experiments/cord-tilt-part-4-1-abstractive-approach-for-t.ipynb

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   "source": [
    "!pip install -qqq wandb"
   ]
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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       "True"
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   "source": [
    "## Logging into wandb\n",
    "\n",
    "import wandb\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "secret_value_0 = user_secrets.get_secret(\"wandb_api\")\n",
    "wandb.login(key=secret_value_0)"
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     "text": [
      "Cloning into 'TiLT-Implementation'...\r\n",
      "remote: Enumerating objects: 54, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (54/54), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (43/43), done.\u001b[K\r\n",
      "remote: Total 54 (delta 19), reused 41 (delta 9), pack-reused 0\u001b[K\r\n",
      "Receiving objects: 100% (54/54), 270.58 KiB | 4.10 MiB/s, done.\r\n",
      "Resolving deltas: 100% (19/19), done.\r\n"
     ]
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   ],
   "source": [
    "!git clone https://github.com/uakarsh/TiLT-Implementation.git"
   ]
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     "text": [
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (from -r ./TiLT-Implementation/requirements.txt (line 1)) (4.27.4)\r\n",
      "Requirement already satisfied: datasets in /opt/conda/lib/python3.7/site-packages (from -r ./TiLT-Implementation/requirements.txt (line 2)) (2.1.0)\r\n",
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      "Requirement already satisfied: pytorch_lightning in /opt/conda/lib/python3.7/site-packages (from -r ./TiLT-Implementation/requirements.txt (line 4)) (1.9.4)\r\n",
      "Collecting seqeval\r\n",
      "  Downloading seqeval-1.2.2.tar.gz (43 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25hCollecting evaluate\r\n",
      "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (4.11.4)\r\n",
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      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (0.13.2)\r\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (4.64.1)\r\n",
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      "Requirement already satisfied: lightning-utilities>=0.6.0.post0 in /opt/conda/lib/python3.7/site-packages (from pytorch_lightning->-r ./TiLT-Implementation/requirements.txt (line 4)) (0.8.0)\r\n",
      "Requirement already satisfied: typing-extensions>=4.0.0 in /opt/conda/lib/python3.7/site-packages (from pytorch_lightning->-r ./TiLT-Implementation/requirements.txt (line 4)) (4.4.0)\r\n",
      "Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from pytorch_lightning->-r ./TiLT-Implementation/requirements.txt (line 4)) (1.13.0)\r\n",
      "Requirement already satisfied: torchmetrics>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from pytorch_lightning->-r ./TiLT-Implementation/requirements.txt (line 4)) (0.11.4)\r\n",
      "Requirement already satisfied: scikit-learn>=0.21.3 in /opt/conda/lib/python3.7/site-packages (from seqeval->-r ./TiLT-Implementation/requirements.txt (line 5)) (1.0.2)\r\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (22.2.0)\r\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (2.1.1)\r\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (4.0.2)\r\n",
      "Requirement already satisfied: asynctest==0.13.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (0.13.0)\r\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (6.0.4)\r\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (1.3.3)\r\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (1.8.2)\r\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (1.3.1)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (3.11.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (3.4)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (1.26.14)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->-r ./TiLT-Implementation/requirements.txt (line 1)) (2022.12.7)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->-r ./TiLT-Implementation/requirements.txt (line 5)) (3.1.0)\r\n",
      "Requirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->-r ./TiLT-Implementation/requirements.txt (line 5)) (1.7.3)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->-r ./TiLT-Implementation/requirements.txt (line 5)) (1.2.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (2023.3)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets->-r ./TiLT-Implementation/requirements.txt (line 2)) (1.16.0)\r\n",
      "Building wheels for collected packages: seqeval\r\n",
      "  Building wheel for seqeval (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16179 sha256=64a306bf144018138366778f6980776510dc4c5903cd6b96142dd6a60fea7159\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/b2/a1/b7/0d3b008d0c77cd57332d724b92cf7650b4185b493dc785f00a\r\n",
      "Successfully built seqeval\r\n",
      "Installing collected packages: seqeval, evaluate\r\n",
      "Successfully installed evaluate-0.4.0 seqeval-1.2.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -r ./TiLT-Implementation/requirements.txt"
   ]
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   "source": [
    "import sys\n",
    "sys.path.append(\"./TiLT-Implementation/src/\")"
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   "source": [
    "import os\n",
    "from transformers import AutoTokenizer, AutoConfig\n",
    "from datasets import load_from_disk\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from pytorch_lightning.loggers import CSVLogger, WandbLogger\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "from dataset import ExtFUNSDDs\n",
    "from torchvision import transforms\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "## Custom imports\n",
    "from visual_backbone import Unet_encoder, RoIPool\n",
    "from t5 import T5ForConditionalGenerationAbstractive, T5Stack\n",
    "from transformers import AutoModel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a4be94",
   "metadata": {
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    "tags": []
   },
   "source": [
    "## 1.1. Preparing the dataset"
   ]
  },
  {
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    "execution": {
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    {
     "data": {
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       "model_id": "900a662584254d5484713f0cec20fc68",
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       "version_minor": 0
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      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
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   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "hf_ds = load_from_disk(\"/kaggle/input/cord-dataset/cord_dataset\")\n",
    "hf_ds = hf_ds.rename_columns({'words':'tokens'})\n",
    "\n",
    "model_name = \"t5-base\"\n",
    "## Visual Embedding extractor's parameters\n",
    "in_channels = 3\n",
    "num_pool_layers = 3\n",
    "channels = 16\n",
    "sampling_ratio = 2\n",
    "spatial_scale = 48 / 384\n",
    "output_size = (3,3)\n",
    "load_weights = True\n",
    "max_epochs = 20\n",
    "\n",
    "## CORD Dataset specific\n",
    "num_classes = 61\n",
    "\n",
    "## Tokenizer's parameter\n",
    "model_max_length = 512\n",
    "\n",
    "t5_config = AutoConfig.from_pretrained(model_name)\n",
    "## Adding new parameters\n",
    "t5_config.update(dict(in_channels = in_channels, num_pool_layers = num_pool_layers,  channels = channels, model_max_length = model_max_length,\n",
    "                      output_size = output_size, spatial_scale = spatial_scale, sampling_ratio = sampling_ratio, use_cache = False, load_weights = load_weights,\n",
    "                      lr =  2e-4, num_classes = num_classes,max_epochs = max_epochs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07a6f9d3",
   "metadata": {
    "execution": {
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   "source": [
    "def get_id2label_and_label2id():\n",
    "    label2id = {'O': 0,\n",
    " 'B-MENU.NM': 1,\n",
    " 'B-MENU.NUM': 2,\n",
    " 'B-MENU.UNITPRICE': 3,\n",
    " 'B-MENU.CNT': 4,\n",
    " 'B-MENU.DISCOUNTPRICE': 5,\n",
    " 'B-MENU.PRICE': 6,\n",
    " 'B-MENU.ITEMSUBTOTAL': 7,\n",
    " 'B-MENU.VATYN': 8,\n",
    " 'B-MENU.ETC': 9,\n",
    " 'B-MENU.SUB_NM': 10,\n",
    " 'B-MENU.SUB_UNITPRICE': 11,\n",
    " 'B-MENU.SUB_CNT': 12,\n",
    " 'B-MENU.SUB_PRICE': 13,\n",
    " 'B-MENU.SUB_ETC': 14,\n",
    " 'B-VOID_MENU.NM': 15,\n",
    " 'B-VOID_MENU.PRICE': 16,\n",
    " 'B-SUB_TOTAL.SUBTOTAL_PRICE': 17,\n",
    " 'B-SUB_TOTAL.DISCOUNT_PRICE': 18,\n",
    " 'B-SUB_TOTAL.SERVICE_PRICE': 19,\n",
    " 'B-SUB_TOTAL.OTHERSVC_PRICE': 20,\n",
    " 'B-SUB_TOTAL.TAX_PRICE': 21,\n",
    " 'B-SUB_TOTAL.ETC': 22,\n",
    " 'B-TOTAL.TOTAL_PRICE': 23,\n",
    " 'B-TOTAL.TOTAL_ETC': 24,\n",
    " 'B-TOTAL.CASHPRICE': 25,\n",
    " 'B-TOTAL.CHANGEPRICE': 26,\n",
    " 'B-TOTAL.CREDITCARDPRICE': 27,\n",
    " 'B-TOTAL.EMONEYPRICE': 28,\n",
    " 'B-TOTAL.MENUTYPE_CNT': 29,\n",
    " 'B-TOTAL.MENUQTY_CNT': 30,\n",
    " 'I-MENU.NM': 31,\n",
    " 'I-MENU.NUM': 32,\n",
    " 'I-MENU.UNITPRICE': 33,\n",
    " 'I-MENU.CNT': 34,\n",
    " 'I-MENU.DISCOUNTPRICE': 35,\n",
    " 'I-MENU.PRICE': 36,\n",
    " 'I-MENU.ITEMSUBTOTAL': 37,\n",
    " 'I-MENU.VATYN': 38,\n",
    " 'I-MENU.ETC': 39,\n",
    " 'I-MENU.SUB_NM': 40,\n",
    " 'I-MENU.SUB_UNITPRICE': 41,\n",
    " 'I-MENU.SUB_CNT': 42,\n",
    " 'I-MENU.SUB_PRICE': 43,\n",
    " 'I-MENU.SUB_ETC': 44,\n",
    " 'I-VOID_MENU.NM': 45,\n",
    " 'I-VOID_MENU.PRICE': 46,\n",
    " 'I-SUB_TOTAL.SUBTOTAL_PRICE': 47,\n",
    " 'I-SUB_TOTAL.DISCOUNT_PRICE': 48,\n",
    " 'I-SUB_TOTAL.SERVICE_PRICE': 49,\n",
    " 'I-SUB_TOTAL.OTHERSVC_PRICE': 50,\n",
    " 'I-SUB_TOTAL.TAX_PRICE': 51,\n",
    " 'I-SUB_TOTAL.ETC': 52,\n",
    " 'I-TOTAL.TOTAL_PRICE': 53,\n",
    " 'I-TOTAL.TOTAL_ETC': 54,\n",
    " 'I-TOTAL.CASHPRICE': 55,\n",
    " 'I-TOTAL.CHANGEPRICE': 56,\n",
    " 'I-TOTAL.CREDITCARDPRICE': 57,\n",
    " 'I-TOTAL.EMONEYPRICE': 58,\n",
    " 'I-TOTAL.MENUTYPE_CNT': 59,\n",
    " 'I-TOTAL.MENUQTY_CNT': 60}\n",
    "    id2label = {0: 'O',\n",
    " 1: 'B-MENU.NM',\n",
    " 2: 'B-MENU.NUM',\n",
    " 3: 'B-MENU.UNITPRICE',\n",
    " 4: 'B-MENU.CNT',\n",
    " 5: 'B-MENU.DISCOUNTPRICE',\n",
    " 6: 'B-MENU.PRICE',\n",
    " 7: 'B-MENU.ITEMSUBTOTAL',\n",
    " 8: 'B-MENU.VATYN',\n",
    " 9: 'B-MENU.ETC',\n",
    " 10: 'B-MENU.SUB_NM',\n",
    " 11: 'B-MENU.SUB_UNITPRICE',\n",
    " 12: 'B-MENU.SUB_CNT',\n",
    " 13: 'B-MENU.SUB_PRICE',\n",
    " 14: 'B-MENU.SUB_ETC',\n",
    " 15: 'B-VOID_MENU.NM',\n",
    " 16: 'B-VOID_MENU.PRICE',\n",
    " 17: 'B-SUB_TOTAL.SUBTOTAL_PRICE',\n",
    " 18: 'B-SUB_TOTAL.DISCOUNT_PRICE',\n",
    " 19: 'B-SUB_TOTAL.SERVICE_PRICE',\n",
    " 20: 'B-SUB_TOTAL.OTHERSVC_PRICE',\n",
    " 21: 'B-SUB_TOTAL.TAX_PRICE',\n",
    " 22: 'B-SUB_TOTAL.ETC',\n",
    " 23: 'B-TOTAL.TOTAL_PRICE',\n",
    " 24: 'B-TOTAL.TOTAL_ETC',\n",
    " 25: 'B-TOTAL.CASHPRICE',\n",
    " 26: 'B-TOTAL.CHANGEPRICE',\n",
    " 27: 'B-TOTAL.CREDITCARDPRICE',\n",
    " 28: 'B-TOTAL.EMONEYPRICE',\n",
    " 29: 'B-TOTAL.MENUTYPE_CNT',\n",
    " 30: 'B-TOTAL.MENUQTY_CNT',\n",
    " 31: 'I-MENU.NM',\n",
    " 32: 'I-MENU.NUM',\n",
    " 33: 'I-MENU.UNITPRICE',\n",
    " 34: 'I-MENU.CNT',\n",
    " 35: 'I-MENU.DISCOUNTPRICE',\n",
    " 36: 'I-MENU.PRICE',\n",
    " 37: 'I-MENU.ITEMSUBTOTAL',\n",
    " 38: 'I-MENU.VATYN',\n",
    " 39: 'I-MENU.ETC',\n",
    " 40: 'I-MENU.SUB_NM',\n",
    " 41: 'I-MENU.SUB_UNITPRICE',\n",
    " 42: 'I-MENU.SUB_CNT',\n",
    " 43: 'I-MENU.SUB_PRICE',\n",
    " 44: 'I-MENU.SUB_ETC',\n",
    " 45: 'I-VOID_MENU.NM',\n",
    " 46: 'I-VOID_MENU.PRICE',\n",
    " 47: 'I-SUB_TOTAL.SUBTOTAL_PRICE',\n",
    " 48: 'I-SUB_TOTAL.DISCOUNT_PRICE',\n",
    " 49: 'I-SUB_TOTAL.SERVICE_PRICE',\n",
    " 50: 'I-SUB_TOTAL.OTHERSVC_PRICE',\n",
    " 51: 'I-SUB_TOTAL.TAX_PRICE',\n",
    " 52: 'I-SUB_TOTAL.ETC',\n",
    " 53: 'I-TOTAL.TOTAL_PRICE',\n",
    " 54: 'I-TOTAL.TOTAL_ETC',\n",
    " 55: 'I-TOTAL.CASHPRICE',\n",
    " 56: 'I-TOTAL.CHANGEPRICE',\n",
    " 57: 'I-TOTAL.CREDITCARDPRICE',\n",
    " 58: 'I-TOTAL.EMONEYPRICE',\n",
    " 59: 'I-TOTAL.MENUTYPE_CNT',\n",
    " 60: 'I-TOTAL.MENUQTY_CNT'}\n",
    "    return id2label, label2id\n",
    "\n",
    "def convert_id_to_label(list_of_label):\n",
    "  return [id2label[x] for x in list_of_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7931f82d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:32.640937Z",
     "iopub.status.busy": "2023-04-10T15:56:32.640636Z",
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     "shell.execute_reply": "2023-04-10T15:56:32.643563Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train_new_tags = list(map(lambda x : convert_id_to_label(x), hf_ds['train']['ner_tags']))\n",
    "# test_new_tags = list(map(lambda x : convert_id_to_label(x), hf_ds['test']['ner_tags']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d885638b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:32.666929Z",
     "iopub.status.busy": "2023-04-10T15:56:32.666632Z",
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     "shell.execute_reply": "2023-04-10T15:56:32.669772Z"
    },
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     "duration": 0.017077,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# hf_ds['train'] = hf_ds['train'].remove_columns(\"ner_tags\").add_column(\"ner_tags\", train_new_tags)\n",
    "# hf_ds['test'] = hf_ds['test'].remove_columns(\"ner_tags\").add_column(\"ner_tags\", test_new_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86f9495",
   "metadata": {
    "id": "8bHthQUuEeVD",
    "papermill": {
     "duration": 0.009007,
     "end_time": "2023-04-10T15:56:32.691763",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.682756",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1.2 Writing the `collate_fn` for custom handling of the dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c536f483",
   "metadata": {
    "execution": {
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    },
    "id": "fchZF6hxESY6",
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     "duration": 0.018057,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CollateFn(object):\n",
    "  def __init__(self, tokenizer):\n",
    "    self.tokenizer = tokenizer\n",
    "\n",
    "  def __call__(self, list_of_ds):\n",
    "    simple_keys = [\"input_ids\", \"attention_mask\", \"bboxes\", \"pixel_values\", \"labels\"]\n",
    "    actual_batch = {}\n",
    "    for key in simple_keys:\n",
    "      actual_batch[key] = torch.stack([x[key] for x in list_of_ds])\n",
    "    \n",
    "    # actual_batch['labels'] = self.tokenizer.batch_encode_plus([x['labels'] for x in list_of_ds], return_tensors = 'pt', is_split_into_words = True,\n",
    "    #                                                           padding='max_length', truncation = True)['input_ids']\n",
    "    return actual_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "141dfa50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:32.740929Z",
     "iopub.status.busy": "2023-04-10T15:56:32.739289Z",
     "iopub.status.idle": "2023-04-10T15:56:32.744770Z",
     "shell.execute_reply": "2023-04-10T15:56:32.743841Z"
    },
    "id": "m26xnsfXEVFJ",
    "papermill": {
     "duration": 0.018483,
     "end_time": "2023-04-10T15:56:32.746969",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.728486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sample_batch_encoding = collate_fn([train_ds[0], train_ds[1]])\n",
    "# for key in sample_batch_encoding:\n",
    "#   sample_batch_encoding[key] = sample_batch_encoding[key].to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378f26a7",
   "metadata": {
    "id": "u5qhZ38OGQDJ",
    "papermill": {
     "duration": 0.009093,
     "end_time": "2023-04-10T15:56:32.765410",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.756317",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.1 Preparing the visual model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2510e9fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:32.785154Z",
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    },
    "id": "_cVQaYBUEZbv",
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class VisualEmbedding(nn.Module):\n",
    "  def __init__(self, config):\n",
    "    super().__init__()\n",
    "    self.unet_encoder = Unet_encoder(in_channels = config.in_channels, channels = config.channels, num_pool_layers = config.num_pool_layers)\n",
    "    self.roi_pool = RoIPool(output_size = config.output_size, spatial_scale = config.spatial_scale)\n",
    "    self.proj = nn.Linear(in_features = 128 * 3 * 3, out_features = config.d_model)\n",
    "    self.config = config\n",
    "\n",
    "  def forward(self, pixel_values, bboxes):\n",
    "    image_embedding = self.unet_encoder(pixel_values)\n",
    "    feature_maps_bboxes = self.roi_pool(image_embedding, bboxes).flatten(2)\n",
    "    projection = self.proj(feature_maps_bboxes)\n",
    "    return projection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd315b57",
   "metadata": {
    "id": "jdJzjR8elcoj",
    "papermill": {
     "duration": 0.009325,
     "end_time": "2023-04-10T15:56:32.813210",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.803885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.2 Preparing the semantic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6ab659c",
   "metadata": {
    "execution": {
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TiLTTransformer(nn.Module):\n",
    "  def __init__(self, config):\n",
    "    super().__init__()\n",
    "    self.config = config\n",
    "    self.visual_embedding_extractor = VisualEmbedding(config)\n",
    "    self.t5_model = T5ForConditionalGenerationAbstractive(config)\n",
    "    \n",
    "\n",
    "  def generate(self, batch):\n",
    "    total_embedding = self.common_step(batch)\n",
    "    return self.t5_model.generate(input_embeds = total_embedding)\n",
    "\n",
    "  def common_step(self, batch):\n",
    "    ## Visual embedding\n",
    "    visual_embedding = self.visual_embedding_extractor(pixel_values = batch['pixel_values'], bboxes = batch['bboxes'])\n",
    "\n",
    "    ## Semantic embedding from t5_model's embedding layer\n",
    "    semantic_embedding = self.t5_model.shared(batch['input_ids'])\n",
    "\n",
    "    ## Net embedding is addition of both the embeddings\n",
    "    total_embedding = visual_embedding + semantic_embedding\n",
    "\n",
    "    return total_embedding\n",
    "\n",
    "  def forward(self, batch):\n",
    "\n",
    "    total_embedding = self.common_step(batch)\n",
    "\n",
    "    ## This is then fed to t5_model\n",
    "    final_output = self.t5_model(attention_mask = batch['attention_mask'], inputs_embeds = total_embedding,\n",
    "                            labels = batch['labels'])\n",
    "    \n",
    "    return final_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7888d3e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:32.863934Z",
     "iopub.status.busy": "2023-04-10T15:56:32.862953Z",
     "iopub.status.idle": "2023-04-10T15:56:32.867516Z",
     "shell.execute_reply": "2023-04-10T15:56:32.866525Z"
    },
    "id": "sZXlIJcmypem",
    "papermill": {
     "duration": 0.01688,
     "end_time": "2023-04-10T15:56:32.869580",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.852700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# tilt_model = TiLTTransformer(t5_config).to(device)\n",
    "# output = tilt_model(sample_batch_encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d29286",
   "metadata": {
    "id": "y0KzA2Icys9y",
    "papermill": {
     "duration": 0.009078,
     "end_time": "2023-04-10T15:56:32.888041",
     "exception": false,
     "start_time": "2023-04-10T15:56:32.878963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.1 Preparing the metrics to evaluate the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da9a6fbf",
   "metadata": {
    "execution": {
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    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "def get_labels(predictions, references):\n",
    "\n",
    "    # Transform predictions and references tensors to numpy arrays\n",
    "    if predictions.device.type == \"cpu\":\n",
    "        y_pred = predictions.detach().clone().numpy()\n",
    "        y_true = references.detach().clone().numpy()\n",
    "\n",
    "    else:\n",
    "        y_pred = predictions.detach().cpu().clone().numpy()\n",
    "        y_true = references.detach().cpu().clone().numpy()\n",
    "\n",
    "    # Remove ignored index (special tokens)\n",
    "    true_predictions = [\n",
    "        [id2label[p] for (p, l) in zip(pred, gold_label) if l != -100]\n",
    "        for pred, gold_label in zip(y_pred, y_true)\n",
    "    ]\n",
    "    true_labels = [\n",
    "        [id2label[l] for (p, l) in zip(pred, gold_label) if l != -100]\n",
    "        for pred, gold_label in zip(y_pred, y_true)\n",
    "    ]\n",
    "    return true_predictions, true_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5025d8c1",
   "metadata": {
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     "shell.execute_reply": "2023-04-10T15:56:40.411397Z"
    },
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     "duration": 0.017911,
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     "exception": false,
     "start_time": "2023-04-10T15:56:40.397154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# labels = sample_batch_encoding['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cec90f50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:40.435118Z",
     "iopub.status.busy": "2023-04-10T15:56:40.434771Z",
     "iopub.status.idle": "2023-04-10T15:56:40.439059Z",
     "shell.execute_reply": "2023-04-10T15:56:40.438038Z"
    },
    "id": "L4bldNSl0FUG",
    "papermill": {
     "duration": 0.016883,
     "end_time": "2023-04-10T15:56:40.441361",
     "exception": false,
     "start_time": "2023-04-10T15:56:40.424478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# true_predictions, true_labels = get_labels(predictions = output.logits.argmax(axis = -1), references = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8146c1d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:40.461206Z",
     "iopub.status.busy": "2023-04-10T15:56:40.460913Z",
     "iopub.status.idle": "2023-04-10T15:56:40.465146Z",
     "shell.execute_reply": "2023-04-10T15:56:40.464082Z"
    },
    "id": "c5tLEdWw1ywr",
    "papermill": {
     "duration": 0.016589,
     "end_time": "2023-04-10T15:56:40.467211",
     "exception": false,
     "start_time": "2023-04-10T15:56:40.450622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# eval_metric = evaluate.load(\"seqeval\")\n",
    "# metric = eval_metric.compute(predictions = true_predictions, references = true_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3812452",
   "metadata": {
    "id": "Q0X5UKG_2idi",
    "papermill": {
     "duration": 0.009067,
     "end_time": "2023-04-10T15:56:40.485566",
     "exception": false,
     "start_time": "2023-04-10T15:56:40.476499",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Part: 4 Writing the `pytorch_lightning` code for training on FUNSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "404b4988",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2023-04-10T15:56:40.494941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9219d11f5a644fb8882db86fc2ddf4b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0c3e9d247464502a155937001f00659",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "id2label, label2id = get_id2label_and_label2id()\n",
    "transform = transforms.Compose([transforms.ToTensor(), \n",
    "                                transforms.Lambda(lambda x : 2 * x - 1)])\n",
    "\n",
    "## Tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast = True, model_max_length = model_max_length)\n",
    "\n",
    "train_ds = ExtFUNSDDs(hf_ds['train'],tokenizer = tokenizer, transform = transform)\n",
    "val_ds = ExtFUNSDDs(hf_ds['test'],tokenizer = tokenizer, transform = transform)\n",
    "\n",
    "collate_fn = CollateFn(tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd4fe0f0",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class DataModule(pl.LightningDataModule):\n",
    "\n",
    "  def __init__(self, train_dataset, eval_dataset, batch_size: int = 2):\n",
    "    super(DataModule, self).__init__()\n",
    "    self.batch_size = batch_size\n",
    "    self.train_dataset = train_dataset\n",
    "    self.eval_dataset = eval_dataset\n",
    "\n",
    "  def train_dataloader(self):\n",
    "    return DataLoader(self.train_dataset, batch_size=self.batch_size,\n",
    "                      shuffle=True, collate_fn = collate_fn)\n",
    "\n",
    "  def val_dataloader(self):\n",
    "    return DataLoader(self.eval_dataset, batch_size=self.batch_size,\n",
    "                      shuffle=False, collate_fn = collate_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "83744ba4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:42.381195Z",
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    },
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     "duration": 0.024774,
     "end_time": "2023-04-10T15:56:42.394993",
     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "class TiltModel(pl.LightningModule):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        self.tilt_model = TiLTTransformer(config)\n",
    "        self.eval_metric = evaluate.load(\"seqeval\")\n",
    "\n",
    "    def forward(self, batch):\n",
    "        return self.tilt_model(batch)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        output = self(batch)\n",
    "        loss = output.loss\n",
    "\n",
    "        predictions = output.logits.argmax(dim=-1)        \n",
    "        true_predictions, true_labels = get_labels(predictions = predictions, references =  batch['labels'])\n",
    "\n",
    "        results = self.eval_metric.compute(predictions=true_predictions, references=true_labels)\n",
    "        self.log(\"train_loss\", output.loss.item(),\n",
    "                 prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"train_overall_fl\",\n",
    "                 results[\"overall_f1\"], prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"train_overall_recall\",\n",
    "                 results[\"overall_recall\"], prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"train_overall_precision\",\n",
    "                 results[\"overall_precision\"], prog_bar=True, on_epoch=True, logger=True)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        output = self(batch)\n",
    "        loss = output.loss\n",
    "\n",
    "        predictions = output.logits.argmax(dim=-1)        \n",
    "        true_predictions, true_labels = get_labels(predictions = predictions, references =  batch['labels'])\n",
    "        results = self.eval_metric.compute(predictions=true_predictions, references=true_labels)\n",
    "\n",
    "        self.log(\"val_loss\", output.loss.item(),\n",
    "                 prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"val_overall_fl\", results[\"overall_f1\"],\n",
    "                 prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"val_overall_recall\",\n",
    "                 results[\"overall_recall\"], prog_bar=True, on_epoch=True, logger=True)\n",
    "        self.log(\"val_overall_precision\",\n",
    "                 results[\"overall_precision\"], prog_bar=True, on_epoch=True, logger=True)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.AdamW(self.parameters(), lr=self.config.lr)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2bc365fd",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-04-10T15:56:42.423609Z"
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    "id": "T5S_yW3Hmvjf",
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     "duration": 0.022114,
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     "status": "completed"
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   "source": [
    "def perform_evaluation(path: str=None, pl_model=None, pl_dl=None):\n",
    "    print(\"Evaluating the model\")\n",
    "    if path is not None:\n",
    "      pl_model = pl_model.load_from_checkpoint(path, config = t5_config)\n",
    "\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    eval_metric = evaluate.load(\"seqeval\")\n",
    "    pl_model = pl_model.to(device)\n",
    "    pl_model.eval();\n",
    "\n",
    "    for idx, batch in enumerate(tqdm(pl_dl.val_dataloader())):\n",
    "      # move batch to device\n",
    "      batch = {k: v.to(device) for k, v in batch.items()}\n",
    "      \n",
    "      with torch.no_grad():\n",
    "        outputs = pl_model(batch)\n",
    "\n",
    "        predictions = outputs.logits.argmax(-1)\n",
    "        true_predictions, true_labels = get_labels(predictions, batch[\"labels\"])\n",
    "        eval_metric.add_batch(references=true_labels,predictions=true_predictions)\n",
    "\n",
    "    results = eval_metric.compute()\n",
    "\n",
    "    metrics = {}\n",
    "    for key in ['overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']:\n",
    "      print_statement = '{0: <30}'.format(str(key) + \" has value:\")\n",
    "      print(print_statement, results[key])\n",
    "      metrics[key] = results[key]\n",
    "\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "312f59d6",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "checkpoint_callback = ModelCheckpoint(dirpath=\"./tilt/models\",monitor=\"val_overall_fl\", mode=\"max\", filename='tilt_best_ckpt', save_top_k = 1)\n",
    "logger = CSVLogger(\"./tilt/logs\", name=\"funsd_dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b5c986c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T15:56:42.485698Z",
     "iopub.status.busy": "2023-04-10T15:56:42.485373Z",
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     "shell.execute_reply": "2023-04-10T15:57:13.645918Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33miakarshu\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.14.2 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
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      ]
     },
     "metadata": {},
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       "Tracking run with wandb version 0.14.0"
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     "data": {
      "text/html": [
       "Run data is saved locally in <code>/kaggle/working/wandb/run-20230410_155642-3ztmfs3i</code>"
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      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/iakarshu/TiLT%20on%20CORD/runs/3ztmfs3i' target=\"_blank\">expert-dream-2</a></strong> to <a href='https://wandb.ai/iakarshu/TiLT%20on%20CORD' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       " View project at <a href='https://wandb.ai/iakarshu/TiLT%20on%20CORD' target=\"_blank\">https://wandb.ai/iakarshu/TiLT%20on%20CORD</a>"
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       " View run at <a href='https://wandb.ai/iakarshu/TiLT%20on%20CORD/runs/3ztmfs3i' target=\"_blank\">https://wandb.ai/iakarshu/TiLT%20on%20CORD/runs/3ztmfs3i</a>"
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/loggers/wandb.py:396: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n",
      "  \"There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse\"\n"
     ]
    }
   ],
   "source": [
    "wandb.init(config=t5_config, project=\"TiLT on CORD\")\n",
    "wandb_logger = WandbLogger(project=\"TiLT on CORD\", log_model = False, entity=\"iakarshu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3b13480a",
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     "status": "completed"
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   "outputs": [],
   "source": [
    "trainer = pl.Trainer(default_root_dir=\"./tilt/logs\", devices=\"auto\",\n",
    "                     accelerator=\"auto\",max_epochs= t5_config.max_epochs, \n",
    "                     logger=wandb_logger,callbacks=[checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3e93acf",
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   "outputs": [
    {
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       "model_id": "97b88718374a4106b75b943a7a64dac6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/892M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights loaded successfully!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9f3b16873a44bedb23e84e3a092648c",
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      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "pl_model = TiltModel(t5_config)\n",
    "pl_dl = DataModule(train_ds, val_ds, batch_size = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df913393",
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    "ckpt_folder = \"./tilt/models\"\n",
    "if os.path.exists(ckpt_folder):\n",
    "  ckpt_path = os.path.join(ckpt_folder, os.listdir(ckpt_folder)[0])\n",
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      "Evaluating the model\n",
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      "overall_precision has value:   0.6480859637340497\n",
      "overall_recall has value:      0.6264199935086011\n",
      "overall_f1 has value:          0.637068823238158\n",
      "overall_accuracy has value:    0.8052400146573837\n"
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