{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42aaa537-ab3b-46d2-a12b-d0355669bf26",
   "metadata": {},
   "source": [
    "# ZeroShotClassification\n",
    "\n",
    "Experimenting with a ZeroShotClassification Pipeline.\n",
    "\n",
    "This works very good. Even \"login id\" was tagged as \"username\" at 20% confidence. Based on testing we will not consider below 18%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8e72fc9-198e-43c6-8045-aaf811145e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "from faker import Faker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "620682d6-fd51-47b7-b99b-a32434c28044",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake = Faker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c5d29b2-2d9f-4009-9f55-690af4be7ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of Faker functions with descriptive labels\n",
    "faker_functions = {\n",
    "    \"person name\": fake.name,\n",
    "    \"first name\": fake.first_name,\n",
    "    \"last name\": fake.last_name,\n",
    "    \"email address\": fake.email,\n",
    "    \"phone number\": fake.phone_number,\n",
    "    \"street address\": fake.street_address,\n",
    "    \"city name\": fake.city,\n",
    "    \"state name\": fake.state,\n",
    "    \"country name\": fake.country,\n",
    "    \"zip code\": fake.zipcode,\n",
    "    \"job title\": fake.job,\n",
    "    \"company name\": fake.company,\n",
    "    \"credit card number\": fake.credit_card_number,\n",
    "    \"date of birth\": fake.date_of_birth,\n",
    "    \"username\": fake.user_name,\n",
    "    \"website url\": fake.url,\n",
    "    \"paragraph text\": fake.paragraph,\n",
    "    \"sentence text\": fake.sentence\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "998e0fa8-0ed5-4b39-8f5e-732628ace900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use mps:0\n"
     ]
    }
   ],
   "source": [
    "pipe = pipeline(model=\"facebook/bart-large-mnli\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a1d7010-1291-4e31-b525-83327b5e7c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pipe(\n",
    "    [\"The first name of a user\", \"login id\", \"full name of member\"],\n",
    "    candidate_labels=list(faker_functions.keys())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8cd7a50-aa79-48e4-aba9-7e78a4e64074",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_highest_score_functions(result, faker_functions, threshold=0.18):\n",
    "    sequence_to_function = {}\n",
    "    \n",
    "    for item in result:\n",
    "        sequence = item['sequence']\n",
    "        label = item['labels'][0]\n",
    "        score = item['scores'][0]\n",
    "        \n",
    "        if (score >= threshold):\n",
    "            sequence_to_function[sequence] = faker_functions.get(label)\n",
    "        else:\n",
    "            sequence_to_function[sequence] = None\n",
    "    \n",
    "    return sequence_to_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c1989e2-99b8-4de3-b7f9-45c625405eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'The first name of a user': <bound method Provider.first_name of <faker.providers.person.en_US.Provider object at 0x34cecc050>>,\n",
       " 'login id': <bound method Provider.user_name of <faker.providers.internet.en_US.Provider object at 0x34ceb2cd0>>,\n",
       " 'full name of member': <bound method Provider.name of <faker.providers.person.en_US.Provider object at 0x34cecc050>>}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_highest_score_functions(result, faker_functions, threshold=0.18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "799de95c-3d08-4da9-8a84-c3bc81ab5928",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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