{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SHAP for sentiment analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An example of SHAP on sentiment analysis. The SHAP explainer for NLP tasks only supports `TextClassificationPipeline` in the `transformer` library. If using this explainer, please cite the original work: https://github.com/slundberg/shap."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import transformers\n",
"from omnixai.data.text import Text\n",
"from omnixai.explainers.nlp import ShapText"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# A transformer model for sentiment analysis\n",
"model = transformers.pipeline(\n",
" 'sentiment-analysis',\n",
" model='distilbert-base-uncased-finetuned-sst-2-english',\n",
" return_all_scores=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/156 [00:00Instance 0: Class POSITIVE\n",
" What a great movie ! if you have no taste .
\n",
"Instance 1: Class POSITIVE
\n",
" it was a fantastic performance !
\n",
"Instance 2: Class POSITIVE
\n",
" best film ever
\n",
"Instance 3: Class POSITIVE
\n",
" such a great show !
\n",
"Instance 4: Class NEGATIVE
\n",
" it was a horrible movie
\n",
"Instance 5: Class POSITIVE
\n",
" i ' ve never watched something as bad
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = Text([\n",
" \"What a great movie! if you have no taste.\",\n",
" \"it was a fantastic performance!\",\n",
" \"best film ever\",\n",
" \"such a great show!\",\n",
" \"it was a horrible movie\",\n",
" \"i've never watched something as bad\"\n",
"])\n",
"explainer = ShapText(model=model)\n",
"explanations = explainer.explain(x)\n",
"explanations.ipython_plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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