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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "spiritual-swift",
"metadata": {},
"outputs": [],
"source": [
"%config Completer.use_jedi = False\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "stopped-single",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow\n",
"import regex"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "numeric-handle",
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "numerous-overall",
"metadata": {},
"outputs": [],
"source": [
"from email_parser import nlp"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "studied-oracle",
"metadata": {},
"outputs": [],
"source": [
"text = \"\"\"tel: 512 222 5555\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "pacific-walter",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'en'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lang = nlp.f_detect_language(text)\n",
"lang"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "every-gardening",
"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>entity</th>\n",
" <th>value</th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>TEL</td>\n",
" <td>512 222 5555</td>\n",
" <td>5</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" entity value start end score\n",
"0 TEL 512 222 5555 5 17 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_result = nlp.f_ner(text, lang=lang)\n",
"df_result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "operating-recorder",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"id": "delayed-overhead",
"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>entity</th>\n",
" <th>value</th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SIGNATURE</td>\n",
" <td>JB</td>\n",
" <td>119</td>\n",
" <td>122</td>\n",
" <td>0.955208</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" entity value start end score\n",
"0 SIGNATURE JB 119 122 0.955208"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nlp.f_detect_email_signature(text, lang=\"fr\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "frozen-jones",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('je', None), (\"m'appelle\", None), ('Jean-Baptiste', 'PER')]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iter_match = regex.finditer(\"\\s|$\", text)\n",
"list_values = []\n",
"start_pos = 0\n",
"for match in iter_match:\n",
" word = match.string[start_pos:match.start()]\n",
" \n",
" df_entity = df_result.query(f\"start>={start_pos} & end<={match.start()}\").head(1)\n",
" if len(df_entity)==1:\n",
" entity = df_entity[\"entity\"].values[0]\n",
" else:\n",
" entity = None\n",
"# list_values\n",
" list_values.append((word, entity))\n",
" start_pos = match.end()\n",
"list_values\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "solid-speaker",
"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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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