components token_classification_datapreprocess

Token Classification DataPreProcess

token_classification_datapreprocess

Overview

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

Version: 0.0.31

View in Studio: https://ml.azure.com/registries/azureml/components/token_classification_datapreprocess/version/0.0.31

Inputs

task arguments

sample input

{tokens_column: [ "EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "." ], ner_tags_column: '["B-ORG", "O", "B-MISC", "O", "O", "O", "B-MISC", "O", "O"]'}

For the above dataset pattern, token_key should be set as tokens_column and tag_key as ner_tags_column

Name	Description	Type	Default	Optional	Enum
token_key	Key for tokens in each example line	string		False
tag_key	Key for tags in each example line	string		False
batch_size	Number of examples to batch before calling the tokenization function	integer	1000	True

Tokenization params pad_to_max_length: type: string enum: - "true" - "false" default: "true" optional: true description: 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.

Name	Description	Type	Default	Optional	Enum
max_seq_length	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

Outputs

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

Environment

azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/39