st-cosine-similarity-loss-finetune.ipynb

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     "text": [
      "\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\r\n",
      "Requirement already satisfied: numba in /opt/conda/lib/python3.7/site-packages (0.54.1)\r\n",
      "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /opt/conda/lib/python3.7/site-packages (from numba) (0.37.0)\r\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from numba) (59.5.0)\r\n",
      "Requirement already satisfied: numpy<1.21,>=1.17 in /opt/conda/lib/python3.7/site-packages (from numba) (1.20.3)\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\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q transformers\n",
    "!pip install numba "
   ]
  },
  {
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     "text": [
      "Collecting sentence-transformers\r\n",
      "  Downloading sentence-transformers-2.2.0.tar.gz (79 kB)\r\n",
      "     |████████████████████████████████| 79 kB 851 kB/s            \r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (4.15.0)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (4.62.3)\r\n",
      "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.9.1)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.10.1)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.20.3)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.0.1)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.7.3)\r\n",
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (3.2.4)\r\n",
      "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.1.96)\r\n",
      "Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.2.1)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.6.0->sentence-transformers) (4.1.1)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (6.0)\r\n",
      "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.0.47)\r\n",
      "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.10.3)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\r\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (4.11.2)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.4.2)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.26.0)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.11.10)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from nltk->sentence-transformers) (1.16.0)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sentence-transformers) (1.1.0)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sentence-transformers) (3.0.0)\r\n",
      "Requirement already satisfied: pillow>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->sentence-transformers) (8.2.0)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.6)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.26.7)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.1)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\r\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.0.9)\r\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (8.0.3)\r\n",
      "Building wheels for collected packages: sentence-transformers\r\n",
      "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.0-py3-none-any.whl size=120748 sha256=14b5ac3e20fac6b01848f8e3e95425df12b614f8f4244acfb7a88d3664cb321d\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/83/c0/df/b6873ab7aac3f2465aa9144b6b4c41c4391cfecc027c8b07e7\r\n",
      "Successfully built sentence-transformers\r\n",
      "Installing collected packages: sentence-transformers\r\n",
      "Successfully installed sentence-transformers-2.2.0\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\r\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence-transformers"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "import json, time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07fc974d",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'NLP_Shared_Task_2022'...\r\n",
      "remote: Enumerating objects: 45996, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (8478/8478), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (8301/8301), done.\u001b[K\r\n",
      "remote: Total 45996 (delta 182), reused 8471 (delta 177), pack-reused 37518\u001b[K\r\n",
      "Receiving objects: 100% (45996/45996), 658.51 MiB | 18.85 MiB/s, done.\r\n",
      "Resolving deltas: 100% (1330/1330), done.\r\n",
      "Updating files: 100% (48299/48299), done.\r\n",
      "/kaggle/working/NLP_Shared_Task_2022\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/AdarshKumar712/NLP_Shared_Task_2022\n",
    "%cd NLP_Shared_Task_2022/"
   ]
  },
  {
   "cell_type": "code",
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   "id": "cdc9fb21",
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   "outputs": [],
   "source": [
    "#Loading Train and Test Dataset Files\n",
    "train_data = pd.read_csv(\"./train.csv\")\n",
    "val_data = pd.read_csv(\"./val.csv\")\n",
    "test_data = pd.read_csv(\"eval_with_result.csv\")"
   ]
  },
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       "1  https://web.archive.org/web/www.thestar.com/ne...        4.0  4.000000   \n",
       "2  https://web.archive.org/web/www.timesofisrael....        1.0  2.000000   \n",
       "3  https://web.archive.org/web/gadgets.ndtv.com/i...        1.0  2.333333   \n",
       "4  https://web.archive.org/web/www.channelnewsasi...        1.0  1.250000   \n",
       "\n",
       "       Time  Narrative   Overall     Style      Tone  \n",
       "0  1.000000   4.000000  4.000000  1.666667  2.000000  \n",
       "1  1.000000   4.000000  3.666667  1.666667  1.333333  \n",
       "2  1.000000   2.333333  2.333333  1.000000  1.333333  \n",
       "3  2.666667   1.666667  2.000000  1.666667  1.666667  \n",
       "4  1.000000   1.250000  1.250000  1.000000  1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17f89e3",
   "metadata": {
    "papermill": {
     "duration": 0.138008,
     "end_time": "2022-04-01T13:49:46.198909",
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     "start_time": "2022-04-01T13:49:46.060901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9cd4ac3",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-04-01T13:49:48.299061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def text_preprocessing(text):\n",
    "    text = re.sub(r'(@.*?)[\\s]', ' ', text)\n",
    "    text = re.sub(r'&amp;', '&', text)\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b7a183b",
   "metadata": {
    "execution": {
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    },
    "papermill": {
     "duration": 7.445242,
     "end_time": "2022-04-01T13:49:55.985298",
     "exception": false,
     "start_time": "2022-04-01T13:49:48.540056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "version_minor": 0
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       "Downloading:   0%|          | 0.00/402 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/723 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/8.66M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
    "#tokenizer_name = \"mbert\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')\n",
    "tokenizer_name = \"SBERT\"\n",
    "\n",
    "#tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')\n",
    "#tokenizer_name = \"xlm\"\n",
    "\n",
    "def load_data(input_data, input_dir, max_len=256):\n",
    "  files_present,files_absent = 0,0\n",
    "  inputs1, inputs2 = list(), list()\n",
    "  masks1, masks2 = list(), list()\n",
    "  targets = list()\n",
    "  for i in range(len(input_data)):\n",
    "    row = input_data.iloc[i]\n",
    "    pair = row['pair_id']\n",
    "    \n",
    "    y_target = row['Overall']\n",
    "    # y_scaler = StandardScaler()\n",
    "    # y_target = y_scaler.transform(y_target.reshape(-1, 1))\n",
    "\n",
    "    f1, f2 = pair.split(\"_\")\n",
    "    folder1 = f1[-2:]\n",
    "    folder2 = f2[-2:]\n",
    "    try:\n",
    "      with open(input_dir+\"/\"+folder1+\"/\"+f1+\".json\",\"r\") as f:\n",
    "        d1 = json.load(f)\n",
    "      with open(input_dir+\"/\"+folder2+\"/\"+f2+\".json\",\"r\") as f:\n",
    "        d2 = json.load(f)\n",
    "    except Exception as E:\n",
    "      files_absent = files_absent + 1\n",
    "      continue\n",
    "    s1 = text_preprocessing(d1['title']+\"[SEP]\"+d1['text'])\n",
    "    s2 = text_preprocessing(d2['title']+\"[SEP]\"+d2['text'])\n",
    "    \n",
    "    files_present = files_present+1\n",
    "    encoded_input1 = tokenizer.encode_plus(s1, return_tensors='pt',return_attention_mask=True, pad_to_max_length=True,max_length=max_len)\n",
    "    encoded_input2 = tokenizer.encode_plus(s2, return_tensors='pt',return_attention_mask=True, pad_to_max_length=True,max_length=max_len)\n",
    "\n",
    "    input_ids1 = encoded_input1['input_ids']\n",
    "    input_ids2 = encoded_input2['input_ids']\n",
    "    attention_masks1 = encoded_input1['attention_mask']\n",
    "    attention_masks2 = encoded_input2['attention_mask']\n",
    "    \n",
    "    inputs1.append(input_ids1)\n",
    "    inputs2.append(input_ids2)\n",
    "    masks1.append(attention_masks1)\n",
    "    masks2.append(attention_masks2)\n",
    "    targets.append(y_target)\n",
    "\n",
    "  print(f\"Files present: {files_present} and Files absent: {files_absent}. % missing = {(files_absent*100)/(files_present+files_absent)}%\")\n",
    "  return inputs1, inputs2, masks1, masks2, targets  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe41d9cd",
   "metadata": {
    "execution": {
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    },
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     "duration": 0.127448,
     "end_time": "2022-04-01T13:49:56.230179",
     "exception": false,
     "start_time": "2022-04-01T13:49:56.102731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "def create_dataloaders(inputs1, inputs2, masks1, masks2, targets, batch_size):\n",
    "    input_tensor1 = torch.tensor([t.numpy() for t in inputs1])\n",
    "    input_tensor2 = torch.tensor([t.numpy() for t in inputs2])\n",
    "    mask_tensor1 = torch.tensor([t.numpy() for t in masks1])\n",
    "    mask_tensor2 = torch.tensor([t.numpy() for t in masks2])\n",
    "    targets_tensor = torch.tensor(targets)\n",
    "    dataset = TensorDataset(input_tensor1, input_tensor2, mask_tensor1, mask_tensor2, targets_tensor)\n",
    "    dataloader = DataLoader(dataset, batch_size=batch_size,shuffle=True)\n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b31f68f1",
   "metadata": {
    "execution": {
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    },
    "papermill": {
     "duration": 77.445829,
     "end_time": "2022-04-01T13:51:13.792196",
     "exception": false,
     "start_time": "2022-04-01T13:49:56.346367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2232: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files present: 4096 and Files absent: 22. % missing = 0.5342399222923749%\n",
      "Files present: 844 and Files absent: 2. % missing = 0.2364066193853428%\n",
      "Files present: 4659 and Files absent: 243. % missing = 4.957160342717258%\n"
     ]
    }
   ],
   "source": [
    "batch_size = 24\n",
    "\n",
    "#Train Set\n",
    "train_inputs1, train_inputs2, train_masks1, train_masks2, train_targets = load_data(train_data, \"articles\", max_len=128)\n",
    "train_dataloader = create_dataloaders(train_inputs1, train_inputs2, train_masks1, train_masks2, train_targets, batch_size)\n",
    "\n",
    "#Train Set\n",
    "val_inputs1, val_inputs2, val_masks1, val_masks2, val_targets = load_data(val_data, \"articles\", max_len=128)\n",
    "val_dataloader = create_dataloaders(val_inputs1, val_inputs2, val_masks1, val_masks2, val_targets, batch_size)\n",
    "\n",
    "#Test Set\n",
    "test_inputs1, test_inputs2, test_masks1, test_masks2, test_targets = load_data(test_data, \"eval_data\", max_len=128)\n",
    "test_dataloader = create_dataloaders(test_inputs1, test_inputs2, test_masks1, test_masks2, test_targets, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79a58279",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T13:51:14.092280Z",
     "iopub.status.busy": "2022-04-01T13:51:14.091683Z",
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     "shell.execute_reply": "2022-04-01T13:51:14.096483Z",
     "shell.execute_reply.started": "2022-04-01T12:36:41.657471Z"
    },
    "papermill": {
     "duration": 0.186739,
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     "exception": false,
     "start_time": "2022-04-01T13:51:13.910536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "if torch.cuda.is_available():       \n",
    "  device = torch.device(\"cuda\")\n",
    "  print(\"Using GPU.\")\n",
    "else:\n",
    "  print(\"No GPU available, using the CPU instead.\")\n",
    "  device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9234f91",
   "metadata": {
    "papermill": {
     "duration": 0.126542,
     "end_time": "2022-04-01T13:51:14.348557",
     "exception": false,
     "start_time": "2022-04-01T13:51:14.222015",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Modeling Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4dfcf921",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-01T13:51:14.605038Z",
     "shell.execute_reply.started": "2022-04-01T12:36:41.665394Z"
    },
    "papermill": {
     "duration": 0.132837,
     "end_time": "2022-04-01T13:51:14.605624",
     "exception": false,
     "start_time": "2022-04-01T13:51:14.472787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Model Architecure\n",
    "\n",
    "class STRegressor(nn.Module):\n",
    "    \n",
    "    def __init__(self, drop_rate=0.2):        \n",
    "        super(STRegressor, self).__init__()\n",
    "        D_in, D_out = 768*2, 1\n",
    "        self.ST = AutoModel.from_pretrained(\"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\")   \n",
    "        self.regressor = nn.Sequential(\n",
    "            nn.Dropout(drop_rate),\n",
    "            nn.Linear(D_in, D_out)) \n",
    "    def mean_pool(self, token_embeds, attention_mask):\n",
    "        # reshape attention_mask to cover 768-dimension embeddings\n",
    "        in_mask = attention_mask.unsqueeze(-1).expand(\n",
    "            token_embeds.size()\n",
    "        ).float()\n",
    "        # perform mean-pooling but exclude padding tokens (specified by in_mask)\n",
    "        pool = torch.sum(token_embeds * in_mask, 1) / torch.clamp(\n",
    "            in_mask.sum(1), min=1e-9\n",
    "        )\n",
    "        return pool\n",
    "    def forward(self, input_ids1, input_ids2, attention_masks1, attention_masks2):\n",
    "        outputs1 = self.mean_pool(self.ST(input_ids1, attention_masks1)[0], attention_masks1)\n",
    "        outputs2 = self.mean_pool(self.ST(input_ids2, attention_masks2)[0], attention_masks2)\n",
    "        #outputs = torch.dot(outputs1,outputs2)\n",
    "        #outputs = outputs1[1]+outputs2[1]\n",
    "        #outputs = torch.cat((outputs1, outputs2), 1)\n",
    "        # last_hidden_state_cls = outputs[0][:, 0, :]\n",
    "        #last_hidden_state_cls = outputs\n",
    "        #final_outputs = self.regressor(last_hidden_state_cls)\n",
    "        return outputs1, outputs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13293a3f",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-04-01T13:51:14.727758",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf681218828e463a9fd2bdab55d09806",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = STRegressor(drop_rate=0.2).to(device)\n",
    "# model = XLMRegressor(drop_rate=0.2).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8118a9e6",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-01T13:51:50.626677Z",
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    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def loss_fn(labels,  o1=None, o2=None):\n",
    "    labels = 1-(labels-1)/3\n",
    "    cosine_similarity = nn.CosineSimilarity()(o1, o2)\n",
    "    cs_loss = nn.MSELoss()(cosine_similarity, labels) \n",
    "    loss = cs_loss\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8274a0bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T13:51:51.058919Z",
     "iopub.status.busy": "2022-04-01T13:51:51.058041Z",
     "iopub.status.idle": "2022-04-01T13:51:51.060280Z",
     "shell.execute_reply": "2022-04-01T13:51:51.059652Z",
     "shell.execute_reply.started": "2022-04-01T12:36:46.528703Z"
    },
    "papermill": {
     "duration": 0.203999,
     "end_time": "2022-04-01T13:51:51.060452",
     "exception": false,
     "start_time": "2022-04-01T13:51:50.856453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad35007b",
   "metadata": {
    "execution": {
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    },
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     "exception": false,
     "start_time": "2022-04-01T13:51:51.251850",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def renormalise_similarity_score(scores):\n",
    "    # reverse the normalisation I did for SBERT finetuning\n",
    "    scores = np.array(scores)\n",
    "    renormalised_scores = (3 - (scores * 3)) + 1\n",
    "    return renormalised_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "409a3c7f",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "def pearson(X,Y):\n",
    "    #print(X,Y)\n",
    "    xm = torch.mean(X.float())\n",
    "    ym = torch.mean(Y.float())\n",
    "    #print(xm, ym)\n",
    "    num = 0.0\n",
    "    deno1 = 0.0\n",
    "    deno2 = 0.0\n",
    "    deno = 0.0\n",
    "    n = X.shape[0]\n",
    "    for i in range(n):\n",
    "        num += (X[i]-xm)*(Y[i]-ym)\n",
    "        deno1 += (X[i]-xm)*(X[i]-xm)\n",
    "        deno2 += (Y[i]-ym)*(Y[i]-ym)\n",
    "    deno = deno1*deno2\n",
    "    deno = torch.sqrt(deno)\n",
    "    return (num/deno).item()\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "def evaluate(model, loss_function, test_dataloader, device):\n",
    "    model.eval()\n",
    "    test_loss, test_pearson, test_mse = [], [], []\n",
    "    predicted=[]\n",
    "    targets=[]\n",
    "    cosine_sims=[]\n",
    "    for batch in tqdm(test_dataloader):\n",
    "        batch_inputs1, batch_inputs2, batch_masks1, batch_masks2, batch_targets = tuple(b.to(device) for b in batch)\n",
    "        batch_inputs1 = batch_inputs1.squeeze(1)\n",
    "        batch_inputs2 = batch_inputs2.squeeze(1)\n",
    "        batch_masks1 = batch_masks1.squeeze(1)\n",
    "        batch_masks2 = batch_masks2.squeeze(1)\n",
    "        with torch.no_grad():\n",
    "            o1, o2 = model(batch_inputs1, batch_inputs2, batch_masks1, batch_masks2)   \n",
    "\n",
    "        #loss = loss_function(outputs, batch_targets, None, None)\n",
    "        #test_loss.append(loss.item())\n",
    "        #print(outputs.shape, batch_targets.shape)\n",
    "        #preds = outputs.cpu().squeeze(1).numpy()\n",
    "        targs = batch_targets.cpu().numpy()\n",
    "        #predicted +=list(preds)\n",
    "        targets += list(targs)\n",
    "        cosine_sims += list(renormalise_similarity_score(nn.CosineSimilarity()(o1, o2).cpu().numpy()))\n",
    "    #print(len(predicted))\n",
    "    #pearson_score1 = pearson(torch.tensor(renormalise_similarity_score(predicted)), torch.tensor(targets))\n",
    "    pearson_score2 = pearson(torch.tensor(cosine_sims), torch.tensor(targets))\n",
    "    return pearson_score2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2237fca9",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52af801f814d4bcf9f07f2232f85201f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/171 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Pearson Corr Train:  0.7479427055801519\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dedf890ed6a34f08aff71bfeab5e99e3",
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Pearson Corr Val:  0.6536821917292049\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3504add5882b4f72b0fda3ee8f1c2646",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/195 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Pearson Corr Test:  0.677634171115753\n"
     ]
    }
   ],
   "source": [
    "print(\"Initial Pearson Corr Train: \",evaluate(model, loss_fn, train_dataloader, device))\n",
    "print(\"Initial Pearson Corr Val: \", evaluate(model, loss_fn, val_dataloader, device))\n",
    "print(\"Initial Pearson Corr Test: \", evaluate(model, loss_fn, test_dataloader, device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3f5dbd4",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.nn.utils.clip_grad import clip_grad_norm\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "from transformers import AdamW\n",
    "\n",
    "\n",
    "freeze = False #set to True for 3rd baseline\n",
    "\n",
    "epochs = 10\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "if freeze:\n",
    "  optimizer = AdamW(model.regressor.parameters(), lr=1e-5, eps=1e-8) \n",
    "else:\n",
    "  optimizer = AdamW(model.parameters(), lr=1e-5, eps=1e-8)    \n",
    "scheduler = get_linear_schedule_with_warmup(optimizer,num_warmup_steps=0, num_training_steps=total_steps)\n",
    "\n",
    "\n",
    "def train(model, optimizer, scheduler, loss_fn, epochs, train_dataloader, device, clip_value=2):\n",
    "    best_so_far_pearson=-1.0\n",
    "    for epoch_i in range(epochs):\n",
    "        print(f\"Epoch {epoch_i}\")\n",
    "        print(\"------------------------------------------------------------\")\n",
    "        best_loss = 1e10\n",
    "\n",
    "        t0_epoch, t0_batch = time.time(), time.time()\n",
    "        total_loss, batch_loss, batch_counts = 0, 0, 0\n",
    "        \n",
    "        model.train()\n",
    "        for step, batch in enumerate(train_dataloader): \n",
    "            batch_counts +=1\n",
    "            \n",
    "            batch_inputs1, batch_inputs2, batch_masks1, batch_masks2, batch_targets = tuple(b.to(device) for b in batch)\n",
    "            batch_inputs1 = batch_inputs1.squeeze(1)\n",
    "            batch_inputs2 = batch_inputs2.squeeze(1)\n",
    "            batch_masks1 = batch_masks1.squeeze(1)\n",
    "            batch_masks2 = batch_masks2.squeeze(1)\n",
    "            \n",
    "            model.zero_grad()\n",
    "            \n",
    "            o1, o2 = model(batch_inputs1, batch_inputs2, batch_masks1, batch_masks2)           \n",
    "            \n",
    "            loss = loss_fn(batch_targets.float().squeeze(), o1, o2)\n",
    "            batch_loss += loss.item()\n",
    "            total_loss += loss.item()\n",
    "            \n",
    "            loss.backward()\n",
    "            \n",
    "            if freeze:\n",
    "              clip_grad_norm(model.regressor.parameters(), clip_value)\n",
    "            else:\n",
    "              clip_grad_norm(model.parameters(), clip_value)\n",
    "            \n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "            \n",
    "            if (step % 50 == 0 and step != 0) or (step == len(train_dataloader) - 1):\n",
    "                # Calculate time elapsed for 20 batches\n",
    "                time_elapsed = time.time() - t0_batch\n",
    "\n",
    "                # Print training results\n",
    "                print(f\"{epoch_i + 1:^7} | {step:^7} | {batch_loss / batch_counts:^12.6f} | {'-':^10} | {'-':^9} | {time_elapsed:^9.2f}\")\n",
    "\n",
    "                # Reset batch tracking variables\n",
    "                batch_loss, batch_counts = 0, 0\n",
    "                t0_batch = time.time()\n",
    "#             with torch.no_grad():\n",
    "#                 del outputs\n",
    "#                 torch.cuda.empty_cache()\n",
    "        \n",
    "#             show_gpu(f'{epoch_i}: GPU memory usage after training model:')\n",
    "            del loss\n",
    "            torch.cuda.empty_cache()\n",
    "            torch.cuda.synchronize()\n",
    "#             show_gpu(f'{epoch_i}: GPU memory usage after clearing cache:')\n",
    "        \n",
    "        avg_train_loss = total_loss / len(train_dataloader)\n",
    "\n",
    "        print(\"-\"*70)\n",
    "        print(f\"{epoch_i + 1:^7} | {'-':^7} | {avg_train_loss:^12.6f}\")\n",
    "        val_pearson_cos = evaluate(model, loss_fn, val_dataloader, device)\n",
    "        print(\"Validation: Pearson Corr Cosine-\",val_pearson_cos)\n",
    "        if val_pearson_cos>best_so_far_pearson:\n",
    "          best_so_far_pearson = val_pearson_cos\n",
    "          torch.save(model, \"ST.pt\")\n",
    "        print();\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "108cfffa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T13:53:12.859234Z",
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   "source": [
    "# from numba import cuda \n",
    "# dev = cuda.get_current_device()\n",
    "# dev.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f82b5225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T13:53:13.105004Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:50: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1    |   50    |   0.054204   |     -      |     -     |   40.85  \n",
      "   1    |   100   |   0.052520   |     -      |     -     |   40.06  \n",
      "   1    |   150   |   0.049564   |     -      |     -     |   40.12  \n",
      "   1    |   170   |   0.046232   |     -      |     -     |   15.86  \n",
      "----------------------------------------------------------------------\n",
      "   1    |    -    |   0.051422  \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd09d7588dcc45aeb20c83f62a61ea77",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7168582059566329\n",
      "\n",
      "Epoch 1\n",
      "------------------------------------------------------------\n",
      "   2    |   50    |   0.032589   |     -      |     -     |   40.75  \n",
      "   2    |   100   |   0.030483   |     -      |     -     |   40.04  \n",
      "   2    |   150   |   0.032277   |     -      |     -     |   40.04  \n",
      "   2    |   170   |   0.033557   |     -      |     -     |   15.85  \n",
      "----------------------------------------------------------------------\n",
      "   2    |    -    |   0.031995  \n"
     ]
    },
    {
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       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7340271101793521\n",
      "\n",
      "Epoch 2\n",
      "------------------------------------------------------------\n",
      "   3    |   50    |   0.021226   |     -      |     -     |   40.78  \n",
      "   3    |   100   |   0.022623   |     -      |     -     |   40.05  \n",
      "   3    |   150   |   0.021663   |     -      |     -     |   40.04  \n",
      "   3    |   170   |   0.022741   |     -      |     -     |   15.85  \n",
      "----------------------------------------------------------------------\n",
      "   3    |    -    |   0.021939  \n"
     ]
    },
    {
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       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7243887678658407\n",
      "\n",
      "Epoch 3\n",
      "------------------------------------------------------------\n",
      "   4    |   50    |   0.017144   |     -      |     -     |   40.65  \n",
      "   4    |   100   |   0.015256   |     -      |     -     |   40.09  \n",
      "   4    |   150   |   0.014055   |     -      |     -     |   40.12  \n",
      "   4    |   170   |   0.013869   |     -      |     -     |   15.87  \n",
      "----------------------------------------------------------------------\n",
      "   4    |    -    |   0.015306  \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c1d32637a644897aaf4838dec924446",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7238615056348451\n",
      "\n",
      "Epoch 4\n",
      "------------------------------------------------------------\n",
      "   5    |   50    |   0.011598   |     -      |     -     |   40.63  \n",
      "   5    |   100   |   0.011936   |     -      |     -     |   40.06  \n",
      "   5    |   150   |   0.012484   |     -      |     -     |   40.07  \n",
      "   5    |   170   |   0.010426   |     -      |     -     |   15.84  \n",
      "----------------------------------------------------------------------\n",
      "   5    |    -    |   0.011819  \n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.726113766345385\n",
      "\n",
      "Epoch 5\n",
      "------------------------------------------------------------\n",
      "   6    |   50    |   0.011258   |     -      |     -     |   40.64  \n",
      "   6    |   100   |   0.008094   |     -      |     -     |   40.05  \n",
      "   6    |   150   |   0.009411   |     -      |     -     |   40.03  \n",
      "   6    |   170   |   0.010219   |     -      |     -     |   15.89  \n",
      "----------------------------------------------------------------------\n",
      "   6    |    -    |   0.009671  \n"
     ]
    },
    {
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       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7242037249242217\n",
      "\n",
      "Epoch 6\n",
      "------------------------------------------------------------\n",
      "   7    |   50    |   0.007446   |     -      |     -     |   40.68  \n",
      "   7    |   100   |   0.007930   |     -      |     -     |   40.07  \n",
      "   7    |   150   |   0.007798   |     -      |     -     |   40.09  \n",
      "   7    |   170   |   0.010421   |     -      |     -     |   15.85  \n",
      "----------------------------------------------------------------------\n",
      "   7    |    -    |   0.008038  \n"
     ]
    },
    {
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       "version_minor": 0
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       "  0%|          | 0/36 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Pearson Corr Cosine- 0.7240477420339354\n",
      "\n",
      "Epoch 7\n",
      "------------------------------------------------------------\n",
      "   8    |   50    |   0.007572   |     -      |     -     |   40.67  \n",
      "   8    |   100   |   0.005528   |     -      |     -     |   40.02  \n",
      "   8    |   150   |   0.008308   |     -      |     -     |   40.04  \n",
      "   8    |   170   |   0.007371   |     -      |     -     |   15.84  \n",
      "----------------------------------------------------------------------\n",
      "   8    |    -    |   0.007166  \n"
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      "Validation: Pearson Corr Cosine- 0.7241016155031484\n",
      "\n",
      "Epoch 8\n",
      "------------------------------------------------------------\n",
      "   9    |   50    |   0.006444   |     -      |     -     |   40.66  \n",
      "   9    |   100   |   0.006536   |     -      |     -     |   40.04  \n",
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      "----------------------------------------------------------------------\n",
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      "Validation: Pearson Corr Cosine- 0.7225134353667837\n",
      "\n",
      "Epoch 9\n",
      "------------------------------------------------------------\n",
      "  10    |   50    |   0.005526   |     -      |     -     |   40.71  \n",
      "  10    |   100   |   0.005800   |     -      |     -     |   40.12  \n",
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      "  10    |   170   |   0.004863   |     -      |     -     |   15.86  \n",
      "----------------------------------------------------------------------\n",
      "  10    |    -    |   0.005716  \n"
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      "Validation: Pearson Corr Cosine- 0.7218763969040068\n",
      "\n"
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   ],
   "source": [
    "# %%time\n",
    "model = train(model, optimizer, scheduler, loss_fn, epochs,train_dataloader, device, clip_value=2)"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
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   "id": "67be6433",
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     "iopub.execute_input": "2022-04-01T14:17:17.912671Z",
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     "status": "completed"
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   },
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   "source": [
    "from numba import cuda \n",
    "# device = cuda.get_current_device()\n",
    "# device.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a595504",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "model1 = torch.load(\"ST.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d807e801",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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     "name": "stdout",
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     "text": [
      "Final Pearson Corr Train:  0.9291495620770891\n"
     ]
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    {
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     "name": "stdout",
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     "text": [
      "Final Pearson Corr Val:  0.7340274132174155\n"
     ]
    },
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      "Final Pearson Corr Test:  0.7518581658802075\n"
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   ],
   "source": [
    "print(\"Final Pearson Corr Train: \",evaluate(model1, loss_fn, train_dataloader, device))\n",
    "print(\"Final Pearson Corr Val: \", evaluate(model1, loss_fn, val_dataloader, device))\n",
    "print(\"Final Pearson Corr Test: \", evaluate(model1, loss_fn, test_dataloader, device))"
   ]
  }
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