fake-covid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-22T20:56:30.349927Z",
     "start_time": "2023-10-22T20:56:28.451433Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ebevoe42UgLK",
    "outputId": "cefa9dd9-3d2c-47f6-bda2-b19c80c24f63",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "  Downloading transformers-4.34.1-py3-none-any.whl (7.7 MB)\n",
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      "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
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      "Collecting tokenizers<0.15,>=0.14 (from transformers)\n",
      "  Downloading tokenizers-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB)\n",
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      "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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      "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
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      "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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      "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
      "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
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      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
      "Installing collected packages: safetensors, dill, multiprocess, huggingface-hub, tokenizers, transformers, datasets\n",
      "Successfully installed datasets-2.14.6 dill-0.3.7 huggingface-hub-0.17.3 multiprocess-0.70.15 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.1\n"
     ]
    }
   ],
   "source": [
    "! pip install transformers plotly datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in /home/stepan/.local/lib/python3.8/site-packages (1.5.16)\n",
      "Requirement already satisfied: six>=1.10 in /usr/lib/python3/dist-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: requests in /home/stepan/.local/lib/python3.8/site-packages (from kaggle) (2.22.0)\n",
      "Requirement already satisfied: python-slugify in /home/stepan/.local/lib/python3.8/site-packages (from kaggle) (8.0.1)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.66.1)\n",
      "Requirement already satisfied: certifi in /home/stepan/.local/lib/python3.8/site-packages (from kaggle) (2023.7.22)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: bleach in /usr/lib/python3/dist-packages (from kaggle) (4.1.0)\n",
      "Requirement already satisfied: urllib3 in /home/stepan/.local/lib/python3.8/site-packages (from kaggle) (1.25.11)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /home/stepan/.local/lib/python3.8/site-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /home/stepan/.local/lib/python3.8/site-packages (from requests->kaggle) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/stepan/.local/lib/python3.8/site-packages (from requests->kaggle) (3.0.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "! pip install --user kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "! mkdir ~/.kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "! mv kaggle.json ~/.kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/stepan/.kaggle/kaggle.json'\n",
      "Downloading fake-news.zip to /home/stepan/FakeSpaceModel\n",
      "100%|██████████████████████████████████████| 46.5M/46.5M [00:03<00:00, 12.5MB/s]\n",
      "100%|██████████████████████████████████████| 46.5M/46.5M [00:03<00:00, 12.4MB/s]\n"
     ]
    }
   ],
   "source": [
    "! ~/.local/bin/kaggle competitions download -c fake-news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  fake-news.zip\n",
      "  inflating: submit.csv              \n",
      "  inflating: test.csv                \n",
      "  inflating: train.csv               \n"
     ]
    }
   ],
   "source": [
    "! unzip fake-news.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Ud9mPBrMnep",
    "outputId": "579591cc-c40d-4fef-fc27-2079fe5ef3ae",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'space-model'...\n",
      "remote: Enumerating objects: 128, done.\u001b[K\n",
      "remote: Counting objects: 100% (128/128), done.\u001b[K\n",
      "remote: Compressing objects: 100% (106/106), done.\u001b[K\n",
      "remote: Total 128 (delta 33), reused 111 (delta 19), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (128/128), 29.56 MiB | 5.29 MiB/s, done.\n",
      "Resolving deltas: 100% (33/33), done.\n"
     ]
    }
   ],
   "source": [
    "! rm -rf space-model\n",
    "! git clone https://github.com/StepanTita/space-model.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ppjscAk9Mwxc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append('space-model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "E-jG_7erM1jL",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stepan/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import json\n",
    "from collections import Counter\n",
    "import random\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification\n",
    "from transformers import DataCollatorWithPadding, get_linear_schedule_with_warmup\n",
    "\n",
    "from datasets import load_dataset, Dataset, DatasetDict\n",
    "\n",
    "from space_model.model import *\n",
    "from space_model.loss import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "DgD4mX6CPqAx",
    "tags": []
   },
   "outputs": [],
   "source": [
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "287PDnJQPm4K",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "seed_everything(seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "gdyFsHsxPtFR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def on_gpu(f):\n",
    "    def wrapper(*args):\n",
    "        if torch.cuda.is_available():\n",
    "            return f(*args)\n",
    "        else:\n",
    "            print('cuda unavailable')\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QdNbXp-oPw7W",
    "outputId": "6a0dff07-c47d-4c4a-c294-81451c25b76f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pynvml in /home/stepan/.local/lib/python3.8/site-packages (11.5.0)\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    ! pip install pynvml\n",
    "    from pynvml import *\n",
    "    from numba import cuda\n",
    "\n",
    "@on_gpu\n",
    "def print_gpu_utilization(dev_id):\n",
    "    try:\n",
    "        nvmlInit()\n",
    "        handle = nvmlDeviceGetHandleByIndex(dev_id)\n",
    "        info = nvmlDeviceGetMemoryInfo(handle)\n",
    "        print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "@on_gpu\n",
    "def free_gpu_cache(dev_id=0):\n",
    "    print(\"Initial GPU Usage\")\n",
    "    print_gpu_utilization(dev_id)\n",
    "\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    print(\"GPU Usage after emptying the cache\")\n",
    "    print_gpu_utilization(dev_id)\n",
    "\n",
    "def print_summary(result):\n",
    "    print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n",
    "    print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n",
    "    print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1cHTPAppKacl",
    "tags": []
   },
   "outputs": [],
   "source": [
    "device_id = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VxNX0IVTNAM5",
    "outputId": "cf6bc67c-4e1f-423a-aa7b-60d85ae2dc9b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(f'cuda:{device_id}' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "KYakW_Q_JQut",
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.cuda.set_device(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "XH_wO_V2O0dh",
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = 'bert-base-cased'\n",
    "MODELS_PATH = 'models'\n",
    "DATASET_NAME = 'covid-fake-news'\n",
    "\n",
    "SEED = 42\n",
    "NUM_EPOCHS = 5\n",
    "BATCH_SIZE = 32\n",
    "MAX_SEQ_LEN = 512\n",
    "LEARNING_RATE = 2e-5\n",
    "MAX_GRAD_NORM = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>Aamir Khan Donate 250 Cr. In PM Relief Cares Fund</td>\n",
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       "                                                  tweet label\n",
       "id                                                           \n",
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       "4     #IndiaFightsCorona: We have 1524 #COVID testin...  real\n",
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       "...                                                 ...   ...\n",
       "6416  A tiger tested positive for COVID-19 please st...  fake\n",
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       "6419  Aamir Khan Donate 250 Cr. In PM Relief Cares Fund  fake\n",
       "6420  It has been 93 days since the last case of COV...  real\n",
       "\n",
       "[6420 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('data/covid_train.csv', index_col=0)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "                                                  tweet label\n",
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       "\n",
       "[2140 rows x 2 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('data/covid_test.csv', index_col=0)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "                                                  tweet label\n",
       "id                                                           \n",
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       "5     6/10 Sky's @EdConwaySky explains the latest #C...  real\n",
       "...                                                 ...   ...\n",
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       "2140  Another 4422 new coronavirus cases have been c...  real\n",
       "\n",
       "[2140 rows x 2 columns]"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df = pd.read_csv('data/covid_val.csv', index_col=0)\n",
    "val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def encode_label(s: str):\n",
    "    if s == 'fake':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
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       "                                                  tweet  label\n",
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       "...                                                 ...    ...\n",
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       "6419  Aamir Khan Donate 250 Cr. In PM Relief Cares Fund      1\n",
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       "\n",
       "[6420 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['label'] = train_df['label'].apply(encode_label)\n",
    "val_df['label'] = val_df['label'].apply(encode_label)\n",
    "test_df['label'] = test_df['label'].apply(encode_label)\n",
    "train_df"
   ]
  },
  {
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      "fc318a7437144dd887e1f85a106b9ef5",
      "cba0699826ac477296d6d08a47081f19"
     ]
    },
    "id": "ExLCdYtWN7Np",
    "outputId": "7ee8235c-5781-48d8-cdd6-8da29a2f9204",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['tweet', 'label', 'id'],\n",
       "        num_rows: 6420\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['tweet', 'label', 'id'],\n",
       "        num_rows: 2140\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['tweet', 'label', 'id'],\n",
       "        num_rows: 2140\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = DatasetDict({\n",
    "    'train': Dataset.from_pandas(train_df[['tweet', 'label']]),\n",
    "    'test': Dataset.from_pandas(test_df[['tweet', 'label']]),\n",
    "    'val': Dataset.from_pandas(val_df[['tweet', 'label']])\n",
    "})\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298,
     "referenced_widgets": [
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     ]
    },
    "id": "ku5ofLQBO0Ib",
    "outputId": "dee74f4c-8325-4bf3-c706-931ced368d72",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertTokenizerFast(name_or_path='bert-base-cased', vocab_size=28996, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
       "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451,
     "referenced_widgets": [
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    },
    "id": "bXCIC4EvJQuv",
    "outputId": "8ec15f53-885f-4cbc-e2f7-b55a6e5e7ac7",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfe1a8fc2b3a4ee696dea9ba12b13e98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/6420 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7baa9fb394043cbb216f4752c4d73c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2140 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7939cfc41df44e38a94225dd4ad03990",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2140 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['tweet', 'label', 'id', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "        num_rows: 6420\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['tweet', 'label', 'id', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "        num_rows: 2140\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['tweet', 'label', 'id', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "        num_rows: 2140\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset = dataset.map(lambda x: tokenizer(x['tweet'], truncation=True, padding='max_length', max_length=MAX_SEQ_LEN, return_tensors='pt'), batched=True)\n",
    "tokenized_dataset.set_format('torch', device=device)\n",
    "tokenized_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QU3O5O2FJQuv",
    "outputId": "49165f6a-fc1d-4c8a-84cd-406ab4639cd3",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME).to(device)\n",
    "base_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "1NzT1ZxdQiQj",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Q5VVJldIJQuw",
    "tags": []
   },
   "outputs": [],
   "source": [
    "for p in base_model.bert.parameters():\n",
    "    p.requires_grad = False\n",
    "# for p in base_model.pre_classifier.parameters():\n",
    "#     p.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4pz3HY6kJQuw",
    "outputId": "1062d4bb-5543-4fa1-8d93-cbf349189ca6",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108311810"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_parameters(base_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Tru9Ev77QseN",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def eval(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.eval()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper\n",
    "\n",
    "def train(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.train()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "OF6FIFmxTFy4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "@train\n",
    "def train_epoch(model, train_dataloader, optimizer, scheduler, config):\n",
    "    train_loss = 0.0\n",
    "    train_preds = []\n",
    "    train_labels = []\n",
    "\n",
    "    for step, batch in enumerate(tqdm(train_dataloader, total=len(train_dataloader))):\n",
    "        ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "        mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "        targets = batch['label'].to(device, dtype = torch.long)\n",
    "\n",
    "        outputs = model(input_ids=ids, attention_mask=mask, labels=targets) # (B, Seq_Len, 2)\n",
    "\n",
    "        loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "        probs = F.softmax(logits, dim=-1).cpu()\n",
    "        pred = torch.argmax(probs, dim=-1) # (B)\n",
    "        train_preds += pred.detach().tolist()\n",
    "        train_labels += [l.item() for l in targets.cpu()]\n",
    "        \n",
    "        if (step + 1) % config['gradient_accumulation_steps'] == 0:\n",
    "            scheduler.step()\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        \n",
    "        batch_loss = loss.item()\n",
    "        if config['gradient_accumulation_steps'] > 1:\n",
    "            batch_loss = batch_loss / config['gradient_accumulation_steps']\n",
    "\n",
    "        train_loss += batch_loss\n",
    "    return train_loss, train_preds, train_labels\n",
    "\n",
    "@eval\n",
    "def eval_epoch(model, val_dataloader):\n",
    "    val_loss = 0.0\n",
    "    val_preds = []\n",
    "    val_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for step, batch in enumerate(tqdm(val_dataloader, total=len(val_dataloader))):\n",
    "            ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "            mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "            targets = batch['label'].to(device, dtype = torch.long)\n",
    "\n",
    "            outputs = model(input_ids=ids, attention_mask=mask, labels=targets)\n",
    "\n",
    "            loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "            probs = F.softmax(logits, dim=-1).cpu()\n",
    "            pred = torch.argmax(probs, dim=-1) # (B)\n",
    "            val_preds += pred.detach().tolist()\n",
    "            val_labels += [l.item() for l in targets.cpu()]\n",
    "\n",
    "            val_loss += loss.item()\n",
    "    return val_loss, val_preds, val_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "COJTR-VlQzGz",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model, train_data, val_data, config):\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = torch.optim.AdamW(\n",
    "        params=model.parameters(),\n",
    "        lr=config['lr'],\n",
    "        weight_decay=config['weight_decay']\n",
    "    )\n",
    "\n",
    "    num_train_steps = int(len(train_data) / config['batch_size'] * config['epochs'])\n",
    "    steps_per_epoch = len(train_data) / config['batch_size']\n",
    "    \n",
    "    scheduler = get_linear_schedule_with_warmup(\n",
    "        optimizer,\n",
    "        num_warmup_steps=config['num_warmup_steps'],\n",
    "        num_training_steps=num_train_steps,\n",
    "    )\n",
    "\n",
    "    print(f'Train steps: {num_train_steps}')\n",
    "    print(f'Steps per epoch: {steps_per_epoch}')\n",
    "\n",
    "    train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=config['batch_size'], shuffle=True)\n",
    "    val_dataloader = torch.utils.data.DataLoader(val_data, batch_size=config['batch_size'])\n",
    "\n",
    "    history = {\n",
    "        'train_losses': [],\n",
    "        'val_losses': [],\n",
    "        'train_acc': [],\n",
    "        'val_acc': [],\n",
    "        'train_f1': [],\n",
    "        'val_f1': [],\n",
    "        'train_precision': [],\n",
    "        'val_precision': [],\n",
    "        'train_recall': [],\n",
    "        'val_recall': [],\n",
    "    }\n",
    "\n",
    "    for epoch_num in range(config['epochs']):\n",
    "        print(f'Epoch: {epoch_num + 1}')\n",
    "\n",
    "        # train stage\n",
    "        train_loss, train_preds, train_labels = train_epoch(model, train_dataloader, optimizer, scheduler, config)\n",
    "\n",
    "        # eval stage\n",
    "        val_loss, val_preds, val_labels = eval_epoch(model, val_dataloader)\n",
    "\n",
    "        # metrics\n",
    "        train_acc = accuracy_score(train_labels, train_preds)\n",
    "        val_acc = accuracy_score(val_labels, val_preds)\n",
    "        train_f1 = f1_score(train_labels, train_preds, average='macro')\n",
    "        val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "        train_precision = precision_score(train_labels, train_preds)\n",
    "        val_precision = precision_score(val_labels, val_preds)\n",
    "        train_recall = recall_score(train_labels, train_preds)\n",
    "        val_recall = recall_score(val_labels, val_preds)\n",
    "\n",
    "        history['train_losses'].append(train_loss / len(train_dataloader))\n",
    "        history['val_losses'].append(val_loss / len(val_dataloader))\n",
    "        history['train_acc'].append(train_acc)\n",
    "        history['val_acc'].append(val_acc)\n",
    "        history['train_f1'].append(train_f1)\n",
    "        history['val_f1'].append(val_f1)\n",
    "        history['train_precision'].append(train_precision)\n",
    "        history['val_precision'].append(val_precision)\n",
    "        history['train_recall'].append(train_recall)\n",
    "        history['val_recall'].append(val_recall)\n",
    "\n",
    "        print()\n",
    "        print(f'Train loss: {train_loss / len(train_dataloader)} | Val loss: {val_loss / len(val_dataloader)}')\n",
    "        print(f'Train acc: {train_acc} | Val acc: {val_acc}')\n",
    "        print(f'Train f1: {train_f1} | Val f1: {val_f1}')\n",
    "        print(f'Train precision: {train_precision} | Val precision: {val_precision}')\n",
    "        print(f'Train recall: {train_recall} | Val recall: {val_recall}')\n",
    "\n",
    "    free_gpu_cache(device_id)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_scheduler_stats(stats):\n",
    "    epochs_x = list(range(1, len(stats['lr_history']) + 1))\n",
    "\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "    ax1.plot(epochs_x, stats['lr_history'])\n",
    "    ax1.set_title('Learning rates')\n",
    "    ax1.set_xlabel('epoch')\n",
    "    ax1.set_ylabel('learning_rate')\n",
    "\n",
    "    ax2.plot(epochs_x, stats['weights_history'])\n",
    "    ax2.set_title('Weights')\n",
    "    ax2.set_xlabel('epoch')\n",
    "    ax2.set_ylabel('weight')\n",
    "\n",
    "    plt.title('Scheduler stats')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JXJRyZ4ZJQux",
    "outputId": "819f0611-baf2-4d67-a7d6-f76f17db003c",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.5741, device='cuda:1', grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = tokenized_dataset['test'][1]['input_ids'].unsqueeze(0)\n",
    "mask = tokenized_dataset['test'][1]['attention_mask'].unsqueeze(0)\n",
    "targets = tokenized_dataset['test'][1]['label'].unsqueeze(0)\n",
    "\n",
    "ids = ids.to(device)\n",
    "mask = mask.to(device)\n",
    "targets = targets.to(device)\n",
    "\n",
    "out = base_model(ids, mask, labels=targets)\n",
    "out.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "j4NnMuDdJQuy",
    "tags": []
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    'epochs': NUM_EPOCHS,\n",
    "    'batch_size': BATCH_SIZE,\n",
    "    'gradient_accumulation_steps': 1,\n",
    "    'fp16': False,\n",
    "    'lr': LEARNING_RATE,\n",
    "    'max_grad_norm': MAX_GRAD_NORM,\n",
    "    'weight_decay': 0.01,\n",
    "    'num_warmup_steps': 0,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EQNqpZRGJQuy",
    "outputId": "1d03404e-7507-4e4d-d190-52f703c71701",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 1003\n",
      "Steps per epoch: 200.625\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 201/201 [02:58<00:00,  1.12it/s]\n",
      "100%|███████████████████████████████████████████| 67/67 [00:22<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.24361321445443293 | Val loss: 0.09892841616172844\n",
      "Train acc: 0.8975077881619937 | Val acc: 0.9649532710280374\n",
      "Train f1: 0.8969696035501873 | Val f1: 0.9648718940352475\n",
      "Train precision: 0.9147099447513812 | Val precision: 0.9646017699115044\n",
      "Train recall: 0.865686274509804 | Val recall: 0.961764705882353\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 201/201 [03:01<00:00,  1.11it/s]\n",
      "100%|███████████████████████████████████████████| 67/67 [00:22<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.06738879851561355 | Val loss: 0.08543544810668413\n",
      "Train acc: 0.9786604361370717 | Val acc: 0.9724299065420561\n",
      "Train f1: 0.978608281353659 | Val f1: 0.9723385250996226\n",
      "Train precision: 0.9802826158396319 | Val precision: 0.9819458375125376\n",
      "Train recall: 0.9748366013071895 | Val recall: 0.9598039215686275\n",
      "Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 201/201 [03:01<00:00,  1.11it/s]\n",
      "100%|███████████████████████████████████████████| 67/67 [00:22<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.026908338551794118 | Val loss: 0.09276305744598216\n",
      "Train acc: 0.9912772585669782 | Val acc: 0.9761682242990655\n",
      "Train f1: 0.9912581699346406 | Val f1: 0.9761039340929248\n",
      "Train precision: 0.9908496732026144 | Val precision: 0.9801783944499505\n",
      "Train recall: 0.9908496732026144 | Val recall: 0.9696078431372549\n",
      "Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 201/201 [03:01<00:00,  1.11it/s]\n",
      "100%|███████████████████████████████████████████| 67/67 [00:22<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.011611287608344237 | Val loss: 0.09439691284900781\n",
      "Train acc: 0.9970404984423676 | Val acc: 0.9775700934579439\n",
      "Train f1: 0.9970338037673221 | Val f1: 0.9775084868676889\n",
      "Train precision: 0.997708674304419 | Val precision: 0.9821428571428571\n",
      "Train recall: 0.996078431372549 | Val recall: 0.9705882352941176\n",
      "Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 201/201 [03:01<00:00,  1.11it/s]\n",
      "100%|███████████████████████████████████████████| 67/67 [00:22<00:00,  3.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.0069783302898569705 | Val loss: 0.09651369735626365\n",
      "Train acc: 0.9989096573208722 | Val acc: 0.97803738317757\n",
      "Train f1: 0.9989072231745721 | Val f1: 0.9779802566664018\n",
      "Train precision: 0.9993457638207393 | Val precision: 0.9812067260138477\n",
      "Train recall: 0.9983660130718954 | Val recall: 0.9725490196078431\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 23951 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 3883 MB.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "history = training(base_model, tokenized_dataset['train'], tokenized_dataset['val'], config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "3Z1HxIVHI1Ox",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_results(history, do_val=True):\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "    x = list(range(0, len(history['train_losses'])))\n",
    "\n",
    "    # loss\n",
    "\n",
    "    ax.plot(x, history['train_losses'], label='train_loss')\n",
    "\n",
    "    if do_val:\n",
    "        ax.plot(x, history['val_losses'], label='val_loss')\n",
    "\n",
    "    plt.title('Train / Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    # accuracy\n",
    "\n",
    "    if 'train_acc' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_acc'], label='train_acc')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_acc'], label='val_acc')\n",
    "\n",
    "    plt.title('Train / Validation Accuracy')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    # f1-score\n",
    "\n",
    "    if 'train_f1' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_f1'], label='train_f1')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_f1'], label='val_f1')\n",
    "\n",
    "        plt.title('Train / Validation F1')\n",
    "        plt.legend(loc='upper right')\n",
    "\n",
    "    # precision\n",
    "\n",
    "    if 'train_precision' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_precision'], label='train_precision')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_precision'], label='val_precision')\n",
    "\n",
    "        plt.title('Train / Validation Precision')\n",
    "        plt.legend(loc='upper right')\n",
    "\n",
    "    # recall\n",
    "\n",
    "    if 'train_recall' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_recall'], label='train_recall')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_recall'], label='val_recall')\n",
    "\n",
    "        plt.title('Train / Validation Recall')\n",
    "        plt.legend(loc='upper right')\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "bG7e7khvJQuz",
    "outputId": "222fe106-f46b-469d-db2b-c01222f8b1d0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "tXbl4gEnJQuz",
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(base_model.state_dict(), f'{MODELS_PATH}/{DATASET_NAME}_{MODEL_NAME}_{NUM_EPOCHS}.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "FfReHdHLJQuz",
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_dataset = tokenized_dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "dQXkQPscJQu0",
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=config['batch_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 67/67 [00:20<00:00,  3.24it/s]\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_preds, val_labels = eval_epoch(base_model, val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "KjpdE0mdJQu0",
    "outputId": "006991a9-afd7-421d-adb5-4ccd4d8eeb94",
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_acc = accuracy_score(val_labels, val_preds)\n",
    "val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "val_precision = precision_score(val_labels, val_preds)\n",
    "val_recall = recall_score(val_labels, val_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "y7RgOP_9JQu0",
    "outputId": "44e7cc2f-b1c3-4889-ae28-e77162a49b1e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 0.08461968362880454\n",
      "Val acc: 0.9752336448598131\n",
      "Val f1: 0.9751805276337099\n",
      "Val precision: 0.9735553379040157\n",
      "Val recall: 0.9745098039215686\n"
     ]
    }
   ],
   "source": [
    "print(f'Val loss: {val_loss / len(val_dataloader)}')\n",
    "print(f'Val acc: {val_acc}')\n",
    "print(f'Val f1: {val_f1}')\n",
    "print(f'Val precision: {val_precision}')\n",
    "print(f'Val recall: {val_recall}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 125\n",
      "Steps per epoch: 25.078125\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:07<00:00,  2.58s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.7065824568271637 | Val loss: 0.6938533981641134\n",
      "Train acc: 0.4806853582554517 | Val acc: 0.5130841121495328\n",
      "Train f1: 0.472618661959095 | Val f1: 0.43071796709934423\n",
      "Train precision: 0.46701974000962926 | Val precision: 0.46405228758169936\n",
      "Train recall: 0.6339869281045751 | Val recall: 0.1392156862745098\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "Train precision: 0.6932539682539682 | Val precision: 0.8350824587706147\n",
      "Train recall: 0.5709150326797385 | Val recall: 0.546078431372549\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 13743 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 1061 MB.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.42s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 0.6256567173533969\n",
      "Val acc: 0.7341121495327103\n",
      "Val f1: 0.7228710473915863\n",
      "Val precision: 0.8272859216255443\n",
      "Val recall: 0.5588235294117647\n",
      "Train steps: 125\n",
      "Steps per epoch: 25.078125\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.64s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6365703413119683 | Val loss: 0.6246938109397888\n",
      "Train acc: 0.67601246105919 | Val acc: 0.7224299065420561\n",
      "Train f1: 0.6700362817762059 | Val f1: 0.7057159393448315\n",
      "Train precision: 0.6963141025641025 | Val precision: 0.8491803278688524\n",
      "Train recall: 0.5679738562091503 | Val recall: 0.5078431372549019\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.64s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.636445192190317 | Val loss: 0.6233420504464043\n",
      "Train acc: 0.6763239875389409 | Val acc: 0.7322429906542056\n",
      "Train f1: 0.6674974934128874 | Val f1: 0.7181206480039005\n",
      "Train precision: 0.7121866897147796 | Val precision: 0.8486739469578783\n",
      "Train recall: 0.538562091503268 | Val recall: 0.5333333333333333\n",
      "Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6327253534243658 | Val loss: 0.6224372982978821\n",
      "Train acc: 0.6783489096573209 | Val acc: 0.733177570093458\n",
      "Train f1: 0.6718276868237107 | Val f1: 0.7194728677685818\n",
      "Train precision: 0.7026476578411406 | Val precision: 0.8469860896445132\n",
      "Train recall: 0.5637254901960784 | Val recall: 0.5372549019607843\n",
      "Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6315318850370554 | Val loss: 0.6220224897066752\n",
      "Train acc: 0.6833333333333333 | Val acc: 0.7308411214953271\n",
      "Train f1: 0.676106136581492 | Val f1: 0.7163308589607635\n",
      "Train precision: 0.7138692211578509 | Val precision: 0.8490566037735849\n",
      "Train recall: 0.5601307189542484 | Val recall: 0.5294117647058824\n",
      "Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6353817857228793 | Val loss: 0.6218383113543192\n",
      "Train acc: 0.6761682242990654 | Val acc: 0.7327102803738318\n",
      "Train f1: 0.6692424043311983 | Val f1: 0.7187977828746176\n",
      "Train precision: 0.7017688194158782 | Val precision: 0.8478260869565217\n",
      "Train recall: 0.5575163398692811 | Val recall: 0.5352941176470588\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 13743 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 1061 MB.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 0.6214079194598727\n",
      "Val acc: 0.7378504672897196\n",
      "Val f1: 0.7260026360384109\n",
      "Val precision: 0.84\n",
      "Val recall: 0.5558823529411765\n",
      "Train steps: 125\n",
      "Steps per epoch: 25.078125\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.635602015715379 | Val loss: 0.6200835638576083\n",
      "Train acc: 0.6749221183800623 | Val acc: 0.7434579439252337\n",
      "Train f1: 0.6660399747349159 | Val f1: 0.7326119596098082\n",
      "Train precision: 0.7103329009943796 | Val precision: 0.841799709724238\n",
      "Train recall: 0.5369281045751634 | Val recall: 0.5686274509803921\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6316156960450686 | Val loss: 0.6189138624403212\n",
      "Train acc: 0.6816199376947041 | Val acc: 0.7429906542056075\n",
      "Train f1: 0.6773798280130077 | Val f1: 0.732177395499124\n",
      "Train precision: 0.6935975609756098 | Val precision: 0.8405797101449275\n",
      "Train recall: 0.5947712418300654 | Val recall: 0.5686274509803921\n",
      "Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.64s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.629880999143307 | Val loss: 0.6185053918096755\n",
      "Train acc: 0.6806853582554517 | Val acc: 0.7313084112149533\n",
      "Train f1: 0.6731308978501396 | Val f1: 0.7155959507150087\n",
      "Train precision: 0.7118288590604027 | Val precision: 0.8606158833063209\n",
      "Train recall: 0.5545751633986928 | Val recall: 0.5205882352941177\n",
      "Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6333067554693955 | Val loss: 0.6177829768922594\n",
      "Train acc: 0.6777258566978193 | Val acc: 0.7369158878504672\n",
      "Train f1: 0.6688163096108166 | Val f1: 0.7238788781619732\n",
      "Train precision: 0.7147811009969658 | Val precision: 0.8488549618320611\n",
      "Train recall: 0.5388888888888889 | Val recall: 0.5450980392156862\n",
      "Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.63s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6349073671377622 | Val loss: 0.6175497902764214\n",
      "Train acc: 0.6827102803738317 | Val acc: 0.7429906542056075\n",
      "Train f1: 0.6765923915028599 | Val f1: 0.7317535432690424\n",
      "Train precision: 0.7064997981429149 | Val precision: 0.844574780058651\n",
      "Train recall: 0.5718954248366013 | Val recall: 0.5647058823529412\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 13743 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 1061 MB.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 0.6172457536061605\n",
      "Val acc: 0.7429906542056075\n",
      "Val f1: 0.7328972546285707\n",
      "Val precision: 0.8338068181818182\n",
      "Val recall: 0.5754901960784313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "iterations = 14\n",
    "for i in range(iterations):\n",
    "    print(f'Iteration: {i + 1}')\n",
    "    history = training(base_model, tokenized_dataset['train'], tokenized_dataset['val'], config)\n",
    "    \n",
    "    val_loss, val_preds, val_labels = eval_epoch(base_model, val_dataloader)\n",
    "    \n",
    "    val_acc = accuracy_score(val_labels, val_preds)\n",
    "    val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "    val_precision = precision_score(val_labels, val_preds)\n",
    "    val_recall = recall_score(val_labels, val_preds)\n",
    "    \n",
    "    print(f'Val loss: {val_loss / len(val_dataloader)}')\n",
    "    print(f'Val acc: {val_acc}')\n",
    "    print(f'Val f1: {val_f1}')\n",
    "    print(f'Val precision: {val_precision}')\n",
    "    print(f'Val recall: {val_recall}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LamqccYFJQu0"
   },
   "source": [
    "## Space Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WS9YV-Hljbbf",
    "outputId": "355cc5ea-b749-4791-d8d9-b343eabf91d9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model = AutoModel.from_pretrained(MODEL_NAME)\n",
    "base_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_gSGah9kbqmg",
    "outputId": "5a6297f5-1ea3-49c3-bf11-478ff5e1891c",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpaceModelForSequenceClassification(\n",
       "  (base_model): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (space_model): SpaceModel(\n",
       "    (concept_spaces): ModuleList(\n",
       "      (0-1): 2 x Sequential(\n",
       "        (0): Linear(in_features=768, out_features=128, bias=False)\n",
       "        (1): Tanh()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (classifier): Linear(in_features=256, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "space_model = SpaceModelForSequenceClassification(base_model, n_embed=768, n_latent=128, n_concept_spaces=2, l1=0.1, l2=1e-5, fine_tune=True).to(device)\n",
    "space_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for p in space_model.base_model.parameters():\n",
    "#     p.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zoqfK9Y-jphw",
    "outputId": "c0181f8c-4535-4b4c-e308-7f32d2ba209d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197122"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_parameters(space_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "iPB1GJojZ9gH",
    "tags": []
   },
   "outputs": [],
   "source": [
    "ids = val_dataset[0]['input_ids'].unsqueeze(0)\n",
    "mask = val_dataset[0]['attention_mask'].unsqueeze(0)\n",
    "targets = val_dataset[0]['label'].unsqueeze(0)\n",
    "\n",
    "ids = ids.to(device)\n",
    "mask = mask.to(device)\n",
    "targets = targets.to(device)\n",
    "\n",
    "base_embed = space_model.base_model(ids, mask).last_hidden_state\n",
    "\n",
    "concept_spaces = space_model.space_model(base_embed).concept_spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pOR6VKYsZaon",
    "outputId": "e8516b9c-b642-4755-8758-a3bf054df6b0",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inter-space loss: 1.6700680255889893\n"
     ]
    }
   ],
   "source": [
    "print(f'Inter-space loss: {space_model.l1 * inter_space_loss(concept_spaces) * BATCH_SIZE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "quk-ppIjZcK2",
    "outputId": "d564d759-a2a2-4fdd-9688-a296689f5448",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intra-space loss: 4.909460067749023\n"
     ]
    }
   ],
   "source": [
    "print(f'Intra-space loss: {space_model.l2 * intra_space_loss(concept_spaces) * BATCH_SIZE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'epochs': 5,\n",
       " 'batch_size': 32,\n",
       " 'gradient_accumulation_steps': 1,\n",
       " 'fp16': False,\n",
       " 'lr': 2e-05,\n",
       " 'max_grad_norm': 1000,\n",
       " 'weight_decay': 0.01,\n",
       " 'num_warmup_steps': 0}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# config['epochs'] = 1\n",
    "# config['lr'] = 2e-5\n",
    "\n",
    "# space_model.l1 = 0\n",
    "# space_model.l2 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z_lJuPF4mAGF",
    "outputId": "cd6a060b-bc55-4539-f1f6-f32f75ac0905",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 125\n",
      "Steps per epoch: 25.078125\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:07<00:00,  2.60s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.2545279688560046 | Val loss: 0.24624150494734445\n",
      "Train acc: 0.9062305295950156 | Val acc: 0.902803738317757\n",
      "Train f1: 0.9055919621699515 | Val f1: 0.9021568918810795\n",
      "Train precision: 0.9339689265536724 | Val precision: 0.9291754756871036\n",
      "Train recall: 0.8643790849673203 | Val recall: 0.861764705882353\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.65s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.40s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.2408633564527218 | Val loss: 0.24416124820709229\n",
      "Train acc: 0.9074766355140187 | Val acc: 0.9037383177570093\n",
      "Train f1: 0.9069608711016193 | Val f1: 0.9030976909976076\n",
      "Train precision: 0.9278278972935462 | Val precision: 0.9302325581395349\n",
      "Train recall: 0.8738562091503268 | Val recall: 0.8627450980392157\n",
      "Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.65s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.23639787275057572 | Val loss: 0.242919170194202\n",
      "Train acc: 0.9049844236760125 | Val acc: 0.9037383177570093\n",
      "Train f1: 0.9044185002359151 | Val f1: 0.9030676907840867\n",
      "Train precision: 0.9274249825540823 | Val precision: 0.9320594479830149\n",
      "Train recall: 0.8686274509803922 | Val recall: 0.8607843137254902\n",
      "Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.65s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.23797796953182954 | Val loss: 0.24216219617260826\n",
      "Train acc: 0.9074766355140187 | Val acc: 0.9037383177570093\n",
      "Train f1: 0.9069255559674321 | Val f1: 0.9030524278676988\n",
      "Train precision: 0.9302163293789253 | Val precision: 0.9329787234042554\n",
      "Train recall: 0.8712418300653595 | Val recall: 0.8598039215686275\n",
      "Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 26/26 [01:08<00:00,  2.64s/it]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.2365839137480809 | Val loss: 0.24180645081732008\n",
      "Train acc: 0.9063862928348909 | Val acc: 0.9046728971962616\n",
      "Train f1: 0.9058127079392051 | Val f1: 0.9040237223388875\n",
      "Train precision: 0.9300454704442113 | Val precision: 0.9322033898305084\n",
      "Train recall: 0.8689542483660131 | Val recall: 0.8627450980392157\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 14589 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 1523 MB.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "space_history = training(space_model, tokenized_dataset['train'], tokenized_dataset['val'], config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PCVDqQz9I1O2",
    "outputId": "6c46cf9f-f159-48f0-9524-1ee61897caf7",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 9/9 [00:21<00:00,  2.38s/it]\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_preds, val_labels = eval_epoch(space_model, val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "BlILC2j3L30y",
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_acc = accuracy_score(val_labels, val_preds)\n",
    "val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "val_precision = precision_score(val_labels, val_preds)\n",
    "val_recall = recall_score(val_labels, val_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KeVaz9nAJQu3",
    "outputId": "ba68374f-2ca2-48a0-8ecc-5620891d3871",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 0.24983344309859806\n",
      "Val acc: 0.8957943925233645\n",
      "Val f1: 0.8950928505872202\n",
      "Val precision: 0.9216931216931217\n",
      "Val recall: 0.8539215686274509\n"
     ]
    }
   ],
   "source": [
    "print(f'Val loss: {val_loss / len(val_dataloader)}')\n",
    "print(f'Val acc: {val_acc}')\n",
    "print(f'Val f1: {val_f1}')\n",
    "print(f'Val precision: {val_precision}')\n",
    "print(f'Val recall: {val_recall}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Val loss: 0.24983344309859806\n",
    "Val acc: 0.8957943925233645\n",
    "Val f1: 0.8950928505872202\n",
    "Val precision: 0.9216931216931217\n",
    "Val recall: 0.8539215686274509"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterations = 14\n",
    "for i in range(iterations):\n",
    "    print('*' * 30, end=' ')\n",
    "    print(f'Iteration: {i + 1} ')\n",
    "    print('*' * 30)\n",
    "    \n",
    "    space_history = training(space_model, tokenized_dataset['train'], tokenized_dataset['val'], config)\n",
    "    \n",
    "    val_loss, val_preds, val_labels = eval_epoch(space_model, val_dataloader)\n",
    "    \n",
    "    val_acc = accuracy_score(val_labels, val_preds)\n",
    "    val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "    val_precision = precision_score(val_labels, val_preds)\n",
    "    val_recall = recall_score(val_labels, val_preds)\n",
    "    \n",
    "    print(f'Val loss: {val_loss / len(val_dataloader)}')\n",
    "    print(f'Val acc: {val_acc}')\n",
    "    print(f'Val f1: {val_f1}')\n",
    "    print(f'Val precision: {val_precision}')\n",
    "    print(f'Val recall: {val_recall}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "id": "U0hz4xrnbtX8",
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(space_model.state_dict(), f'{MODELS_PATH}/{DATASET_NAME}_space-{MODEL_NAME}_{NUM_EPOCHS}.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "gTfxyoGEJQu2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset = tokenized_dataset['unsupervised']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T_m4HYdjJbj3",
    "outputId": "add06483-722e-4ed1-d0f0-b45223bfd36a",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7773, 8132, 46980]\n"
     ]
    }
   ],
   "source": [
    "# examples with less than 16 words are easier to visualize, so focus on them\n",
    "examples_ids = []\n",
    "for i, v in enumerate(list(test_dataset['input_ids'])):\n",
    "    s = [t for t in v.cpu() if t != tokenizer.pad_token_id]\n",
    "    if len(s) <= 16:\n",
    "        examples_ids.append(i)\n",
    "print(examples_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Q943tIFJmgr",
    "outputId": "bdbcec92-523f-498c-da76-fada8c01f632",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scatters = []\n",
    "colors = ['blue', 'red']\n",
    "for sample_index in examples_ids:\n",
    "    # extract example, decode to tokens and get the sequence length (ingoring padding)\n",
    "    test_tokenized_batch = test_dataset[sample_index]\n",
    "    tokens = [tokenizer.decode(t) for t in test_tokenized_batch['input_ids'] if t != tokenizer.pad_token_id]\n",
    "    seq_len = len(tokens)\n",
    "\n",
    "    input_ids = test_tokenized_batch['input_ids'].to(device).unsqueeze(0)\n",
    "    attention_mask = test_tokenized_batch['attention_mask'].to(device).unsqueeze(0)\n",
    "\n",
    "    base_embed = space_model.base_model(input_ids, attention_mask).last_hidden_state\n",
    "\n",
    "    concept_spaces = space_model.space_model(base_embed).concept_spaces\n",
    "\n",
    "    print()\n",
    "\n",
    "    for c, embed in enumerate(concept_spaces):\n",
    "        x, y, z = embed[0, :seq_len, 0].detach().cpu().numpy(), embed[0, :seq_len, 1].detach().cpu().numpy(), embed[0, :seq_len, 2].detach().cpu().numpy()\n",
    "\n",
    "        scatters.append(go.Scatter3d(\n",
    "            x=x, y=y, z=z, mode='markers+text',\n",
    "            name=f'Example: {sample_index} ({c})',\n",
    "            text=tokens,\n",
    "            marker=dict(color=colors[c]),\n",
    "            # hovertext=[]\n",
    "        ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "yqojVYN0JogZ",
    "outputId": "d9e92fdf-def5-4a6f-9911-04acd0445867",
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = go.Figure(\n",
    "    data=scatters,\n",
    "    layout=go.Layout(\n",
    "        title=go.layout.Title(text='Space Embeddings')\n",
    "    ))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "id": "_WCjolXJJQu3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig.write_html(f'plots/{DATASET_NAME}_space-{MODEL_NAME}_{NUM_EPOCHS}.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "id": "fLRRQIDtJQu3",
    "outputId": "423dbbd1-42c3-48f6-c4a4-f2ba6f0ce8d7",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# space_bert = SpaceBertForSequenceClassification(bert, n_embed=768, n_latent=3, n_concept_spaces=2, l1=0, l2=0).to(device)\n",
    "# space_bert.load_state_dict(torch.load(f'{MODELS_PATH}/ce_loss_2_epochs.bin'))\n",
    "# space_bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "fwNKTiMmJQu3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def inter_space_loss(concept_spaces: List[torch.Tensor], embed, labels):\n",
    "    \"\"\"\n",
    "    :param concept_spaces: pytorch tensors of shape: (n_concept_spaces, B, seq_len, n_latent)\n",
    "    :param embed: LM embeddings of shape: (B, seq_len, n_embed)\n",
    "    :param labels: labels of shape (B)\n",
    "    :return: loss (scalar)\n",
    "    \"\"\"\n",
    "\n",
    "    loss = 0.0\n",
    "\n",
    "    for k in range(len(concept_spaces)):\n",
    "        latent_embed = concept_spaces[k].permute(0, 2, 1)  # (B, seq_len, n_latent).T -> (B, n_latent, seq_len)\n",
    "        \n",
    "        # match loss\n",
    "        loss += torch.nan_to_num((1 - F.tanh(latent_embed[labels == k] @ embed[labels == k])).mean()).item()  # (B', seq_len, n_latent) * (B', seq_len, n_embed)\n",
    "            \n",
    "        # mismatch loss\n",
    "        loss += torch.nan_to_num((1 + F.tanh(latent_embed[labels != k] @ embed[labels != k])).mean()).item()\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "concept_spaces = [ # (n=2, B=2, seq_len=3, n_latent=4)\n",
    "    torch.tensor([\n",
    "        [\n",
    "            [-1.0, -1.0, -1.0, -1.0],\n",
    "            [-1.0, -1.0, -1.0, -1.0],\n",
    "            [-1.0, -1.0, -1.0, -1.0]\n",
    "        ],\n",
    "        [\n",
    "            [1.0, 1.0, 1.0, 1.0],\n",
    "            [1.0, 1.0, 1.0, 1.0],\n",
    "            [1.0, 1.0, 1.0, 1.0]\n",
    "        ],\n",
    "    ]),\n",
    "    torch.tensor([\n",
    "        [\n",
    "            [1.0, 1.0, 1.0, 1.0],\n",
    "            [1.0, 1.0, 1.0, 1.0],\n",
    "            [1.0, 1.0, 1.0, 1.0]\n",
    "        ],\n",
    "        [\n",
    "            [-1.0, -1.0, -1.0, -1.0],\n",
    "            [-1.0, -1.0, -1.0, -1.0],\n",
    "            [-1.0, -1.0, -1.0, -1.0]\n",
    "        ],\n",
    "    ])\n",
    "]\n",
    "\n",
    "embed = torch.tensor( # (B=2, seq_len=3, n_embed=6)\n",
    "    [\n",
    "        [\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
    "        ],\n",
    "        [\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n",
    "            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
    "        ]\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "labels = torch.tensor([\n",
    "    1, \n",
    "    0\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3794069290161133"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_space_loss(concept_spaces, embed, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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