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{
"cells": [
{
"cell_type": "markdown",
"id": "9949dcdb",
"metadata": {},
"source": [
"# Prepare SST data for gender annotation\n",
"\n",
"* Import SST data from huggingface\n",
"* Use word lists to automatically annotate (pre-annotate?) sentences for gender\n",
"* Subsample gendered sentences: 400 masculine, 400 feminine, 400 neutral\n",
"* Prepare CSVs for human annotation"
]
},
{
"cell_type": "markdown",
"id": "5badcb9d",
"metadata": {},
"source": [
"## Import SST data from huggingface"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "022eb689",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1f0b6c0f",
"metadata": {},
"outputs": [
{
"data": {
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"version_major": 2,
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"text/plain": [
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},
"metadata": {},
"output_type": "display_data"
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{
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"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No config specified, defaulting to: sst/default\n",
"Reusing dataset sst (/Users/katygero/.cache/huggingface/datasets/sst/default/1.0.0/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\n"
]
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{
"data": {
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"model_id": "7ece2dc64d6b4207949652de2450479a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"sst\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "453d8782",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sentence': \"The Rock is destined to be the 21st Century 's new `` Conan '' and that he 's going to make a splash even greater than Arnold Schwarzenegger , Jean-Claud Van Damme or Steven Segal .\",\n",
" 'label': 0.6944400072097778,\n",
" 'tokens': \"The|Rock|is|destined|to|be|the|21st|Century|'s|new|``|Conan|''|and|that|he|'s|going|to|make|a|splash|even|greater|than|Arnold|Schwarzenegger|,|Jean-Claud|Van|Damme|or|Steven|Segal|.\",\n",
" 'tree': '70|70|68|67|63|62|61|60|58|58|57|56|56|64|65|55|54|53|52|51|49|47|47|46|46|45|40|40|41|39|38|38|43|37|37|69|44|39|42|41|42|43|44|45|50|48|48|49|50|51|52|53|54|55|66|57|59|59|60|61|62|63|64|65|66|67|68|69|71|71|0'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#splits: ['test', 'train', 'validation']\n",
"dataset['train'][0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f44b9c0",
"metadata": {},
"outputs": [],
"source": [
"train = pd.DataFrame(dataset['train'])\n",
"train['split'] = 'train'\n",
"\n",
"val = pd.DataFrame(dataset['validation'])\n",
"val['split'] = 'validation'\n",
"\n",
"test = pd.DataFrame(dataset['test'])\n",
"test['split'] = 'test'\n",
"\n",
"data = pd.concat([train, val, test])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bfc658d6",
"metadata": {},
"outputs": [
{
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" <tr>\n",
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" <td>The|gorgeously|elaborate|continuation|of|``|Th...</td>\n",
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" <td>Yet the act is still charming here .</td>\n",
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" <td>An imaginative comedy\\/thriller .</td>\n",
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" <td>An|imaginative|comedy\\/thriller|.</td>\n",
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" <td>( A ) rare , beautiful film .</td>\n",
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" <th>2207</th>\n",
" <td>( An ) hilarious romantic comedy .</td>\n",
" <td>0.888890</td>\n",
" <td>(|An|)|hilarious|romantic|comedy|.</td>\n",
" <td>12|11|11|9|8|8|10|9|10|13|12|13|0</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2208</th>\n",
" <td>Never ( sinks ) into exploitation .</td>\n",
" <td>0.625000</td>\n",
" <td>Never|(|sinks|)|into|exploitation|.</td>\n",
" <td>11|10|9|9|8|8|13|12|10|11|12|13|0</td>\n",
" <td>test</td>\n",
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" <tr>\n",
" <th>2209</th>\n",
" <td>( U ) nrelentingly stupid .</td>\n",
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" <td>(|U|)|nrelentingly|stupid|.</td>\n",
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" sentence label \\\n",
"0 The Rock is destined to be the 21st Century 's... 0.694440 \n",
"1 The gorgeously elaborate continuation of `` Th... 0.833330 \n",
"2 Singer\\/composer Bryan Adams contributes a sle... 0.625000 \n",
"3 You 'd think by now America would have had eno... 0.500000 \n",
"4 Yet the act is still charming here . 0.722220 \n",
"... ... ... \n",
"2205 An imaginative comedy\\/thriller . 0.777780 \n",
"2206 ( A ) rare , beautiful film . 0.916670 \n",
"2207 ( An ) hilarious romantic comedy . 0.888890 \n",
"2208 Never ( sinks ) into exploitation . 0.625000 \n",
"2209 ( U ) nrelentingly stupid . 0.069444 \n",
"\n",
" tokens \\\n",
"0 The|Rock|is|destined|to|be|the|21st|Century|'s... \n",
"1 The|gorgeously|elaborate|continuation|of|``|Th... \n",
"2 Singer\\/composer|Bryan|Adams|contributes|a|sle... \n",
"3 You|'d|think|by|now|America|would|have|had|eno... \n",
"4 Yet|the|act|is|still|charming|here|. \n",
"... ... \n",
"2205 An|imaginative|comedy\\/thriller|. \n",
"2206 (|A|)|rare|,|beautiful|film|. \n",
"2207 (|An|)|hilarious|romantic|comedy|. \n",
"2208 Never|(|sinks|)|into|exploitation|. \n",
"2209 (|U|)|nrelentingly|stupid|. \n",
"\n",
" tree split \n",
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"1 71|70|69|69|67|67|66|64|63|62|62|61|61|58|57|5... train \n",
"2 72|71|71|70|68|68|67|67|66|63|62|62|60|60|58|5... train \n",
"3 36|35|34|33|33|32|30|29|27|26|25|24|23|23|22|2... train \n",
"4 15|13|13|10|9|9|11|12|10|11|12|14|14|15|0 train \n",
"... ... ... \n",
"2205 7|6|5|5|6|7|0 test \n",
"2206 13|12|12|11|10|9|9|15|10|11|14|13|14|15|0 test \n",
"2207 12|11|11|9|8|8|10|9|10|13|12|13|0 test \n",
"2208 11|10|9|9|8|8|13|12|10|11|12|13|0 test \n",
"2209 10|9|9|7|7|8|8|11|10|11|0 test \n",
"\n",
"[11855 rows x 5 columns]"
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},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.max_colwidth', 50)\n",
"data"
]
},
{
"cell_type": "markdown",
"id": "ee3a4945",
"metadata": {},
"source": [
"## Loosely annotate sentences (masc, femme, neutral) using word lists"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0e85a47a",
"metadata": {},
"outputs": [],
"source": [
"gender_words = {\n",
" 'femm': ['she', 'her', 'hers', 'mum', 'mom', 'mother', 'daughter', 'sister', 'niece', 'aunt', 'grandmother',\n",
" 'lady', 'woman', 'girl', \"ma'am\", 'female', 'wife', 'ms', 'miss', 'mrs', 'ms.', 'mrs.'],\n",
" 'masc': ['he', 'him', 'his', 'dad', 'father', 'son', 'brother', 'nephew', 'uncle', 'grandfather',\n",
" 'gentleman', 'man', 'boy', 'sir', 'male', 'husband', 'mr', 'mr.'],\n",
" 'neut': ['they', 'them', 'theirs', 'parent', 'child', 'sibling',\n",
" 'person', 'spouse']\n",
"}\n",
"\n",
"def label_gender(row):\n",
" tokens = row['tokens'].lower().split('|')\n",
" gender = 'none'\n",
" for key, words in gender_words.items():\n",
" for w in words:\n",
" if w in tokens or w+\"s\" in tokens:\n",
" if gender == 'none':\n",
" gender = key\n",
" else:\n",
" gender = 'mixed'\n",
" break\n",
" return gender\n",
"\n",
"data['gender'] = data.apply(lambda row: label_gender(row), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "46e8b885",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"none 10001\n",
"masc 931\n",
"neut 420\n",
"femm 372\n",
"mixed 131\n",
"Name: gender, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "91233c13",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"none 8113\n",
"masc 743\n",
"neut 350\n",
"femm 303\n",
"mixed 104\n",
"Name: gender, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_polarized = data[(data['label']>.6)|(data['label']<.4)]\n",
"data_polarized['gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8a7a12bb",
"metadata": {},
"outputs": [
{
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" <td>If Mr. Zhang 's subject matter is , to some degree at least , quintessentially American , his approach to storytelling might be called Iranian .</td>\n",
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" <td>( City ) reminds us how realistically nuanced a Robert De Niro performance can be when he is not more lucratively engaged in the shameless self-caricature of ` Analyze This ' ( 1999 ) and ` Analyze That , ' promised ( or threatened ) for later this year .</td>\n",
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" <td>It celebrates the group 's playful spark of nonconformity , glancing vividly back at what Hibiscus grandly called his ` angels of light . '</td>\n",
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" <td>Writer\\/director Alexander Payne ( Election ) and his co-writer Jim Taylor brilliantly employ their quirky and fearless ability to look American angst in the eye and end up laughing .</td>\n",
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" <td>Pacino is the best he 's been in years and Keener is marvelous .</td>\n",
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" <td>masc</td>\n",
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" <td>So purely enjoyable that you might not even notice it 's a fairly straightforward remake of Hollywood comedies such as Father of the Bride .</td>\n",
" <td>0.79167</td>\n",
" <td>masc</td>\n",
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" <th>150</th>\n",
" <td>Robin Williams has thankfully ditched the saccharine sentimentality of Bicentennial Man in favour of an altogether darker side .</td>\n",
" <td>0.63889</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>153</th>\n",
" <td>Hoffman 's performance is authentic to the core of his being .</td>\n",
" <td>0.73611</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Too often , Son of the Bride becomes an exercise in trying to predict when a preordained `` big moment '' will occur and not `` if . ''</td>\n",
" <td>0.30556</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>A solid piece of journalistic work that draws a picture of a man for whom political expedience became a deadly foreign policy .</td>\n",
" <td>0.54167</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>In The Pianist , Polanski is saying what he has long wanted to say , confronting the roots of his own preoccupations and obsessions , and he allows nothing to get in the way .</td>\n",
" <td>0.72222</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>With Dirty Deeds , David Caesar has stepped into the mainstream of filmmaking with an assurance worthy of international acclaim and with every cinematic tool well under his control -- driven by a natural sense for what works on screen .</td>\n",
" <td>0.81944</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>What 's not to like about a movie with a ` children 's ' song that includes the line ` My stepdad 's not mean , he 's just adjusting ' ?</td>\n",
" <td>0.72222</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>This English-language version ... does full honor to Miyazaki 's teeming and often unsettling landscape , and to the conflicted complexity of his characters .</td>\n",
" <td>0.58333</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>In its dry and forceful way , it delivers the same message as Jiri Menzel 's Closely Watched Trains and Danis Tanovic 's No Man 's Land .</td>\n",
" <td>0.68056</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>Beresford nicely mixes in as much humor as pathos to take us on his sentimental journey of the heart .</td>\n",
" <td>0.62500</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>Visually fascinating ... an often intense character study about fathers and sons , loyalty and duty .</td>\n",
" <td>0.70833</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentence \\\n",
"0 The Rock is destined to be the 21st Century 's new `` Conan '' and that he 's going to make a splash even greater than Arnold Schwarzenegger , Jean-Claud Van Damme or Steven Segal . \n",
"43 `` Auto Focus '' works as an unusual biopic and document of male swingers in the Playboy era \n",
"44 If Mr. Zhang 's subject matter is , to some degree at least , quintessentially American , his approach to storytelling might be called Iranian . \n",
"52 ( City ) reminds us how realistically nuanced a Robert De Niro performance can be when he is not more lucratively engaged in the shameless self-caricature of ` Analyze This ' ( 1999 ) and ` Analyze That , ' promised ( or threatened ) for later this year . \n",
"90 Allen shows he can outgag any of those young whippersnappers making moving pictures today . \n",
"98 It celebrates the group 's playful spark of nonconformity , glancing vividly back at what Hibiscus grandly called his ` angels of light . ' \n",
"136 Writer\\/director Alexander Payne ( Election ) and his co-writer Jim Taylor brilliantly employ their quirky and fearless ability to look American angst in the eye and end up laughing . \n",
"141 Pacino is the best he 's been in years and Keener is marvelous . \n",
"146 So purely enjoyable that you might not even notice it 's a fairly straightforward remake of Hollywood comedies such as Father of the Bride . \n",
"150 Robin Williams has thankfully ditched the saccharine sentimentality of Bicentennial Man in favour of an altogether darker side . \n",
"153 Hoffman 's performance is authentic to the core of his being . \n",
"168 Too often , Son of the Bride becomes an exercise in trying to predict when a preordained `` big moment '' will occur and not `` if . '' \n",
"170 A solid piece of journalistic work that draws a picture of a man for whom political expedience became a deadly foreign policy . \n",
"181 In The Pianist , Polanski is saying what he has long wanted to say , confronting the roots of his own preoccupations and obsessions , and he allows nothing to get in the way . \n",
"194 With Dirty Deeds , David Caesar has stepped into the mainstream of filmmaking with an assurance worthy of international acclaim and with every cinematic tool well under his control -- driven by a natural sense for what works on screen . \n",
"230 What 's not to like about a movie with a ` children 's ' song that includes the line ` My stepdad 's not mean , he 's just adjusting ' ? \n",
"231 This English-language version ... does full honor to Miyazaki 's teeming and often unsettling landscape , and to the conflicted complexity of his characters . \n",
"240 In its dry and forceful way , it delivers the same message as Jiri Menzel 's Closely Watched Trains and Danis Tanovic 's No Man 's Land . \n",
"249 Beresford nicely mixes in as much humor as pathos to take us on his sentimental journey of the heart . \n",
"255 Visually fascinating ... an often intense character study about fathers and sons , loyalty and duty . \n",
"\n",
" label gender \n",
"0 0.69444 masc \n",
"43 0.65278 masc \n",
"44 0.52778 masc \n",
"52 0.63889 masc \n",
"90 0.76389 masc \n",
"98 0.72222 masc \n",
"136 0.75000 masc \n",
"141 0.88889 masc \n",
"146 0.79167 masc \n",
"150 0.63889 masc \n",
"153 0.73611 masc \n",
"168 0.30556 masc \n",
"170 0.54167 masc \n",
"181 0.72222 masc \n",
"194 0.81944 masc \n",
"230 0.72222 masc \n",
"231 0.58333 masc \n",
"240 0.68056 masc \n",
"249 0.62500 masc \n",
"255 0.70833 masc "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.max_colwidth', None)\n",
"pd.set_option('display.max_rows', None)\n",
"data[['sentence', 'label', 'gender']][data['gender'] == 'masc'][:20]"
]
},
{
"cell_type": "markdown",
"id": "b544c0c9",
"metadata": {},
"source": [
"## Look at distribution of data"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "9eaa3478",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='gender', ylabel='label'>"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import seaborn as sns\n",
"\n",
"sns.barplot(data=data, x='gender', y='label')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "379620ab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='gender', ylabel='count'>"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data=data, x='gender')"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "bd177487",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='gender', ylabel='count'>"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data=data[data['gender']!='none'], x='gender')"
]
},
{
"cell_type": "markdown",
"id": "c675abea",
"metadata": {},
"source": [
"## Subsample for 400 from each category (masc, femm, neut)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "fc4a6180",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>sentence</th>\n",
" <th>label</th>\n",
" <th>tokens</th>\n",
" <th>tree</th>\n",
" <th>split</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8204</td>\n",
" <td>As pedestrian as they come .</td>\n",
" <td>0.18056</td>\n",
" <td>As|pedestrian|as|they|come|.</td>\n",
" <td>10|9|9|8|7|7|8|11|10|11|0</td>\n",
" <td>train</td>\n",
" <td>neut</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2150</td>\n",
" <td>Oft-described as the antidote to American Pie-...</td>\n",
" <td>0.33333</td>\n",
" <td>Oft-described|as|the|antidote|to|American|Pie-...</td>\n",
" <td>55|54|53|53|52|51|50|49|49|48|47|46|43|41|41|4...</td>\n",
" <td>test</td>\n",
" <td>neut</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3960</td>\n",
" <td>I have a confession to make : I did n't partic...</td>\n",
" <td>0.25000</td>\n",
" <td>I|have|a|confession|to|make|:|I|did|n't|partic...</td>\n",
" <td>44|43|42|41|40|40|45|39|36|36|37|34|34|32|31|3...</td>\n",
" <td>train</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1308</td>\n",
" <td>Either a fascinating study of the relationship...</td>\n",
" <td>0.50000</td>\n",
" <td>Either|a|fascinating|study|of|the|relationship...</td>\n",
" <td>42|38|37|37|36|34|34|33|33|40|30|30|31|28|27|2...</td>\n",
" <td>train</td>\n",
" <td>femm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2272</td>\n",
" <td>Manages to please its intended audience -- chi...</td>\n",
" <td>0.72222</td>\n",
" <td>Manages|to|please|its|intended|audience|--|chi...</td>\n",
" <td>35|33|31|29|28|28|27|26|26|25|23|22|22|21|20|1...</td>\n",
" <td>train</td>\n",
" <td>neut</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1167</th>\n",
" <td>5807</td>\n",
" <td>( Janey ) forgets about her other obligations ...</td>\n",
" <td>0.23611</td>\n",
" <td>(|Janey|)|forgets|about|her|other|obligations|...</td>\n",
" <td>74|73|73|69|68|67|66|66|70|65|64|62|62|61|57|5...</td>\n",
" <td>train</td>\n",
" <td>femm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1168</th>\n",
" <td>7125</td>\n",
" <td>It 's a frightful vanity film that , no doubt ...</td>\n",
" <td>0.11111</td>\n",
" <td>It|'s|a|frightful|vanity|film|that|,|no|doubt|...</td>\n",
" <td>43|41|39|38|37|37|36|35|33|33|32|30|30|29|27|2...</td>\n",
" <td>train</td>\n",
" <td>neut</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1169</th>\n",
" <td>143</td>\n",
" <td>It 's a hoot and a half , and a great way for ...</td>\n",
" <td>0.79167</td>\n",
" <td>It|'s|a|hoot|and|a|half|,|and|a|great|way|for|...</td>\n",
" <td>67|64|58|58|59|57|57|61|62|55|54|54|53|52|51|5...</td>\n",
" <td>test</td>\n",
" <td>masc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1170</th>\n",
" <td>4452</td>\n",
" <td>Eventually , they will have a showdown , but ,...</td>\n",
" <td>0.18056</td>\n",
" <td>Eventually|,|they|will|have|a|showdown|,|but|,...</td>\n",
" <td>61|60|52|51|50|49|49|53|54|55|47|47|46|44|44|4...</td>\n",
" <td>train</td>\n",
" <td>neut</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1171</th>\n",
" <td>2812</td>\n",
" <td>Instead , she sees it as a chance to revitaliz...</td>\n",
" <td>0.68056</td>\n",
" <td>Instead|,|she|sees|it|as|a|chance|to|revitaliz...</td>\n",
" <td>41|40|39|36|36|35|34|33|32|31|30|28|28|27|26|2...</td>\n",
" <td>train</td>\n",
" <td>femm</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1172 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" index sentence label \\\n",
"0 8204 As pedestrian as they come . 0.18056 \n",
"1 2150 Oft-described as the antidote to American Pie-... 0.33333 \n",
"2 3960 I have a confession to make : I did n't partic... 0.25000 \n",
"3 1308 Either a fascinating study of the relationship... 0.50000 \n",
"4 2272 Manages to please its intended audience -- chi... 0.72222 \n",
"... ... ... ... \n",
"1167 5807 ( Janey ) forgets about her other obligations ... 0.23611 \n",
"1168 7125 It 's a frightful vanity film that , no doubt ... 0.11111 \n",
"1169 143 It 's a hoot and a half , and a great way for ... 0.79167 \n",
"1170 4452 Eventually , they will have a showdown , but ,... 0.18056 \n",
"1171 2812 Instead , she sees it as a chance to revitaliz... 0.68056 \n",
"\n",
" tokens \\\n",
"0 As|pedestrian|as|they|come|. \n",
"1 Oft-described|as|the|antidote|to|American|Pie-... \n",
"2 I|have|a|confession|to|make|:|I|did|n't|partic... \n",
"3 Either|a|fascinating|study|of|the|relationship... \n",
"4 Manages|to|please|its|intended|audience|--|chi... \n",
"... ... \n",
"1167 (|Janey|)|forgets|about|her|other|obligations|... \n",
"1168 It|'s|a|frightful|vanity|film|that|,|no|doubt|... \n",
"1169 It|'s|a|hoot|and|a|half|,|and|a|great|way|for|... \n",
"1170 Eventually|,|they|will|have|a|showdown|,|but|,... \n",
"1171 Instead|,|she|sees|it|as|a|chance|to|revitaliz... \n",
"\n",
" tree split gender \n",
"0 10|9|9|8|7|7|8|11|10|11|0 train neut \n",
"1 55|54|53|53|52|51|50|49|49|48|47|46|43|41|41|4... test neut \n",
"2 44|43|42|41|40|40|45|39|36|36|37|34|34|32|31|3... train masc \n",
"3 42|38|37|37|36|34|34|33|33|40|30|30|31|28|27|2... train femm \n",
"4 35|33|31|29|28|28|27|26|26|25|23|22|22|21|20|1... train neut \n",
"... ... ... ... \n",
"1167 74|73|73|69|68|67|66|66|70|65|64|62|62|61|57|5... train femm \n",
"1168 43|41|39|38|37|37|36|35|33|33|32|30|30|29|27|2... train neut \n",
"1169 67|64|58|58|59|57|57|61|62|55|54|54|53|52|51|5... test masc \n",
"1170 61|60|52|51|50|49|49|53|54|55|47|47|46|44|44|4... train neut \n",
"1171 41|40|39|36|36|35|34|33|32|31|30|28|28|27|26|2... train femm \n",
"\n",
"[1172 rows x 7 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subbed_data = []\n",
"for genderword in ['masc', 'femm', 'neut']:\n",
" local_data = data[data['gender'] == genderword]\n",
" if local_data.shape[0] < 400:\n",
" subbed_data.append(local_data)\n",
" else:\n",
" subbed_data.append(local_data.sample(n=400, replace=False))\n",
" \n",
"# shuffle data using '.sample(frac=1)'\n",
"subsample_data = pd.concat(subbed_data).sample(frac=1).reset_index()\n",
"\n",
"\n",
"pd.reset_option('^display.', silent=True)\n",
"\n",
"subsample_data"
]
},
{
"cell_type": "markdown",
"id": "68dd053c",
"metadata": {},
"source": [
"## Sample 99 sentences for each annotator as test run"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c6a11844",
"metadata": {},
"outputs": [],
"source": [
"idx = 0\n",
"c = 99 # count\n",
"for annotator in ['katy', 'fatma', 'anna', 'nathan', 'aashka']:\n",
" subset = subsample_data.loc[idx:idx+c, ['index', 'sentence', 'label', 'gender']]\n",
" subset.to_csv(f\"sentiment-bias-annotations_{annotator}_{idx}-{idx+c}.csv\", float_format='{:,.2f}'.format, index_label='annotation_index')\n",
" idx += int(c/3)"
]
},
{
"cell_type": "markdown",
"id": "a9b8639b",
"metadata": {},
"source": [
"## Prepare rest of annotations"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "416bcaa3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_datapoints 787\n",
"total_annotations 2361\n",
"annotations_per_person 590.25\n",
"completed_annotations 396\n",
"remaining_annotations_per_person 491.25\n"
]
}
],
"source": [
"rawdata = pd.read_csv('sentiment_data_for_annotation.csv')\n",
"\n",
"info = {\n",
" 'total_datapoints': rawdata.shape[0],\n",
" 'total_annotations': rawdata.shape[0] * 3,\n",
" 'annotations_per_person': rawdata.shape[0] * 3/4,\n",
" 'completed_annotations': 99*4,\n",
" 'remaining_annotations_per_person': (rawdata.shape[0] * 3 - 99*4)/4\n",
"}\n",
"\n",
"for key, val in info.items():\n",
" print(key, val)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "32c173b8",
"metadata": {},
"outputs": [],
"source": [
"c = 591\n",
"start = 0\n",
"total = rawdata.shape[0]\n",
"\n",
"for annotator in ['katy', 'fatma', 'anna', 'nathan']:\n",
" end = start + c\n",
" subset = rawdata.loc[start:end, ['index', 'sentence', 'label', 'gender']]\n",
" \n",
" if end > total:\n",
" looped_idx = end - total\n",
" second_subset = rawdata.loc[:looped_idx, ['index', 'sentence', 'label', 'gender']]\n",
" subset = pd.concat([subset, second_subset])\n",
" end = looped_idx\n",
" \n",
" subset.to_csv(f\"sst-annotations-v3_{annotator}_{start}-{end}.csv\", \n",
" float_format='{:,.2f}'.format, \n",
" index_label='annotation_index')\n",
" start += int(c/3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42736e58",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|