight_decay: Optional[float]
+        """
+        self._training_parameters = self._training_parameters or NlpFixedParameters()
+
+        self._training_parameters.gradient_accumulation_steps = (
+            gradient_accumulation_steps
+            if gradient_accumulation_steps is not None
+            else self._training_parameters.gradient_accumulation_steps
+        )
+
+        self._training_parameters.learning_rate = (
+            learning_rate if learning_rate is not None else self._training_parameters.learning_rate
+        )
+
+        self._training_parameters.learning_rate_scheduler = (
+            NlpLearningRateScheduler[camel_to_snake(learning_rate_scheduler).upper()]
+            if learning_rate_scheduler is not None
+            else self._training_parameters.learning_rate_scheduler
+        )
+
+        self._training_parameters.model_name = (
+            model_name if model_name is not None else self._training_parameters.model_name
+        )
+
+        self._training_parameters.number_of_epochs = (
+            number_of_epochs if number_of_epochs is not None else self._training_parameters.number_of_epochs
+        )
+
+        self._training_parameters.training_batch_size = (
+            training_batch_size if training_batch_size is not None else self._training_parameters.training_batch_size
+        )
+
+        self._training_parameters.validation_batch_size = (
+            validation_batch_size
+            if validation_batch_size is not None
+            else self._training_parameters.validation_batch_size
+        )
+
+        self._training_parameters.warmup_ratio = (
+            warmup_ratio if warmup_ratio is not None else self._training_parameters.warmup_ratio
+        )
+
+        self._training_parameters.weight_decay = (
+            weight_decay if weight_decay is not None else self._training_parameters.weight_decay
+        )
+
+    def set_featurization(self, *, dataset_language: Optional[str] = None) -> None:
+        """Define featurization configuration for AutoML NLP job.
+
+        :keyword dataset_language: Language of the dataset, defaults to None
+        :type dataset_language: Optional[str]
+        """
+        self._featurization = NlpFeaturizationSettings(
+            dataset_language=dataset_language,
+        )
+
+    def extend_search_space(self, value: Union[SearchSpace, List[SearchSpace]]) -> None:
+        """Add (a) search space(s) for an AutoML NLP job.
+
+        :param value: either a SearchSpace object or a list of SearchSpace objects with nlp-specific parameters.
+        :type value: Union[~azure.ai.ml.automl.SearchSpace, List[~azure.ai.ml.automl.SearchSpace]]
+        """
+        self._search_space = self._search_space or []
+        if isinstance(value, list):
+            self._search_space.extend(
+                [cast_to_specific_search_space(item, NlpSearchSpace, self.task_type) for item in value]  # type: ignore
+            )
+        else:
+            self._search_space.append(
+                cast_to_specific_search_space(value, NlpSearchSpace, self.task_type)  # type: ignore
+            )
+
+    @classmethod
+    def _get_search_space_from_str(cls, search_space_str: Optional[str]) -> Optional[List]:
+        if search_space_str is not None:
+            return [NlpSearchSpace._from_rest_object(entry) for entry in search_space_str if entry is not None]
+        return None
+
+    def _restore_data_inputs(self) -> None:
+        """Restore MLTableJobInputs to Inputs within data_settings.
+
+        self.training_data and self.validation_data should reflect what user passed in (Input) Once we get response back
+        from service (as MLTableJobInput), we should set responsible ones back to Input
+        """
+        super()._restore_data_inputs()
+        self.training_data = self.training_data if self.training_data else None  # type: ignore
+        self.validation_data = self.validation_data if self.validation_data else None  # type: ignore
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AutoMLNLPJob):
+            return NotImplemented
+
+        return (
+            self.primary_metric == other.primary_metric
+            and self.log_verbosity == other.log_verbosity
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+            and self._featurization == other._featurization
+            and self._limits == other._limits
+            and self._sweep == other._sweep
+            and self._training_parameters == other._training_parameters
+            and self._search_space == other._search_space
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py
new file mode 100644
index 00000000..5649dea2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py
@@ -0,0 +1,47 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    NlpVerticalFeaturizationSettings as RestNlpVerticalFeaturizationSettings,
+)
+from azure.ai.ml.entities._job.automl.featurization_settings import FeaturizationSettings, FeaturizationSettingsType
+
+
+class NlpFeaturizationSettings(FeaturizationSettings):
+    """Featurization settings for all AutoML NLP Verticals.
+
+    :ivar type: Specifies the type of FeaturizationSettings. Set automatically to "NLP" for this class.
+    :vartype type: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_featurization_settings]
+                :end-before: [END automl.nlp_featurization_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp featurization settings
+    """
+
+    type = FeaturizationSettingsType.NLP
+
+    def _to_rest_object(self) -> RestNlpVerticalFeaturizationSettings:
+        return RestNlpVerticalFeaturizationSettings(
+            dataset_language=self.dataset_language,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpVerticalFeaturizationSettings) -> "NlpFeaturizationSettings":
+        return cls(
+            dataset_language=obj.dataset_language,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpFeaturizationSettings):
+            return NotImplemented
+
+        return super().__eq__(other)
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py
new file mode 100644
index 00000000..13c594b6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py
@@ -0,0 +1,117 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpFixedParameters as RestNlpFixedParameters
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpFixedParameters(RestTranslatableMixin):
+    """Configuration of fixed parameters for all candidates of an AutoML NLP Job
+
+    :param gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+            pass. This must be a positive integer, defaults to None
+    :type gradient_accumulation_steps: Optional[int]
+    :param learning_rate: initial learning rate. Must be a float in (0, 1), defaults to None
+    :type learning_rate: Optional[float]
+    :param learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+            'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup', defaults to None
+    :type learning_rate_scheduler: Optional[str]
+    :param model_name: the model name to use during training. Must choose from 'bert-base-cased',
+            'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+            'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+            'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased',
+            defaults to None
+    :type model_name: Optional[str]
+    :param number_of_epochs: the number of epochs to train with. Must be a positive integer, defaults to None
+    :type number_of_epochs: Optional[int]
+    :param training_batch_size: the batch size during training. Must be a positive integer, defaults to None
+    :type training_batch_size: Optional[int]
+    :param validation_batch_size: the batch size during validation. Must be a positive integer, defaults to None
+    :type validation_batch_size: Optional[int]
+    :param warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1], defaults to None
+    :type warmup_ratio: Optional[float]
+    :param weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1] defaults to None
+    :type weight_decay: Optional[float]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_fixed_parameters]
+                :end-before: [END automl.nlp_fixed_parameters]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp fixed parameters
+    """
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_ratio: Optional[float] = None,
+        weight_decay: Optional[float] = None,
+    ):
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+    def _to_rest_object(self) -> RestNlpFixedParameters:
+        return RestNlpFixedParameters(
+            gradient_accumulation_steps=self.gradient_accumulation_steps,
+            learning_rate=self.learning_rate,
+            learning_rate_scheduler=self.learning_rate_scheduler,
+            model_name=self.model_name,
+            number_of_epochs=self.number_of_epochs,
+            training_batch_size=self.training_batch_size,
+            validation_batch_size=self.validation_batch_size,
+            warmup_ratio=self.warmup_ratio,
+            weight_decay=self.weight_decay,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpFixedParameters) -> "NlpFixedParameters":
+        return cls(
+            gradient_accumulation_steps=obj.gradient_accumulation_steps,
+            learning_rate=obj.learning_rate,
+            learning_rate_scheduler=obj.learning_rate_scheduler,
+            model_name=obj.model_name,
+            number_of_epochs=obj.number_of_epochs,
+            training_batch_size=obj.training_batch_size,
+            validation_batch_size=obj.validation_batch_size,
+            warmup_ratio=obj.warmup_ratio,
+            weight_decay=obj.weight_decay,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpFixedParameters):
+            return NotImplemented
+
+        return (
+            self.gradient_accumulation_steps == other.gradient_accumulation_steps
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.number_of_epochs == other.number_of_epochs
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_ratio == other.warmup_ratio
+            and self.weight_decay == other.weight_decay
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py
new file mode 100644
index 00000000..1e99f4f0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py
@@ -0,0 +1,79 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpVerticalLimitSettings as RestNlpLimitSettings
+from azure.ai.ml._utils.utils import from_iso_duration_format_mins, to_iso_duration_format_mins
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpLimitSettings(RestTranslatableMixin):
+    """Limit settings for all AutoML NLP Verticals.
+
+    :param max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+    :type max_concurrent_trials: int
+    :param max_trials: Maximum number of AutoML iterations.
+    :type max_trials: int
+    :param timeout_minutes: AutoML job timeout.
+    :type timeout_minutes: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_limit_settings]
+                :end-before: [END automl.nlp_limit_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp limit settings
+    """
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_trials: int = 1,
+        max_nodes: int = 1,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ):
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_trials = max_trials
+        self.max_nodes = max_nodes
+        self.timeout_minutes = timeout_minutes
+        self.trial_timeout_minutes = trial_timeout_minutes
+
+    def _to_rest_object(self) -> RestNlpLimitSettings:
+        return RestNlpLimitSettings(
+            max_concurrent_trials=self.max_concurrent_trials,
+            max_trials=self.max_trials,
+            max_nodes=self.max_nodes,
+            timeout=to_iso_duration_format_mins(self.timeout_minutes),
+            trial_timeout=to_iso_duration_format_mins(self.trial_timeout_minutes),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpLimitSettings) -> "NlpLimitSettings":
+        return cls(
+            max_concurrent_trials=obj.max_concurrent_trials,
+            max_trials=obj.max_trials,
+            max_nodes=obj.max_nodes,
+            timeout_minutes=from_iso_duration_format_mins(obj.timeout),
+            trial_timeout_minutes=from_iso_duration_format_mins(obj.trial_timeout),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpLimitSettings):
+            return NotImplemented
+
+        return (
+            self.max_concurrent_trials == other.max_concurrent_trials
+            and self.max_trials == other.max_trials
+            and self.max_nodes == other.max_nodes
+            and self.timeout_minutes == other.timeout_minutes
+            and self.trial_timeout_minutes == other.trial_timeout_minutes
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py
new file mode 100644
index 00000000..e4ad435f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py
@@ -0,0 +1,185 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpLearningRateScheduler, NlpParameterSubspace
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.constants import NlpModels
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.search_space_utils import _convert_from_rest_object, _convert_to_rest_object
+from azure.ai.ml.entities._job.sweep.search_space import Choice, SweepDistribution
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpSearchSpace(RestTranslatableMixin):
+    """Search space for AutoML NLP tasks.
+
+    :param gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+        pass. This must be a positive integer., defaults to None
+    :type gradient_accumulation_steps: Optional[Union[int, SweepDistribution]]
+    :param learning_rate: initial learning rate. Must be a float in (0, 1), defaults to None
+    :type learning_rate: Optional[Union[float, SweepDistribution]]
+    :param learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+        'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup', defaults to None
+    :type learning_rate_scheduler: Optional[Union[str, SweepDistribution]]
+    :param model_name: the model name to use during training. Must choose from 'bert-base-cased',
+        'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+        'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+        'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased',
+        defaults to None
+    :type model_name: Optional[Union[str, SweepDistribution]]
+    :param number_of_epochs: the number of epochs to train with. Must be a positive integer, defaults to None
+    :type number_of_epochs: Optional[Union[int, SweepDistribution]]
+    :param training_batch_size: the batch size during training. Must be a positive integer, defaults to None
+    :type training_batch_size: Optional[Union[int, SweepDistribution]]
+    :param validation_batch_size: the batch size during validation. Must be a positive integer, defaults to None
+    :type validation_batch_size: Optional[Union[int, SweepDistribution]]
+    :param warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1], defaults to None
+    :type warmup_ratio: Optional[Union[float, SweepDistribution]]
+    :param weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1], defaults to None
+    :type weight_decay: Optional[Union[float, SweepDistribution]]
+
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_search_space]
+                :end-before: [END automl.nlp_search_space]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp search space
+    """
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[Union[int, SweepDistribution]] = None,
+        learning_rate: Optional[Union[float, SweepDistribution]] = None,
+        learning_rate_scheduler: Optional[Union[str, SweepDistribution]] = None,
+        model_name: Optional[Union[str, SweepDistribution]] = None,
+        number_of_epochs: Optional[Union[int, SweepDistribution]] = None,
+        training_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        validation_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        warmup_ratio: Optional[Union[float, SweepDistribution]] = None,
+        weight_decay: Optional[Union[float, SweepDistribution]] = None
+    ):
+        # Since we want customers to be able to specify enums as well rather than just strings, we need to access
+        # the enum values here before we serialize them ('NlpModels.BERT_BASE_CASED' vs. 'bert-base-cased').
+        if isinstance(learning_rate_scheduler, NlpLearningRateScheduler):
+            learning_rate_scheduler = camel_to_snake(learning_rate_scheduler.value)
+        elif isinstance(learning_rate_scheduler, Choice):
+            if learning_rate_scheduler.values is not None:
+                learning_rate_scheduler.values = [
+                    camel_to_snake(item.value) if isinstance(item, NlpLearningRateScheduler) else item
+                    for item in learning_rate_scheduler.values
+                ]
+
+        if isinstance(model_name, NlpModels):
+            model_name = model_name.value
+        elif isinstance(model_name, Choice):
+            if model_name.values is not None:
+                model_name.values = [item.value if isinstance(item, NlpModels) else item for item in model_name.values]
+
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+    def _to_rest_object(self) -> NlpParameterSubspace:
+        return NlpParameterSubspace(
+            gradient_accumulation_steps=(
+                _convert_to_rest_object(self.gradient_accumulation_steps)
+                if self.gradient_accumulation_steps is not None
+                else None
+            ),
+            learning_rate=_convert_to_rest_object(self.learning_rate) if self.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_to_rest_object(self.learning_rate_scheduler)
+                if self.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_to_rest_object(self.model_name) if self.model_name is not None else None,
+            number_of_epochs=(
+                _convert_to_rest_object(self.number_of_epochs) if self.number_of_epochs is not None else None
+            ),
+            training_batch_size=(
+                _convert_to_rest_object(self.training_batch_size) if self.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_to_rest_object(self.validation_batch_size) if self.validation_batch_size is not None else None
+            ),
+            warmup_ratio=_convert_to_rest_object(self.warmup_ratio) if self.warmup_ratio is not None else None,
+            weight_decay=_convert_to_rest_object(self.weight_decay) if self.weight_decay is not None else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: NlpParameterSubspace) -> "NlpSearchSpace":
+        return cls(
+            gradient_accumulation_steps=(
+                _convert_from_rest_object(obj.gradient_accumulation_steps)
+                if obj.gradient_accumulation_steps is not None
+                else None
+            ),
+            learning_rate=_convert_from_rest_object(obj.learning_rate) if obj.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_from_rest_object(obj.learning_rate_scheduler)
+                if obj.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_from_rest_object(obj.model_name) if obj.model_name is not None else None,
+            number_of_epochs=(
+                _convert_from_rest_object(obj.number_of_epochs) if obj.number_of_epochs is not None else None
+            ),
+            training_batch_size=(
+                _convert_from_rest_object(obj.training_batch_size) if obj.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_from_rest_object(obj.validation_batch_size) if obj.validation_batch_size is not None else None
+            ),
+            warmup_ratio=_convert_from_rest_object(obj.warmup_ratio) if obj.warmup_ratio is not None else None,
+            weight_decay=_convert_from_rest_object(obj.weight_decay) if obj.weight_decay is not None else None,
+        )
+
+    @classmethod
+    def _from_search_space_object(cls, obj: SearchSpace) -> "NlpSearchSpace":
+        return cls(
+            gradient_accumulation_steps=(
+                obj.gradient_accumulation_steps if hasattr(obj, "gradient_accumulation_steps") else None
+            ),
+            learning_rate=obj.learning_rate if hasattr(obj, "learning_rate") else None,
+            learning_rate_scheduler=obj.learning_rate_scheduler if hasattr(obj, "learning_rate_scheduler") else None,
+            model_name=obj.model_name if hasattr(obj, "model_name") else None,
+            number_of_epochs=obj.number_of_epochs if hasattr(obj, "number_of_epochs") else None,
+            training_batch_size=obj.training_batch_size if hasattr(obj, "training_batch_size") else None,
+            validation_batch_size=obj.validation_batch_size if hasattr(obj, "validation_batch_size") else None,
+            warmup_ratio=obj.warmup_ratio if hasattr(obj, "warmup_ratio") else None,
+            weight_decay=obj.weight_decay if hasattr(obj, "weight_decay") else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpSearchSpace):
+            return NotImplemented
+
+        return (
+            self.gradient_accumulation_steps == other.gradient_accumulation_steps
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.number_of_epochs == other.number_of_epochs
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_ratio == other.warmup_ratio
+            and self.weight_decay == other.weight_decay
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py
new file mode 100644
index 00000000..e446a30c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py
@@ -0,0 +1,65 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpSweepSettings as RestNlpSweepSettings
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SamplingAlgorithmType
+from azure.ai.ml.entities._job.sweep.early_termination_policy import EarlyTerminationPolicy
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+# pylint: disable=protected-access
+class NlpSweepSettings(RestTranslatableMixin):
+    """Sweep settings for all AutoML NLP tasks.
+
+    :param sampling_algorithm: Required. Specifies type of hyperparameter sampling algorithm.
+        Possible values include: "Grid", "Random", and "Bayesian".
+    :type sampling_algorithm: Union[str, ~azure.ai.ml.automl.SamplingAlgorithmType]
+    :param early_termination: Early termination policy to end poorly performing training candidates,
+        defaults to None.
+    :type early_termination: Optional[~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_sweep_settings]
+                :end-before: [END automl.nlp_sweep_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp sweep settings
+    """
+
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[str, SamplingAlgorithmType],
+        early_termination: Optional[EarlyTerminationPolicy] = None,
+    ):
+        self.sampling_algorithm = sampling_algorithm
+        self.early_termination = early_termination
+
+    def _to_rest_object(self) -> RestNlpSweepSettings:
+        return RestNlpSweepSettings(
+            sampling_algorithm=self.sampling_algorithm,
+            early_termination=self.early_termination._to_rest_object() if self.early_termination else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpSweepSettings) -> "NlpSweepSettings":
+        return cls(
+            sampling_algorithm=obj.sampling_algorithm,
+            early_termination=(
+                EarlyTerminationPolicy._from_rest_object(obj.early_termination) if obj.early_termination else None
+            ),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpSweepSettings):
+            return NotImplemented
+
+        return self.sampling_algorithm == other.sampling_algorithm and self.early_termination == other.early_termination
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py
new file mode 100644
index 00000000..290f4f70
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py
@@ -0,0 +1,248 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._restclient.v2023_04_01_preview.models._azure_machine_learning_workspaces_enums import (
+    ClassificationPrimaryMetrics,
+)
+from azure.ai.ml._restclient.v2024_01_01_preview.models import TextClassification as RestTextClassification
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextClassificationJob(AutoMLNLPJob):
+    """Configuration for AutoML Text Classification Job.
+
+    :param target_column_name: The name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model, defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed, defaults to None
+    :type primary_metric: Optional[~azure.ai.ml.automl.ClassificationPrimaryMetrics]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.automl_nlp_job.text_classification_job]
+                :end-before: [END automl.automl_nlp_job.text_classification_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text classification job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        target_column_name: Optional[str] = None,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[ClassificationPrimaryMetrics] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super().__init__(
+            task_type=TaskType.TEXT_CLASSIFICATION,
+            primary_metric=primary_metric or TextClassificationJob._DEFAULT_PRIMARY_METRIC,
+            target_column_name=target_column_name,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        """setter for primary metric
+
+        :param value: _description_
+        :type value: Union[str, ClassificationPrimaryMetrics]
+        """
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextClassificationJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_classification = RestTextClassification(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_classification)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_classification,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextClassificationJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextClassification = properties.task_details
+        assert isinstance(task_details, RestTextClassification)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_classification_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_classification_job._restore_data_inputs()
+
+        return text_classification_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "TextClassificationJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification import TextClassificationSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationNode
+
+            loaded_data = load_from_dict(
+                AutoMLTextClassificationNode,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(TextClassificationSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextClassificationJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextClassificationJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification import TextClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextClassificationNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextClassificationSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextClassificationJob):
+            return NotImplemented
+
+        if not super(TextClassificationJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py
new file mode 100644
index 00000000..ac19b451
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py
@@ -0,0 +1,252 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationMultilabelPrimaryMetrics, JobBase, TaskType
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    TextClassificationMultilabel as RestTextClassificationMultilabel,
+)
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextClassificationMultilabelJob(AutoMLNLPJob):
+    """Configuration for AutoML Text Classification Multilabel Job.
+
+    :param target_column_name: The name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model, defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed., defaults to None
+    :type primary_metric: Optional[str]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_classification_multilabel_job]
+                :end-before: [END automl.text_classification_multilabel_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text classification multilabel job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationMultilabelPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        target_column_name: Optional[str] = None,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super().__init__(
+            task_type=TaskType.TEXT_CLASSIFICATION_MULTILABEL,
+            primary_metric=primary_metric or TextClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC,
+            target_column_name=target_column_name,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationMultilabelPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationMultilabelPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationMultilabelPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_classification_multilabel = RestTextClassificationMultilabel(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_classification_multilabel)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_classification_multilabel,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextClassificationMultilabelJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextClassificationMultilabel = properties.task_details
+        assert isinstance(task_details, RestTextClassificationMultilabel)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_classification_multilabel_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_classification_multilabel_job._restore_data_inputs()
+
+        return text_classification_multilabel_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "TextClassificationMultilabelJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification_multilabel import (
+            TextClassificationMultilabelSchema,
+        )
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationMultilabelNode
+
+            loaded_data = load_from_dict(
+                AutoMLTextClassificationMultilabelNode,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(
+                TextClassificationMultilabelSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextClassificationMultilabelJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextClassificationMultilabelJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification_multilabel import (
+            TextClassificationMultilabelSchema,
+        )
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationMultilabelNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextClassificationMultilabelNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextClassificationMultilabelSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextClassificationMultilabelJob):
+            return NotImplemented
+
+        if not super(TextClassificationMultilabelJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py
new file mode 100644
index 00000000..a87965f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py
@@ -0,0 +1,231 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._restclient.v2023_04_01_preview.models._azure_machine_learning_workspaces_enums import (
+    ClassificationPrimaryMetrics,
+)
+from azure.ai.ml._restclient.v2024_01_01_preview.models import TextNer as RestTextNER
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextNerJob(AutoMLNLPJob):
+    """Configuration for AutoML Text NER Job.
+
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model,
+        defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed, defaults to None
+    :type primary_metric: Optional[str]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_ner_job]
+                :end-before: [END automl.text_ner_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text ner job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super(TextNerJob, self).__init__(
+            task_type=TaskType.TEXT_NER,
+            primary_metric=primary_metric or TextNerJob._DEFAULT_PRIMARY_METRIC,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextNerJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_ner = RestTextNER(
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_ner)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_ner,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextNerJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextNER = properties.task_details
+        assert isinstance(task_details, RestTextNER)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_ner_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_ner_job._restore_data_inputs()
+
+        return text_ner_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "TextNerJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_ner import TextNerSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextNerNode
+
+            loaded_data = load_from_dict(AutoMLTextNerNode, data, context, additional_message, **kwargs)
+        else:
+            loaded_data = load_from_dict(TextNerSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextNerJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextNerJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_ner import TextNerSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextNerNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextNerNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextNerSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextNerJob):
+            return NotImplemented
+
+        if not super(TextNerJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)