components question_answering_datapreprocess

Question Answering DataPreProcess

question_answering_datapreprocess

Overview

Component to preprocess data for question answering task. See docs to learn more.

Version: 0.0.65

View in Studio: https://ml.azure.com/registries/azureml/components/question_answering_datapreprocess/version/0.0.65

Inputs

Task arguments

Sample example

{question_column: "In what year did Paul VI formally appoint Mary as mother of the Catholic church?", context_column: "Paul VI opened the third period on 14 September 1964, telling the Council Fathers that he viewed the text about the Church as the most important document to come out from the Council. As the Council discussed the role of bishops in the papacy, Paul VI issued an explanatory note confirming the primacy of the papacy, a step which was viewed by some as meddling in the affairs of the Council American bishops pushed for a speedy resolution on religious freedom, but Paul VI insisted this to be approved together with related texts such as ecumenism. The Pope concluded the session on 21 November 1964, with the formal pronouncement of Mary as Mother of the Church.", answers_column: {answer_start_column: [595], text_column: ['1964']}}

If the dataset follows above pattern, question_key: "question_column"; context_key: "context_column"; answers_key: answers_column; answer_start_key: answer_start_column; answer_text_key: text_column

Name	Description	Type	Default	Optional	Enum
question_key	The question whose answer needs to be extracted from the provided context	string		False
context_key	The context that contains the answer to the question	string		False
answers_key	The value of this field is text in JSON format with two nested keys: answer_start_key and answer_text_key with their corresponding values	string		False
answer_start_key	Refers to the position where the answer beings in context. Needs a value that maps to a nested key in the values of the answers_key parameter	string		False
answer_text_key	Contains the answer to the question. Needs a value that maps to a nested key in the values of the answers_key parameter	string		False
doc_stride	The amount of context overlap to keep in case the number of tokens per example exceed max_seq_length	integer	128	True
n_best_size	The top_n max probable start tokens and end tokens to be consider while generating possible answers.	integer	20	True
max_answer_length_in_tokens	The maximum allowed answer length specified in token length. The default value for this parameter is 30. All the answers with above 30 tokens will not be considered as a possible answer.	integer	30	True
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

Model input

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/80