Component to preprocess data for single label classification task. See docs to learn more.

Version: 0.0.63

View in Studio: https://ml.azure.com/registries/azureml/components/text_classification_datapreprocess/version/0.0.63

Text Claasification task type

Name	Description	Type	Default	Optional	Enum
task_name	Text Classification task type	string	SingleLabelClassification	False	['SingleLabelClassification']

task arguments sample input1 {"sentence":"Our friends won't buy this analysis, let alone the next one we propose.","label":true,"idx":0} For this setting, sentence1_key is sentence, and label_key is label. The optional parameter sentence2_key can be ignored sample input2 {"sentence1":"Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .","sentence2":"Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .","label":1,"idx":0} If your dataset follows above pattern, sentence1_key should be set as sentence1 and sentence2_key as sentence2 label_key as label.

Name	Description	Type	Default	Optional	Enum
sentence1_key	Key for sentence1_key in each example line	string		False
sentence2_key	Key for sentence2_key in each example line	string		True
label_key	label key in each example line	string		False
batch_size	Number of examples to batch before calling the tokenization function	integer	1000	True

Tokenization params

Name	Description	Type	Default	Optional	Enum
pad_to_max_length	If set to True, the returned sequences will be padded according to the model's padding side and padding index, up to their max_seq_length. If no max_seq_length is specified, the padding is done up to the model's max length.	string	false	True	['true', 'false']
max_seq_length	Controls the maximum length to use when pad_to_max_length parameter is set to true. Default is -1 which means the padding is done up to the model's max length. Else will be padded to max_seq_length.	integer	-1	True

Data inputs Please note that either train_file_path or train_mltable_path needs to be passed. In case both are passed, mltable path will take precedence. The validation and test paths are optional and an automatic split from train data happens if they are not passed. If both validation and test files are missing, 10% of train data will be assigned to each of them and the remaining 80% will be used for training If anyone of the file is missing, 20% of the train data will be assigned to it and the remaining 80% will be used for training

Name	Description	Type	Default	Optional	Enum
train_file_path	Path to the registered training data asset. The supported data formats are jsonl, json, csv, tsv and parquet.	uri_file		True
validation_file_path	Path to the registered validation data asset. The supported data formats are jsonl, json, csv, tsv and parquet.	uri_file		True
test_file_path	Path to the registered test data asset. The supported data formats are jsonl, json, csv, tsv and parquet.	uri_file		True
train_mltable_path	Path to the registered training data asset in mltable format.	mltable		True
validation_mltable_path	Path to the registered validation data asset in mltable format.	mltable		True
test_mltable_path	Path to the registered test data asset in mltable format.	mltable		True

Dataset parameters

Name	Description	Type	Default	Optional	Enum
model_selector_output	output folder of model selector containing model metadata like config, checkpoints, tokenizer config	uri_folder		False

Name	Description	Type
output_dir	The folder contains the tokenized output of the train, validation and test data along with the tokenizer files used to tokenize the data	uri_folder

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