File size: 3,764 Bytes
446d7b4 | 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 | {
"cells": [
{
"cell_type": "markdown",
"id": "e12b9784-0a73-447c-bd95-5c4db12213ec",
"metadata": {},
"source": [
"## Load "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "94c34109-799b-4094-934b-85df33a3be99",
"metadata": {},
"outputs": [],
"source": [
"import transformers\n",
"import pandas as pd\n",
"import numpy as np\n",
"import torch\n",
"from transformers import BertTokenizer\n",
"\n",
"# Path of bert model\n",
"path = '/home/colombo_phd/ItalianLaws/Data/BERT-Domains/'\n",
"\n",
"# label df to convert token to string\n",
"label = pd.read_csv(path +'label_tokens.csv', sep = ';')\n",
"\n",
"# Load model\n",
"if torch.cuda.is_available():\n",
" model = torch.load('bert_model')\n",
"else:\n",
" model = torch.load(path +'bert_model', map_location=torch.device('cpu'))"
]
},
{
"cell_type": "markdown",
"id": "f8df905b-9a7b-46ec-8aab-adb15b50aad5",
"metadata": {},
"source": [
"## String to evaluate - title of the law"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5868b342-3161-4862-b269-1d4959359d48",
"metadata": {},
"outputs": [],
"source": [
"title = 'Regolamento per il commercio di prodotti agricoli in europa'"
]
},
{
"cell_type": "markdown",
"id": "3c2d173a-4702-4fb3-93f8-c1ea366bdc41",
"metadata": {},
"source": [
"## Run model"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d57af582-61bc-4be9-b305-63a40ede1311",
"metadata": {},
"outputs": [],
"source": [
"tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n",
"encoded_dict = tokenizer.encode_plus(\n",
" title,\n",
" add_special_tokens = True,\n",
" max_length = 389,\n",
" truncation=True,\n",
" pad_to_max_length = True,\n",
" return_attention_mask = True,\n",
" return_tensors = 'pt',\n",
" )\n",
"test_input_ids = torch.cat([encoded_dict['input_ids']], dim=0)\n",
"test_attention_masks = torch.cat([encoded_dict['attention_mask']], dim=0)\n",
"\n",
"b_input_ids = test_input_ids.to(device)\n",
"b_input_mask = test_attention_masks.to(device)\n",
"with torch.no_grad():\n",
" output= model(b_input_ids,\n",
" token_type_ids=None,\n",
" attention_mask=b_input_mask)\n",
" logits = output.logits\n",
" logits = logits.detach().cpu().numpy()\n",
" pred_flat = np.argmax(logits, axis=1).flatten()"
]
},
{
"cell_type": "markdown",
"id": "bbf3114a-c0ad-411c-b35b-1d7ec922035d",
"metadata": {},
"source": [
"## Derive domain"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "c2e3004b-d735-4462-bbda-6bdb02586102",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'economia'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label[label['label']== pred_flat[0]]['Ministries'].iloc[0]"
]
}
],
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|