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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: ml_samples_automl_nlp.py
DESCRIPTION:
These samples demonstrate how to use AutoML NLP functions
USAGE:
python ml_samples_automl_nlp.py
"""
import os
class AutoMLNLPSamples(object):
def automl_nlp_jobs(self):
# [START automl.text_classification]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
test_classification_job = automl.text_classification(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="Sentiment",
primary_metric="accuracy",
tags={"my_custom_tag": "My custom value"},
)
# [END automl.text_classification]
# [START automl.text_classification_multilabel]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
text_classification_multilabel_job = automl.text_classification_multilabel(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="terms",
primary_metric="accuracy",
tags={"my_custom_tag": "My custom value"},
)
# [END automl.text_classification_multilabel]
# [START automl.text_ner]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
text_ner_job = automl.text_ner(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
tags={"my_custom_tag": "My custom value"},
)
# [END automl.text_ner]
# [START automl.automl_nlp_job.text_classification_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
text_classification_job = automl.TextClassificationJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="terms",
tags={"my_custom_tag": "My custom value"},
)
# [END automl.automl_nlp_job.text_classification_job]
# [START automl.text_classification_multilabel_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
text_classification_multilabel_job = automl.TextClassificationMultilabelJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="terms",
primary_metric="accuracy",
tags={"my_custom_tag": "My custom value"},
)
# [END automl.text_classification_multilabel_job]
# [START automl.text_ner_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
text_ner_job = automl.TextNerJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
tags={"my_custom_tag": "My custom value"},
)
# [END automl.text_ner_job]
# [START automl.nlp_sweep_settings]
from azure.ai.ml import automl
from azure.ai.ml.sweep import BanditPolicy
nlp_sweep_settings = automl.NlpSweepSettings(
sampling_algorithm="Grid",
early_termination=BanditPolicy(evaluation_interval=2, slack_factor=0.05, delay_evaluation=6),
)
# [END automl.nlp_sweep_settings]
# [START automl.nlp_search_space]
from azure.ai.ml import automl
from azure.ai.ml.constants import NlpLearningRateScheduler
from azure.ai.ml.sweep import Uniform
nlp_search_space = automl.NlpSearchSpace(
learning_rate_scheduler=NlpLearningRateScheduler.LINEAR,
warmup_ratio=0.1,
model_name="roberta-base",
weight_decay=Uniform(0.01, 0.1),
)
# [END automl.nlp_search_space]
# [START automl.nlp_limit_settings]
from azure.ai.ml import automl
nlp_limit_settings = automl.NlpLimitSettings(
max_concurrent_trials=2, max_trials=4, max_nodes=4, timeout_minutes=120
)
# [END automl.nlp_limit_settings]
# [START automl.nlp_fixed_parameters]
from azure.ai.ml import automl
from azure.ai.ml.constants import NlpLearningRateScheduler
nlp_fixed_parameters = automl.NlpFixedParameters(
model_name="roberta-base",
learning_rate_scheduler=NlpLearningRateScheduler.LINEAR,
warmup_ratio=0.1,
)
# [END automl.nlp_fixed_parameters]
# [START automl.nlp_featurization_settings]
from azure.ai.ml import automl
nlp_featurization_settings = automl.NlpFeaturizationSettings(dataset_language="eng")
# [END automl.nlp_featurization_settings]
if __name__ == "__main__":
sample = AutoMLNLPSamples()
sample.automl_nlp_jobs()
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