MP2_3.ipynb

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     "text": [
      "Mounted at /content/drive\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting transformers\n",
      "  Downloading transformers-4.19.2-py3-none-any.whl (4.2 MB)\n",
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      "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
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      "\u001b[?25hCollecting pyyaml>=5.1\n",
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      "  Downloading huggingface_hub-0.7.0-py3-none-any.whl (86 kB)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
      "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
      "  Attempting uninstall: pyyaml\n",
      "    Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "Successfully installed huggingface-hub-0.7.0 pyyaml-6.0 tokenizers-0.12.1 transformers-4.19.2\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "!pip install transformers\n",
    "from transformers import BertTokenizer, BertModel\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "seed = 5\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
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       "      <th>3</th>\n",
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      "text/plain": [
       "   id  label                                              tweet\n",
       "0   0      2  ! <user> as a woman you should not complain ab...\n",
       "1   1      1  ! <user> boy dats cold tyga dwn bad for cuffin...\n",
       "2   2      1  ! <user> dawg ! <user> you ever fuck a bitch a...\n",
       "3   3      1             ! <user> <user> she look like a tranny\n",
       "4   4      1  ! <user> the shit you hear about me might be t..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "filename = 'drive/MyDrive/pre_main_data.csv'\n",
    "df = pd.read_csv(filename)\n",
    "df.tweet = df.tweet.astype(str)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LahEtvQVj6Ak"
   },
   "source": [
    "# Preprocessing"
   ]
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      "text/plain": [
       "   label                                              tweet\n",
       "0      2  as a woman you should not complain about clean...\n",
       "1      1  boy dats cold tyga dwn bad for cuffin dat hoe ...\n",
       "2      1  dawg you ever fuck a bitch and she start to cr...\n",
       "3      1                             she look like a tranny\n",
       "4      1  the shit you hear about me might be true or it..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "df['tweet'] = df['tweet'].map(lambda x:\n",
    "                              x.replace('<user>','').\n",
    "                              replace('!','').\n",
    "                              replace('<emotion>','').\n",
    "                              replace('<number>','').\n",
    "                              replace('<url>',''))\n",
    "df['tweet'] = df['tweet'].map(lambda x:\n",
    "                              re.sub(' +', ' ', x))  #remove extra spaces\n",
    "df['tweet'] = df['tweet'].map(lambda x:\n",
    "                              x.lstrip()) #remove spcaces at the begining\n",
    "del df['id']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S7JAz0lI-Zfe"
   },
   "source": [
    "**Delete duplicate tweets**"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    "id": "TnSH3s50eH9j",
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       "      <td>as a woman you should not complain about clean...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>boy dats cold tyga dwn bad for cuffin dat hoe ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>dawg you ever fuck a bitch and she start to cr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>she look like a tranny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>the shit you hear about me might be true or it...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-637b32f1-c260-4d6b-b987-f8d1b9b7eaf4')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "\n",
       "      <script>\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-637b32f1-c260-4d6b-b987-f8d1b9b7eaf4');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   label                                              tweet\n",
       "0      2  as a woman you should not complain about clean...\n",
       "1      1  boy dats cold tyga dwn bad for cuffin dat hoe ...\n",
       "2      1  dawg you ever fuck a bitch and she start to cr...\n",
       "3      1                             she look like a tranny\n",
       "4      1  the shit you hear about me might be true or it..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[\"tweet\"].duplicated().sum()) #show number of duplicated tweets\n",
    "df.drop_duplicates(inplace=True, subset='tweet')\n",
    "print(df[\"tweet\"].duplicated().sum())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e3MbPK1xwR4q"
   },
   "source": [
    "# Bert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h4BnozZmas47"
   },
   "source": [
    "## Creating Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7NO3PZw9atJA"
   },
   "outputs": [],
   "source": [
    "bert_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
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      "dc4d3f34e625436fb39013ab79c5c90a",
      "6ac271923a404f64ad31442bd5c145f3",
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      "38deab93da794c2e8ad0202a08443ab8",
      "39c592fab493451d94e918dcbfbae2e4",
      "971c2aab624944038aef1a12f8c3f1fb",
      "ec8a029e0df24dff89192c4578a2424c",
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      "a4f662d0f82d4230b5041f119a45da5f",
      "f14b02454bd145b39c79ff806c6ee77a",
      "02f40cd655324fb69b6910fc7cb09799"
     ]
    },
    "id": "szZ91FtMbLAF",
    "outputId": "b38d2255-1185-4863-c45e-72e108dc2e05"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "0df68b16822a4d98b2e4cf03e7a687bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "13b47e4b22674d1381dc832639f31e46",
       "version_major": 2,
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      "text/plain": [
       "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-B0zZTnn-oGq"
   },
   "source": [
    "**Tokenize Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V6eGiFfWa91f"
   },
   "outputs": [],
   "source": [
    "bert_df.tweet = bert_df['tweet'].map(lambda x: \n",
    "                                     tokenizer(x,\n",
    "                                               padding=\"max_length\",\n",
    "                                               truncation=True,\n",
    "                                               max_length=40,\n",
    "                                               return_tensors='pt')) # convert to pytorch tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2p5K1E3KCqt",
    "outputId": "a8391673-9cff-4cc3-8345-4a7b5fd7a28b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24280"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "k4g4jpF4biz2",
    "outputId": "fb3ef521-d101-483c-af04-6e3d95b32a0f"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>[input_ids, token_type_ids, attention_mask]</td>\n",
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       "      <td>[input_ids, token_type_ids, attention_mask]</td>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "   label                                        tweet\n",
       "0      2  [input_ids, token_type_ids, attention_mask]\n",
       "1      1  [input_ids, token_type_ids, attention_mask]\n",
       "2      1  [input_ids, token_type_ids, attention_mask]\n",
       "3      1  [input_ids, token_type_ids, attention_mask]\n",
       "4      1  [input_ids, token_type_ids, attention_mask]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s-1g1o2k-xf8"
   },
   "source": [
    "**Show an example**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6HDf7NkzBE8O",
    "outputId": "4840e49f-9296-4d06-b85c-eb61931a8679"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[  101,  2004,  1037,  2450,  2017,  2323,  2025, 17612,  2055,  9344,\n",
       "          2039,  2115,  2160,  1998,  2004,  1037,  2158,  2017,  2323,  2467,\n",
       "          2202,  1996, 11669,  2041,   102,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_df.tweet[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3iGmhkf-1Ee"
   },
   "source": [
    "**Define dataset class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rvi0TBAEIAEu"
   },
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class TextDataset(Dataset):\n",
    "  def __init__(self, df):\n",
    "    self.labels = df['label'].values\n",
    "    self.text = df['tweet'].values\n",
    "    self.labels = F.one_hot(torch.from_numpy(self.labels).long()) # convert labels to one-hot\n",
    "  def __len__(self):\n",
    "    return len(self.labels)\n",
    "  def __getitem__(self, idx):\n",
    "    label = self.labels[idx]\n",
    "    text = self.text[idx]\n",
    "    return text, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jVnlZL2P-8d-"
   },
   "source": [
    "### Train-Valid-Test splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FlelISU8y3EQ",
    "outputId": "5010c2f8-c4d3-47f5-8e1a-86552e6f3a58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training len:\t valid len:\t test len:\n",
      "18210 \t\t 2428 \t\t 3642\n"
     ]
    }
   ],
   "source": [
    "total_len = len(bert_df)\n",
    "training_ratio = 0.75\n",
    "validation_ratio = 0.10\n",
    "test_ratio = 0.15\n",
    "train_len, valid_len, test_len = training_ratio*total_len, validation_ratio*total_len, test_ratio*total_len\n",
    "train_len, valid_len, test_len = int(train_len), int(valid_len), int(test_len)\n",
    "print('training len:\\t valid len:\\t test len:')\n",
    "print(train_len, '\\t\\t', valid_len, '\\t\\t',test_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q0G1AZAlyD5P"
   },
   "outputs": [],
   "source": [
    "TotalData = TextDataset(bert_df)\n",
    "train, valid = random_split(TotalData, [train_len,total_len-train_len], \n",
    "                            generator=torch.Generator().manual_seed(seed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1HWBltuGzl9L"
   },
   "outputs": [],
   "source": [
    "valid, test = random_split(valid, [valid_len,len(valid)-valid_len], \n",
    "                           generator=torch.Generator().manual_seed(seed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "exlvG0ql0qX9",
    "outputId": "95c69a42-3f47-4ba6-a52e-f2ff2f49cb2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training len:\t valid len:\t test len:\n",
      "18210 \t\t 2428 \t\t 3642\n"
     ]
    }
   ],
   "source": [
    "print('training len:\\t valid len:\\t test len:')\n",
    "print(len(train), '\\t\\t', len(valid), '\\t\\t',len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "miajo5oA1hIJ"
   },
   "source": [
    "## Make Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121,
     "referenced_widgets": [
      "415aea8451e143ff889f292a7501dd60",
      "988436e335da47d3824f58c7882f892b",
      "09678b9cf8834563a8fd4d600d3b9628",
      "6f9f04c6e2424530a320a4092e6d6d60",
      "a573b1d982324fe384781c488f116948",
      "cbee64897eb746fd8adda3251eef2fa2",
      "6fc13955f34f4e3dbe444ebb34736486",
      "7f56c31eb17144779c4749385eb7495d",
      "b39e096b8f514f77b9ce8823640f2c24",
      "5a129e2438e247feb31c4624bcc69785",
      "fdb56fb0992d43298cd0e359d48e25c0"
     ]
    },
    "id": "EzXkAO_806xA",
    "outputId": "164ee770-c6d4-4109-8b4c-68403fca8818"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "415aea8451e143ff889f292a7501dd60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "bert = BertModel.from_pretrained('bert-base-uncased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "g2CQyqbDNJGR"
   },
   "outputs": [],
   "source": [
    "num_classes = len(df.label.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B0i5p6YKM4yX"
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.nn.utils.rnn import pack_padded_sequence\n",
    "class BERTLSTMnn(nn.Module):\n",
    "  def __init__(self, bert, seq_len, n_layers):\n",
    "    super().__init__()\n",
    "    self.bert = bert\n",
    "    embedding_size = bert.config.to_dict()['hidden_size']\n",
    "    self.lstm = nn.LSTM(input_size=embedding_size, \n",
    "                        hidden_size=seq_len, \n",
    "                        num_layers=n_layers,\n",
    "                        batch_first=True,\n",
    "                        dropout=0.2,\n",
    "                        bidirectional=True)\n",
    "    self.dense = nn.Linear(seq_len*2, num_classes)\n",
    "    self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "  def forward(self, sentence):\n",
    "    with torch.no_grad():\n",
    "      embedded = self.bert(input_ids=sentence['input_ids'].squeeze(1),\n",
    "                           attention_mask=sentence['attention_mask'])\n",
    "      \n",
    "    _, (HiddenStates, CellStates) = self.lstm(embedded['last_hidden_state'])\n",
    "\n",
    "    output = self.dense(torch.cat((HiddenStates[0], HiddenStates[1]), dim = 1))\n",
    "    output = self.softmax(output)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qOX1UGuL-B_8",
    "outputId": "127ec383-5808-4789-ddfa-9dd3647c5c69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 40])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_df.tweet[0]['input_ids'].size()  #need squeeze!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jHY3GW9j_T70"
   },
   "source": [
    "**Function for evaluating accuracy and loss of input**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "x12ApRaDEsJg"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, data, batch_size, criterion):\n",
    "  dataloader = DataLoader(data, batch_size, shuffle=True)\n",
    "  error = 0\n",
    "  loss = 0\n",
    "  model.eval()\n",
    "  for batch in dataloader:\n",
    "    batch[0], batch[1] = batch[0].to(device), batch[1].to(device)\n",
    "    with torch.no_grad():\n",
    "      prediction = model(batch[0]).to(device) \n",
    "    loss += criterion(prediction, batch[1].float()).item()\n",
    "    prediction = prediction.argmax(axis=1).cpu().numpy()\n",
    "    target = batch[1].argmax(axis=1).cpu().numpy()\n",
    "    error += np.sum(np.abs(target - prediction))\n",
    "\n",
    "  accuracy = 1 - error/len(data)\n",
    "  loss = loss/len(dataloader)\n",
    "  return accuracy, loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6vOFN9Dk_X-h"
   },
   "source": [
    "**Function for doing a single epoch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "KkpE-ooyVHzf"
   },
   "outputs": [],
   "source": [
    "def fit(model, train_dataset, batch_size, optimizer, criterion):\n",
    "  data_len = len(train_dataset)\n",
    "  train_loss = 0\n",
    "  error = 0\n",
    "  model.train()\n",
    "  train_dataloader = DataLoader(train_dataset, batch_size, shuffle=True) # shuffle for fitting\n",
    "  for i, batch in enumerate(train_dataloader):\n",
    "    print(f'\\rbatch {i+1}/{len(train_dataloader)} - training loss = {train_loss / (i+1)}', end='')\n",
    "    optimizer.zero_grad()\n",
    "    predictions = model(batch[0].to(device)).squeeze(1)\n",
    "    loss = criterion(predictions, batch[1].to(device).float())\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    train_loss += loss.item()\n",
    "    predictions = predictions.argmax(axis=1).cpu().numpy()\n",
    "    targets = batch[1].argmax(axis=1).cpu().numpy()\n",
    "    error += np.sum(np.abs(targets - predictions))\n",
    "  return  1 - error/len(train_dataset), train_loss/len(train_dataloader) # return accuracy and loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CA9tFShK_blb"
   },
   "source": [
    "**Function for predicting output**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "7oIljekI5vtE"
   },
   "outputs": [],
   "source": [
    "def predict(model, input, batch_size):\n",
    "  dataloader = DataLoader(input, batch_size, shuffle=False) # don't shuffle for prediction\n",
    "  output = np.empty((0,num_classes))\n",
    "  targets = np.empty((0,num_classes))\n",
    "  for batch in dataloader:\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "      prediction = model(batch[0].to(device)).cpu()\n",
    "    prediction = prediction.detach().numpy()\n",
    "    output = np.vstack([output, prediction])\n",
    "    target = batch[1].cpu().numpy()\n",
    "    targets = np.vstack([targets, target])\n",
    "  return output, targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_wFFVslD6ljj"
   },
   "source": [
    "### Create Model Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4ise4dNwTtOx"
   },
   "outputs": [],
   "source": [
    "seq_len = 32\n",
    "layers = 16\n",
    "model = BERTLSTMnn(bert, seq_len, layers)\n",
    "for name, param in model.named_parameters():\n",
    "    if name.startswith('bert'):\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RhNVen5KUrJ_"
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "model = model.to(device)\n",
    "criterion = criterion.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hq2RGHChh5xx"
   },
   "source": [
    "## Start Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 981
    },
    "id": "QV8V7wKR2ujQ",
    "outputId": "0d11bd23-53cd-43e0-d5b3-afeaff5da69c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1/12\n",
      "batch 570/570 - training loss = 0.7271243971690797 training accuracy = 0.8200988467874795  validation loss = 0.6902669417230707  validation accuracy 0.8496705107084019\n",
      "epoch 2/12\n",
      "batch 570/570 - training loss = 0.6794288579832044 training accuracy = 0.8611751784733663  validation loss = 0.6859047397186882  validation accuracy 0.8612026359143328\n",
      "epoch 3/12\n",
      "batch 570/570 - training loss = 0.666375670307561 training accuracy = 0.8751784733662823  validation loss = 0.661907188986477  validation accuracy 0.8826194398682042\n",
      "epoch 4/12\n",
      "batch 570/570 - training loss = 0.6585186342398326 training accuracy = 0.8827567270730368  validation loss = 0.6586733460426331  validation accuracy 0.8838550247116969\n",
      "epoch 5/12\n",
      "batch 570/570 - training loss = 0.6525679301797298 training accuracy = 0.8891268533772653  validation loss = 0.6490756100729892  validation accuracy 0.8920922570016474\n",
      "epoch 6/12\n",
      "batch 570/570 - training loss = 0.648839060883773 training accuracy = 0.8912136188907194  validation loss = 0.6504602667532469  validation accuracy 0.8875617792421746\n",
      "epoch 7/12\n",
      "batch 570/570 - training loss = 0.64524137367282 training accuracy = 0.8952224052718287  validation loss = 0.6458187189541365  validation accuracy 0.8953871499176277\n",
      "epoch 8/12\n",
      "batch 570/570 - training loss = 0.644249750752198 training accuracy = 0.8971444261394838  validation loss = 0.6582084525572626  validation accuracy 0.8879736408566722\n",
      "epoch 9/12\n",
      "batch 570/570 - training loss = 0.6426145257657034 training accuracy = 0.8985172981878089  validation loss = 0.6491558089068061  validation accuracy 0.8966227347611203\n",
      "epoch 10/12\n",
      "batch 570/570 - training loss = 0.6424619465543513 training accuracy = 0.8971993410214169  validation loss = 0.6506411817513014  validation accuracy 0.8929159802306426\n",
      "epoch 11/12\n",
      "batch 570/570 - training loss = 0.6408983233727907 training accuracy = 0.900219659527732  validation loss = 0.6445988063749514  validation accuracy 0.8962108731466227\n",
      "epoch 12/12\n",
      "batch 570/570 - training loss = 0.637720557472162 training accuracy = 0.9037891268533773  validation loss = 0.6460864638027392  validation accuracy 0.8945634266886326\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "batch_size = 32\n",
    "epochs = 12\n",
    "accuracy_history = np.zeros((epochs, 2))\n",
    "loss_history = np.zeros((epochs, 2))\n",
    "for epoch in range(epochs):\n",
    "  print(f'epoch {epoch+1}/{epochs}')\n",
    "  accuracy_history[epoch,0] , loss_history[epoch,0] = fit(model, train, batch_size, optimizer, criterion)\n",
    "  accuracy_history[epoch,1] , loss_history[epoch,1] = evaluate(model, valid, batch_size, criterion)\n",
    "  print(' training accuracy =', accuracy_history[epoch,0], \n",
    "        ' validation loss ='  , loss_history[epoch,1], \n",
    "        ' validation accuracy', accuracy_history[epoch,1])\n",
    "plt.plot(accuracy_history[:,0])\n",
    "plt.plot(accuracy_history[:,1])\n",
    "plt.title('accuracy per epoch')\n",
    "plt.legend(['training', 'validation'])\n",
    "plt.show()\n",
    "plt.figure()\n",
    "plt.plot(loss_history[:,0])\n",
    "plt.plot(loss_history[:,1])\n",
    "plt.title('loss per epoch')\n",
    "plt.legend(['training', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tOwSpWFf_kON"
   },
   "source": [
    "## Display Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "lyhrc7H6Prld",
    "outputId": "5be29338-151a-4dc2-f8b1-02fca86a0fdb",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f42da432350>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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taynX/mg+p/zNKv7+a0u59otHsHxRFy68cgmf/PT7zPivjv/HNIfe3oLV7E3OEfGViFgXEXcDpwMXRcTFWRz75+z5zu2Cdtgxm1mxqJyVSyqorSnhmUd6ccKZ6/MdVrvYv18tw47cAkDX7vUMPnQr1VVl29+PgGcf7cUp565t9lgfPXYz+/erbbNYC1Fpp6C8cz0lpUFFl3o+3FxKbY1YvqgLAK++0ItPnLk6z1G2kmKezFTSyF0XoBLolLxuUo53bheM/Q+sYdWK8u3r1VVl9Olfk8eI8mPl0nLefrMLh4/c8eSCN1/qRu++tQwcum3HdkvK+crpw/nGZw7ljZe65SPUgrD6vQoeumcA9/3xFe7/00w2byzl2Wn7U1IaDPvYBwB8Yuxq+vTf1syRrL001ez9QRPvBXBqawSQ3PF9GUBnurbGIa2FtmwqYeKlh3DFjcvptt+OK9tP/7Y3YxrV+ioPqOGXM+fSo7KOBa934YaLhzD5mb/stE9adO9Ry/GnreHiU4/lgw2lXHfbW5zyN9VMmnAYl133LmXlwasv9KK+SL6aYmj2NnWT8yntEUBETAYmA/RQZd6/0tUry+g7YMdf5z79a3Zq+hW72hqYeOkhnPqZtXzi7B3N/bpaeGFaT25//K3tZeUVQXlF5vrfsCO3MOCQbSx/p4LhR21p97jz7egT1/Hess6sX5P5XfnTjEpGjNzA04/25Z8//3EARn5iHQMPKYLvJmgYutahZTOxQarMn92VgUO20W/wVjqV1TPmnHW8OKNnvsNqFxFwy9cPYvCwrXz28lU7vffqc/sx+NCt9B2w4xLAutWl1CV9H1WLy1n+bjkHHpTOZt2qqgoOP3ojFZ3rgODoE9az9O2u9KzMfB9l5fV87h+XM+2BA/MbaGspgmt+WY3wSJP6OnHHtwZy8/3vUFIKMx6sZPFbxd/TCzDn5W489V+VDPnoFr78ycMAuPjaFYw+bSN/fGTnJi/AGy92577vHUinTple8q9NWkaP3pls+LOJ/Xn6t73ZuqWELxw7grEXrOHCb6xs98/UXua/th/PP74/P/7t69TVwdtzu/PYf/bji1ctYfQpaylR8IcHDuS1F4vjD2kxNHsVbfQAzsZ3bgPvAddHxF5vXoRMs/c4ndYm8RSD6Stm5zuEgnfWsJPyHUJBe3Hz71lfV92iNmvF4MExaMJVWW37zje+/so+TGzQLrIZ3iYy09gPjYgbJR0EHBgRLze1317u3DazYlAENb9srvndCZwANCSzjWRmTTWzFFJkvxSybK75HRcRIyX9D0BErJVU3txOZlbEiqC3N5vkV5PMkR8AkvrSasOazawjKvRaXTayafbeBvwGOEDSTWSms7q5TaMys8KWhltdIuJXkl4BTiMzaeC5ETGvzSMzs8LUAa7nZSOb3t6DgM3A7xqXRcSStgzMzApYGpIf8Ad2PMioMzAEmE/m2ZlmlkIqgqv+2TR7P954PZnR5SttFpGZWTvIeXhbRLwq6bi2CMbMOog0NHsl/VOj1RJgJLCizSIys8KWlg4PYL9Gr2vJXAN8qG3CMbMOodiTX3Jz834R8Y12isfMOoJiTn6SOkVErSRPk2Fm24ni7+19mcz1vdmSHgV+DWxqeDMiHm7j2MysEKXoml9nYDWZZ3Y03O8XgJOfWVoVefI7IOnpfZMdSa9BEXx0M9tnRZABmkp+pUB3dk56DYrgo5vZvir2Zm9VRNzYbpGYWcdR5Mmv489WaGatL4q/t9dPEjKzPSvmml9ErGnPQMys4yiGa35+aLmZ5a6VZnKWNEXS+5LebFRWKekJSQuSn72Tckm6TdJCSa8nM0w17HNRsv0CSRdl8xGc/MwsN9kmvuxqhz8Hxu5Sdg3wVEQMA55K1gHOAoYly2XAXZBJlsD1wHHAaOD6hoTZFCc/M8uJaL1HV0bEs8Cul9jOAe5NXt8LnNuo/L7IeBHoJak/cCbwRESsiYi1wBPsnlB3k/N8fmZmOVzz6yNpVqP1yRExuZl9+kVEVfJ6JdAveT0QWNpou2VJ2d7Km+TkZ2a5yz75VUfEqH0+TURIbdO94mavmeWubR9d+V7SnCX5+X5SvhwY3Gi7QUnZ3sqb5ORnZrnJ8npfC+prjwINPbYXAY80Kv9i0ut7PLA+aR5PB86Q1Dvp6DgjKWuSm71mlrtWaohKegAYQ+ba4DIyvbaTgKmSvgQsBs5LNp8GnA0sJPM43Yshc0+ypInAzGS7G7O5T9nJz8xy1lrD2yLigr28tdsIs4gIYNxejjMFmJLLuZ38OpCxQ/zQvObUHT883yEUtJhV0SrHKYYRHk5+ZpablnVmFAwnPzPLnZOfmaVNwwiPjs7Jz8xypvqOn/2c/MwsN77mZ2Zp5WavmaWTk5+ZpZFrfmaWTk5+ZpY6KXh6m5nZbnyfn5mlV3T87OfkZ2Y5c83PzNLHNzmbWVq5w8PMUsnJz8zSJ3CHh5mlkzs8zCydnPzMLG18k7OZpVOEJzM1s5Tq+LnPyc/Mcudmr5mlTwBu9ppZKnX83OfkZ2a5c7PXzFLJvb1mlj6e1cXM0ihzk3PHz34l+Q7AzDqg+iyXZkhaJOkNSbMlzUrKKiU9IWlB8rN3Ui5Jt0laKOl1SSNb8hGc/MwsZ4rIasnSKRFxdESMStavAZ6KiGHAU8k6wFnAsGS5DLirJZ/Bzd49GDVmA1dMXEFpSfDYA5VMvb1fvkMqCN32q2XCd9/lkOFbiIBbrx7C0ne6cN3tC+k3cCvvLa/g5nGH8sGG9Pxa/eK2X7NlSxn19aKuvoRx3/p/nHzcIi7829kcNGAdX/3XT/HWO30A6FRax4RL/8zwodXUh7jz3tG8Pq9/nj/BPmj7a37nAGOS1/cCzwDfTMrvi4gAXpTUS1L/iKjal5O02W+ppMHAfUA/Ml/V5Ij4UVudr7WUlATjbl7OtecPpbqqjB9PW8CL03uyZEHnfIeWd1dcv5hX/tiTm74yjE5l9VR0ruf8cSuY/UIPpt49gPOuWMF5X65iyncH5zvUdvWN74xlw8Ydvx+Llvbi27ecwoRL/7TTdmef+hYAl33zXHr12MJN33yS8f/yKSLUrvG2XE5je/s0NGcTkyNi8k4HgxmSAvhJ8l6/RgltJZkcAjAQWNpo32VJ2T4lv7Zs9tYCX4+IEcDxwDhJI9rwfK3isGM2s2JROSuXVFBbU8Izj/TihDPX5zusvOu6Xy0fH72Rx/+zLwC1NSVs2tiJE05fx5MPZWo2Tz7UhxPPWJvPMAvCkhW9WFbVc7fygwetZ/acTE1v3YYubNpczvCh1e0dXuuIyG6B6ogY1WiZvMuRPhERI8k0acdJOnnn00Sb1TPbLPlFRFVEvJq83gjMI5OlC9r+B9awakX59vXqqjL69K/JY0SF4cBBW1m/poyvf+9dbv/9m0yY9C4VXero1aeGNasy39eaVWX06pOu7ypCTLp2Bnfc9DvOPnV+k9u+vbg3Jxy7hJKSeg7su5FhQ6rpu/+mdoq0FSUPLc9mafZQEcuTn+8DvwFGA+9J6g+Q/Hw/2Xw50LhZMSgp2yftcnFG0iHAMcBL7XE+a32lnYJDj9jEnTcczPzZ3bni3xbzd1/etbWhYpjdPCdX3XAWq9d2o1ePLUy6bgZLV/Tkjb8cuMdtH39mGAcNXM+dN/2O96q7M/etA6iv72hN3kQr/ENL6gaURMTG5PUZwI3Ao8BFwKTk5yPJLo8C4yU9CBwHrN/X633QDslPUnfgIWBCRGzYw/uXkem5oTNd2zqcZq1eWUbfAdu2r/fpX0N1VVkeIyoM1VXlVK8sZ/7s7gA891glf3fFCtZVl1HZdxtrVpVT2Xcb61en67tavbYbkGnGvjDzIA77SPVek199fQl3/2L09vUffvsPe2wedwit80euH/AbSZDJRfdHxOOSZgJTJX0JWAycl2w/DTgbWAhsBi5uycnbNPlJKiOT+H4VEQ/vaZvkGsBkgB6qzHu9Yf7srgwcso1+g7eyemUZY85Zx6RxB+c7rLxbW13OqqpyBg3dwrJ3unDMietZsrALSxZ24ZOfrWbq3QP45Ger+fMTvfIdarvpXFGDBFs+LKNzRQ3HHrmCXz581F63ryivRQo+3FrGyI+voK6uhCXLO+b3pfqWP74tIt4BdvvCImI1cNoeygMY1+ITJ9qyt1fAPcC8iLilrc7T2urrxB3fGsjN979DSSnMeLCSxW+5pxfgzusP5upb36asPKhaUsEt/zwUlQTX3f42Z563iveXV3DT+EPzHWa76dXzQ274p/8GoLQ0ePqFIcx6bRAnjVrMuH94iZ49PuQ7Vz/J24squXbSGfTqsYV/v/YJIkT1mq58986/zvMn2EdBVjcwFzpFG12kkfQJ4DngDXZ8VddFxLS97dNDlXGcdkv4llBFRb5DKHh1xxf8DQV5NXPWHWzYuLxFFxp7dhsQx4+4PKttZ8y64ZVGNy8XlDar+UXE82SGAZpZsSmCnq303IpvZq3Hyc/MUqdIrvk5+ZlZzlqjtzffnPzMLEfhZq+ZpVDg5GdmKdXxW71OfmaWu2KYxt7Jz8xy5+RnZqkTAXUdv93r5GdmuXPNz8xSycnPzFIngOyf4VGwnPzMLEcB4Wt+ZpY2gTs8zCylfM3PzFLJyc/M0scTG5hZGgXgKa3MLJVc8zOz9PHwNjNLo4DwfX5mlkoe4WFmqeRrfmaWOhHu7TWzlHLNz8zSJ4i6unwH0WJOfmaWG09pZWapVQS3upTkOwAz61gCiPrIammOpLGS5ktaKOmato9+Byc/M8tNJJOZZrM0QVIpcAdwFjACuEDSiHb4BICbvWa2D1qpw2M0sDAi3gGQ9CBwDjC3NQ7eHEUBdVlLWgUsznccjfQBqvMdRAHz99O8QvuODo6Ivi05gKTHyXyubHQGPmy0PjkiJifH+VtgbERcmqxfCBwXEeNbEl+2Cqrm19J/lNYmaVZEjMp3HIXK30/zivE7ioix+Y6hNfian5nly3JgcKP1QUlZu3D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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "predictions, targets = predict(model, test, 128)\n",
    "predictions, targets = predictions.argmax(axis=1), targets.argmax(axis=1)\n",
    "cm = confusion_matrix(targets, predictions)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yNy2j1U-bsPV",
    "outputId": "4257febe-e600-4a62-d052-fd13d105b409"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       223\n",
      "           1       0.92      0.97      0.94      2840\n",
      "           2       0.80      0.90      0.84       579\n",
      "\n",
      "    accuracy                           0.90      3642\n",
      "   macro avg       0.57      0.62      0.60      3642\n",
      "weighted avg       0.84      0.90      0.87      3642\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(targets, predictions))"
   ]
  }
 ],
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