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Hate, Abuse, and Profanity (HAP) Annotation

Please see the set of transform project conventions for details on general project conventions, transform configuration, testing and IDE set up.

Contributor

Description

Prerequisite

This repository needs NLTK and please refer to requirements.txt.

Overview

The hap transform maps a non-empty input table to an output table with an added hap_score column. Each row in the table represents a document, and the hap transform performs the following three steps to calculate the hap score for each document:

  • Sentence spliting: we use NLTK to split the document into sentence pieces.
  • hap annotation: each sentence is assigned a hap score between 0 and 1, where 1 represents hap and 0 represents non-hap.
  • Aggregation: the document hap score is determined by selecting the maximum hap score among its sentences.

input format

The input is in .parquet format and contains the following columns:

doc_id contents
1 GSC is very much a little Swiss Army knife for...
2 Here are only a few examples. And no, I'm not ...

output format

The output is in .parquet format and includes an additional column, in addition to those in the input:

doc_id contents hap_score
1 GSC is very much a little Swiss Army knife for... 0.002463
2 Here are only a few examples. And no, I'm not ... 0.989713

Configuration

The set of dictionary keys holding HAPTransformConfiguration configuration for values are as follows:

  • --model_name_or_path - specify the HAP model, which should be compatible with HuggingFace's AutoModelForSequenceClassification. Defaults to IBM's open-source toxicity classifier ibm-granite/granite-guardian-hap-38m.
  • --batch_size - modify it based on the infrastructure capacity. Defaults to 128.
  • --max_length - the maximum length for the tokenizer. Defaults to 512.
  • --doc_text_column - the column name containing the document text in the input .parquet file. Defaults to contents.
  • --annotation_column - the column name containing hap (toxicity) score in the output .parquet file. Defaults to hap_score.

Usage

Place your input Parquet file in the test-data/input/ directory. A sample file, test1.parquet, is available in this directory. Once done, run the script.

python hap_local_python.py

You will obtain the output file test1.parquet in the output directory.

Code example

notebook

Transforming data using the transform image

To use the transform image to transform your data, please refer to the running images quickstart, substituting the name of this transform image and runtime as appropriate.

Testing

Currently we have:

Throughput

The table below shows the throughput (tokens per second) of the HAP transform module, which primarily includes sentence splitting, HAP annotation, and HAP score aggregation. We herein compare two models:

We processed 6,000 documents (12 MB in Parquet file size) using the HAP transform module and reported the average CPU throughput over three trials.

Model used in HAP transform module throughput (tokens per second)
granite-guardian-hap-38m 6.16 k
granite-guardian-hap-125m 1.14 k

Credits

The HAP transform is jointly developed by IBM Research - Tokyo and Yorktown.