Spaces:
Sleeping
Sleeping
File size: 4,733 Bytes
5eaaba5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|