Aspect-Based_Sentiment_Analysis/[colab_1]_Aspect_Based_Term_Extraction.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCm-Qniexs2d",
        "outputId": "99c60c6c-4cb6-41b3-cb2d-dbd5065f9387"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error:\n",
            "\n",
            "\t'NoneType' object has no attribute 'groups'\n",
            "\n",
            "To report issues, please visit https://github.com/wkentaro/gdown/issues.\n"
          ]
        }
      ],
      "source": [
        "!gdown 1d7JABk4jViI-USjLsWmhGkvzi8uQIL5C\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3_qL8fu_5Ga",
        "outputId": "8bc90774-95aa-4e8e-fcee-4ae5535449eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  ./data.zip\n",
            "   creating: data/\n",
            "  inflating: __MACOSX/._data         \n",
            "  inflating: data/restaurants_train.csv  \n",
            "  inflating: __MACOSX/data/._restaurants_train.csv  \n",
            "  inflating: data/restaurants_test.csv  \n",
            "  inflating: __MACOSX/data/._restaurants_test.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip ./data.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZB6hIY1_GNV"
      },
      "source": [
        "## **Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "WXZ9e4vbWF-y",
        "outputId": "ecfedae2-ead4-40ff-b120-2bf467b80f86"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"['But', 'the', 'staff', 'was', 'so', 'horrible', 'to', 'us']\""
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.iloc[0]['Tokens']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lPikx-kTWK6i",
        "outputId": "610b53cf-773f-464a-f547-c55e0f1791f5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[0, 0, 1, 0, 0, 0, 0, 0, 0]'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.iloc[0]['Tags']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "kj_duDPGU09A"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class ABSADataset(Dataset):\n",
        "    def __init__(self, df, tokenizer): # input dataframe\n",
        "        super().__init__()\n",
        "        self.df = df\n",
        "        self.tokenizer = tokenizer\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens, tags, polarity = self.df.iloc[idx, :3].values\n",
        "        # Tokens now is the string -> need to process\n",
        "        tokens = tokens.replace(\"'\", \"\").strip(\"][\").split(\", \") # because tokens = '['But', 'I', ...]' is the string, delete ' and []\n",
        "        # Tag also string, process the same\n",
        "        tags = tags.replace(\"'\", \"\").strip(\"][\").split(\", \")\n",
        "        # Pass polarity because it don't use in this problem\n",
        "\n",
        "        bert_tokens = [] # create list save new tokens if in tokenizing it will be divided to subword\n",
        "        bert_tags = []\n",
        "\n",
        "        for i in range(len(tokens)):\n",
        "            sub_token = self.tokenizer.tokenize(tokens[i])\n",
        "            bert_tokens += sub_token\n",
        "            bert_tags += [int(tags[i])]*len(sub_token)\n",
        "            # if len(sub_token) > 1:\n",
        "            #     bert_tags.append(int(tags[i]))\n",
        "            #     bert_tags.append(int)\n",
        "            # else:\n",
        "            #     bert_tags.append(int(tags[i]))\n",
        "\n",
        "        bert_ids = self.tokenizer.convert_tokens_to_ids(bert_tokens)\n",
        "\n",
        "        bert_ids = torch.tensor(bert_ids)\n",
        "        bert_label = torch.tensor(bert_tags)\n",
        "\n",
        "        return bert_tokens, bert_ids, bert_label\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
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            "d6eb9ee9000844558d46d6055944a618"
          ]
        },
        "id": "uyMN_xPbYcMn",
        "outputId": "07a4d92b-5df7-45f3-8176-8a7fc4d7c122"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "8c4238c4ce104fc59d669eae6ecf097f",
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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          "output_type": "display_data"
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        {
          "data": {
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              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40648776e6d04f0eb51c3e10be242cce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c426616fa5b498884093fe4ec6e01cd",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "model_name = \"bert-base-uncased\"\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "rDhMBASlYqyd"
      },
      "outputs": [],
      "source": [
        "train_ds = ABSADataset(train_df, tokenizer)\n",
        "test_ds = ABSADataset(test_df, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zv2ZhWFVYvXe"
      },
      "outputs": [],
      "source": [
        "next(iter(train_ds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8HQLZiFh_IZL"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class ABSADataset(Dataset):\n",
        "    def __init__(self, df, tokenizer):\n",
        "        self.df = df\n",
        "        self.tokenizer = tokenizer\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens, tags, pols = self.df.iloc[idx, :3].values\n",
        "\n",
        "        tokens = tokens.replace(\"'\", \"\").strip(\"][\").split(', ')\n",
        "        tags = tags.strip('][').split(', ')\n",
        "        pols = pols.strip('][').split(', ')\n",
        "\n",
        "        bert_tokens = []\n",
        "        bert_tags = []\n",
        "        bert_pols = []\n",
        "        for i in range(len(tokens)):\n",
        "            t = self.tokenizer.tokenize(tokens[i])\n",
        "            bert_tokens += t\n",
        "            bert_tags += [int(tags[i])]*len(t)\n",
        "            bert_pols += [int(pols[i])]*len(t)\n",
        "\n",
        "        bert_ids = self.tokenizer.convert_tokens_to_ids(bert_tokens)\n",
        "\n",
        "        ids_tensor = torch.tensor(bert_ids)\n",
        "        tags_tensor = torch.tensor(bert_tags)\n",
        "        pols_tensor = torch.tensor(bert_pols)\n",
        "        return bert_tokens, ids_tensor, tags_tensor, pols_tensor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2vBmzMtT_14U"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv('./data/restaurants_train.csv')\n",
        "test_df = pd.read_csv('./data/restaurants_test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VccDur9UBeZb",
        "outputId": "a042a7f7-2f52-48bb-8474-bc10b2dba02a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Tokens        ['But', 'the', 'staff', 'was', 'so', 'horrible...\n",
              "Tags                                [0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
              "Polarities                  [-1, -1, 0, -1, -1, -1, -1, -1, -1]\n",
              "Name: 0, dtype: object"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.iloc[0]"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
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        },
        "id": "RdTe7kWqAei5",
        "outputId": "336e7c07-0796-409a-a9a3-012570c0d9d5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc50b06b039a4e018d638b0f2409d5b6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e1c10e89ae84dd295df1092a7d79d87",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0b9fea6ddbc8427294621fbd927c7c07",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a93391df06b488ca443f98cd96cfa03",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "model_name = \"bert-base-uncased\"\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b67AASAB-fC",
        "outputId": "fc3aafb5-97dd-423d-aa35-b87144855ce8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['[UNK]', '[SEP]', '[PAD]', '[CLS]', '[MASK]']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.all_special_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4XGqw7jCuWZ",
        "outputId": "b179bb08-fac2-4831-8285-231cd9f1b726"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "101"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.cls_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQc7LGiHC3Tw",
        "outputId": "d225de68-ea37-4dc3-a39b-b506a2e6b7cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "102"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.sep_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eGoxRlVuAqkO"
      },
      "outputs": [],
      "source": [
        "train_ds = ABSADataset(train_df, tokenizer)\n",
        "test_ds = ABSADataset(test_df, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqAItm7yxerF",
        "outputId": "77e9440f-c402-469c-819a-88ac579bbcc5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3602"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6gGCUp-BMHi",
        "outputId": "123a25f3-41b2-4f15-f226-18ac56150a68"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(['but', 'the', 'staff', 'was', 'so', 'horrible', 'to', 'us'],\n",
              " tensor([2021, 1996, 3095, 2001, 2061, 9202, 2000, 2149]),\n",
              " tensor([0, 0, 1, 0, 0, 0, 0, 0]),\n",
              " tensor([-1, -1,  0, -1, -1, -1, -1, -1]))"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "next(iter(train_ds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "fya8wAmRC7Ge"
      },
      "outputs": [],
      "source": [
        "from torch.nn.utils.rnn import pad_sequence\n",
        "# Input for BERT :\n",
        "# Ids\n",
        "# Mask\n",
        "def padding(samples):\n",
        "    ids_tensors = [s[1] for s in samples]\n",
        "    ids_tensors = pad_sequence(ids_tensors, batch_first=True)\n",
        "\n",
        "    tags_tensors = [s[2] for s in samples]\n",
        "    tags_tensors = pad_sequence(tags_tensors, batch_first=True)\n",
        "\n",
        "    pols_tensors = [s[3] for s in samples]\n",
        "    pols_tensors = pad_sequence(pols_tensors, batch_first=True)\n",
        "\n",
        "    masks_tensors = torch.zeros(ids_tensors.shape, dtype=torch.long)\n",
        "    masks_tensors = masks_tensors.masked_fill(ids_tensors != 0, 1)\n",
        "\n",
        "    return ids_tensors, tags_tensors, pols_tensors, masks_tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "N1eFREOMDYmg"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 32\n",
        "train_loader = DataLoader(\n",
        "    train_ds, batch_size=batch_size, shuffle=True, collate_fn=padding\n",
        ")\n",
        "test_loader = DataLoader(\n",
        "    test_ds, batch_size=batch_size, shuffle=True, collate_fn=padding\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYFrHjjpVaV1",
        "outputId": "d2cbb40a-3cc0-4991-fab4-2324dde34d47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([32, 51])"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "next(iter(train_loader))[0].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5KqNndiELIG"
      },
      "source": [
        "## **Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "YSePW5i2ENJU"
      },
      "outputs": [],
      "source": [
        "from transformers import BertModel\n",
        "\n",
        "class ABTEBert(torch.nn.Module):\n",
        "    def __init__(self, model_name):\n",
        "        super(ABTEBert, self).__init__()\n",
        "        self.bert = BertModel.from_pretrained(model_name)\n",
        "        self.linear = torch.nn.Linear(self.bert.config.hidden_size, 3)\n",
        "        self.loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "    def forward(self, ids_tensors, masks_tensors, tags_tensors):\n",
        "        bert_outputs= self.bert(\n",
        "            input_ids=ids_tensors, attention_mask=masks_tensors, return_dict=False\n",
        "            )\n",
        "        bert_outputs = bert_outputs[0]\n",
        "\n",
        "        linear_outputs = self.linear(bert_outputs)\n",
        "        if tags_tensors is not None:\n",
        "            tags_tensors = tags_tensors.view(-1)\n",
        "            linear_outputs_ = linear_outputs.view(-1,3)\n",
        "            loss = self.loss_fn(linear_outputs_, tags_tensors)\n",
        "            return loss, linear_outputs\n",
        "        else:\n",
        "            return linear_outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "R6H-d06XEzRT"
      },
      "outputs": [],
      "source": [
        "model = ABTEBert(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuFp9jxhE2Av",
        "outputId": "87ad08ed-8906-4944-fd1a-feff3077e09e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3YzgUr1FBlo",
        "outputId": "560c10bc-1059-4a24-8095-e8a9148c00bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ABTEBert(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 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",
              "  (linear): Linear(in_features=768, out_features=3, bias=True)\n",
              "  (loss_fn): CrossEntropyLoss()\n",
              ")"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "7YVK29c_FheI"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def train_epoch(model, optimizer, train_loader, device):\n",
        "    losses = []\n",
        "    for batch in (train_loader):\n",
        "        #ids_tensors, tags_tensors, _, masks_tensors = batch\n",
        "        ids_tensors, masks_tensors, tags_tensors = batch\n",
        "\n",
        "        #return ids_tensors, mask_tensors, tags_tensors\n",
        "        ids_tensors = ids_tensors.to(device)\n",
        "        tags_tensors = tags_tensors.to(device)\n",
        "        masks_tensors = masks_tensors.to(device)\n",
        "\n",
        "        loss, _ = model(\n",
        "            ids_tensors=ids_tensors,\n",
        "            masks_tensors=masks_tensors,\n",
        "            tags_tensors=tags_tensors\n",
        "        )\n",
        "        losses.append(loss.item())\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "    return sum(losses)/len(losses)\n",
        "\n",
        "def evaluate_epoch(model, valid_loader, device):\n",
        "    losses = []\n",
        "\n",
        "    preds, labels = [], []\n",
        "    with torch.no_grad():\n",
        "        for batch in (valid_loader):\n",
        "            # ids_tensors, tags_tensors, _, masks_tensors = batch\n",
        "            ids_tensors, masks_tensors, tags_tensors = batch\n",
        "\n",
        "            ids_tensors = ids_tensors.to(device)\n",
        "            tags_tensors = tags_tensors.to(device)\n",
        "            masks_tensors = masks_tensors.to(device)\n",
        "\n",
        "            loss, outputs = model(\n",
        "                ids_tensors=ids_tensors,\n",
        "                masks_tensors=masks_tensors,\n",
        "                tags_tensors=tags_tensors\n",
        "            )\n",
        "            losses.append(loss.item())\n",
        "\n",
        "            #_, p = torch.max(outputs, dim=2)\n",
        "            _, p = torch.max(outputs, dim=1)\n",
        "            preds += list([int(j) for i in [p] for j in i ])\n",
        "            labels += list([int(j) for i in tags_tensors for j in i ])\n",
        "\n",
        "    acc = np.mean(np.array(preds) == np.array(labels))\n",
        "    return sum(losses)/len(losses), acc\n",
        "\n",
        "def train(model, model_name, save_model, optimizer, train_loader, valid_loader, num_epochs, device):\n",
        "    train_losses = []\n",
        "    eval_accs, eval_losses = [], []\n",
        "    best_loss_eval = 100\n",
        "    times = []\n",
        "    for epoch in range(1, num_epochs+1):\n",
        "        epoch_start_time = time.time()\n",
        "        # Training\n",
        "        train_loss = train_epoch(model, optimizer, train_loader, device)\n",
        "        train_losses.append(train_loss)\n",
        "\n",
        "        # Evaluation\n",
        "        eval_loss, eval_acc = evaluate_epoch(model, valid_loader, device)\n",
        "        eval_accs.append(eval_acc)\n",
        "        eval_losses.append(eval_loss)\n",
        "\n",
        "        # Save best model\n",
        "        if eval_loss < best_loss_eval:\n",
        "            torch.save(model.state_dict(), save_model + f'/{model_name}.pt')\n",
        "\n",
        "        times.append(time.time() - epoch_start_time)\n",
        "        # Print loss, acc end epoch\n",
        "        print(\"-\" * 59)\n",
        "        print(\n",
        "            \"| End of epoch {:3d} | Time: {:5.2f}s | Train Loss {:8.3f} \"\n",
        "            \"| Valid Accuracy {:8.3f} | Valid Loss {:8.3f} \".format(\n",
        "                epoch, time.time() - epoch_start_time, train_loss, eval_acc, eval_loss\n",
        "            )\n",
        "        )\n",
        "        print(\"-\" * 59)\n",
        "\n",
        "    # Load best model\n",
        "    model.load_state_dict(torch.load(save_model + f'/{model_name}.pt'))\n",
        "    model.eval()\n",
        "    metrics = {\n",
        "        'train_loss': train_losses,\n",
        "        'valid_accuracy': eval_accs,\n",
        "        'valid_loss': eval_losses,\n",
        "        'time': times\n",
        "    }\n",
        "    return model, metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "_1gnIKSKFTcy"
      },
      "outputs": [],
      "source": [
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-Snf1ujGegy",
        "outputId": "da22aca9-52d2-46b3-f064-e21dd1cb453c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.2707750349577549"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "loss = train_epoch(model, optimizer, train_loader, device)\n",
        "loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6NLnLFHMvFL",
        "outputId": "aaf1ee5f-71ec-44d1-8e9c-0c0e734e7de5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.22752500857625688, 0.9122709274329944)"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "loss, acc = evaluate_epoch(model, test_loader, device)\n",
        "loss, acc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cTa_h-LeOQKs"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "ZfzkpCoxPBTK"
      },
      "outputs": [],
      "source": [
        "!mkdir \"./model\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXUtw_QRNzxm",
        "outputId": "5fc938f2-1961-4b05-f90d-da21a07b515d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-----------------------------------------------------------\n",
            "| End of epoch   1 | Time: 41.92s | Train Loss    0.253 | Valid Accuracy    0.915 | Valid Loss    0.220 \n",
            "-----------------------------------------------------------\n",
            "-----------------------------------------------------------\n",
            "| End of epoch   2 | Time: 41.17s | Train Loss    0.153 | Valid Accuracy    0.918 | Valid Loss    0.211 \n",
            "-----------------------------------------------------------\n",
            "-----------------------------------------------------------\n",
            "| End of epoch   3 | Time: 40.32s | Train Loss    0.096 | Valid Accuracy    0.921 | Valid Loss    0.239 \n",
            "-----------------------------------------------------------\n",
            "-----------------------------------------------------------\n",
            "| End of epoch   4 | Time: 40.29s | Train Loss    0.051 | Valid Accuracy    0.916 | Valid Loss    0.274 \n",
            "-----------------------------------------------------------\n",
            "-----------------------------------------------------------\n",
            "| End of epoch   5 | Time: 40.07s | Train Loss    0.025 | Valid Accuracy    0.923 | Valid Loss    0.336 \n",
            "-----------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "save_model = \"./model\"\n",
        "model = ABTEBert(model_name)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)\n",
        "num_epochs = 5\n",
        "best_model, metrics = train(\n",
        "    model, model_name, save_model, optimizer, train_loader, test_loader, num_epochs, device\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zwdUW4UOSZM"
      },
      "source": [
        "## **Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "Tjz6fmp7OVAV"
      },
      "outputs": [],
      "source": [
        "def predict(best_model, sentence, device):\n",
        "    word_pieces = list(tokenizer.tokenize(sentence))\n",
        "    input_ids = tokenizer.convert_tokens_to_ids(word_pieces)\n",
        "    input_tensor = torch.tensor([input_ids]).to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_tensor, None, None)\n",
        "        _, predictions = torch.max(outputs, dim=2)\n",
        "\n",
        "    predictions = predictions[0].tolist()\n",
        "    return word_pieces, predictions, outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89mp_T2dZYcY",
        "outputId": "ddceeae9-aa8d-413e-f382-ee16e099f216"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(['the', 'bread', 'is', 'top', 'notch', 'as', 'well'],\n",
              " [0, 1, 0, 0, 0, 0, 0],\n",
              " tensor([[[ 6.1280, -2.5562, -3.6860],\n",
              "          [-1.6620,  3.9972, -2.8892],\n",
              "          [ 4.8778, -1.3735, -3.3773],\n",
              "          [ 6.1778, -3.3957, -2.6757],\n",
              "          [ 6.0563, -3.2639, -2.9226],\n",
              "          [ 5.8376, -2.9415, -3.2173],\n",
              "          [ 5.9651, -3.3867, -2.7176]]], device='cuda:0'))"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentence = \" \".join(test_df.iloc[0][\"Tokens\"].replace(\"'\", \"\").strip(\"][\").split(', '))\n",
        "predict(best_model, sentence, device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QnjfWq34iYIl"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
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            "grid_auto_columns": null,
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            "grid_auto_rows": null,
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