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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NUBIA: A SoTA evaluation metric for text generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import and initialize the Nubia class from nubia.py (wherever you cloned the repo)\n",
"See our [colab notebook](https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg) as an example\n",
"\n",
"Note: The first time you initialize the class it will download the pretrained models from the S3 bucket, this could take a while depending on your internet connection."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from nubia import Nubia"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading archive file pretrained/roBERTa_STS\n",
"| [input] dictionary: 50265 types\n",
"loading archive file pretrained/roBERTa_MNLI\n",
"| dictionary: 50264 types\n"
]
}
],
"source": [
"nubia = Nubia()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### You're now ready to start evaluating! \n",
"\n",
"`nubia.score` takes 7 parameters: `(ref, hyp, verbose=False, get_features=False, six_dim=False, aggregator=\"agg_one\")`\n",
"\n",
"`ref` and `hyp` are the strings nubia will compare. \n",
"\n",
"Setting `get_features` to `True` will return a dictionary with additional features (semantic relation, contradiction, irrelevancy, logical agreement, and grammaticality) aside from the nubia score. `Verbose=True` prints all the features.\n",
"\n",
"`six_dim = True` will use a six dimensional \n",
"\n",
"`aggregator` is set to `agg_one` by default, but you may choose to try `agg_two` which is"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.9905961979783401"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nubia.score(\"The dinner was delicious.\", \"It was a tasty dinner.\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.601254306957781"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Semantic relation: 1.4594818353652954/5.0\n",
"Percent chance of contradiction: 99.90345239639282%\n",
"Percent chance of irrelevancy or new information: 0.06429857457987964%\n",
"Percent chance of logical agreement: 0.03225349937565625%\n",
"\n",
"NUBIA score: 0.601254306957781/1.0\n"
]
},
{
"data": {
"text/plain": [
"{'nubia_score': 0.601254306957781,\n",
" 'features': {'semantic_relation': 1.4594818353652954,\n",
" 'contradiction': 99.90345239639282,\n",
" 'irrelevancy': 0.06429857457987964,\n",
" 'logical_agreement': 0.03225349937565625,\n",
" 'grammar_ref': 5.1724853515625,\n",
" 'grammar_hyp': 4.905452728271484}}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\", verbose=True, get_features=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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