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  {
   "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]"
   ]
  }
 ],
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