Using Sagemaker clarify shap value for token level explanation

philschmid · May 11, 2022, 5:02pm

@MaximusDecimusMeridi the AWS team shared with me this example:

aws/amazon-sagemaker-examples/blob/main/sagemaker_processing/fairness_and_explainability/text_explainability/text_explainability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explaining text sentiment analysis using SageMaker Clarify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. [Overview](#Overview)\n",
    " 1. [Prerequisites and Data](#Prerequisites-and-Data)\n",
    "     1. [Initialize SageMaker](#Initialize-SageMaker)\n",
    "     1. [Loading the data: Women's Ecommerce clothing reviews Dataset](#Loading-the-data:-Women's-ecommerce-clothing-reviews-dataset) \n",
    "     1. [Data preparation for model training](#Data-preparation-for-model-training) \n",
    " 1. [Train and Deploy Hugging Face Model](#Train-and-Deploy-Hugging-Face-Model)\n",
    "     1. [Train model with Hugging Face estimator](#Train-model-with-Hugging-Face-estimator)\n",

This file has been truncated. show original

i haven’t looked at it. So would be nice if you could report back on this and the experience

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Using Sagemaker clarify shap value for token level explanation

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