parsbert-fine-tune.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YHsrJinXYAaj"
      },
      "outputs": [],
      "source": [
        "!pip install transformers datasets tqdm torchinfo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "Teip3fVnYTJ3"
      },
      "outputs": [],
      "source": [
        "import torch \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "from transformers import pipeline\n",
        "from transformers import AutoTokenizer\n",
        "from datasets import load_dataset\n",
        "from torchinfo import summary\n",
        "\n",
        "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, roc_auc_score\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "6sPfoHl8Y83j"
      },
      "outputs": [],
      "source": [
        "df_ = pd.read_csv('cleaned-text-version-5.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "yZW59vpcZ0Dy"
      },
      "outputs": [],
      "source": [
        "df = df_[['text', 'label']].copy()"
      ]
    },
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        "outputId": "e3741651-25d1-4f37-e76b-4c8994d9e8a8"
      },
      "outputs": [
        {
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              "                                                text  label\n",
              "0  نماینده مجلس خطاب به قربانی اسیدپاشی ازدست مجر...      0\n",
              "1  آرره دیگه بعدش گفتم استاد دارید مسخره میکنید م...      1\n",
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              "4  در گذشته زندگی می کنی یا حال اگر در گذشته زندگ...      1"
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['label'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "calSKiuIgH4q"
      },
      "outputs": [],
      "source": [
        "train_data, test_data = train_test_split(df, test_size=0.25, random_state=42, shuffle=True)\n",
        "val_data, test_data = train_test_split(test_data, test_size=0.5, random_state=42, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "LVYUJihYlXin"
      },
      "outputs": [],
      "source": [
        "df1 = train_data[['text', 'label']]\n",
        "df1.columns = ['sentence', 'label']\n",
        "df1.to_csv('train_data.csv', index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "ErKFpln0aWLd"
      },
      "outputs": [],
      "source": [
        "df2 = val_data[['text', 'label']]\n",
        "df2.columns = ['sentence', 'label']\n",
        "df2.to_csv('val_data.csv', index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "_LysNodLggxj"
      },
      "outputs": [],
      "source": [
        "df3 = test_data[['text', 'label']]\n",
        "df3.columns = ['sentence', 'label']\n",
        "df3.to_csv('test_data.csv', index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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            "c0d1847ba36a418aa2ed336783602187",
            "e69d7ddf549041609f9f89c6662e249e",
            "1fbe27885a3843d09a0d5850fc1ccafd",
            "bcac07f9e25e43aea5a926b63caaf965",
            "6875c97fe9954b1997972317dfc865c9",
            "00badbd548eb4905b6eedf5d4ac46a54",
            "dfa0e1b592844aa6b55f41464b7e64f7",
            "8f22b3ff665b424086fcc6e65779846a",
            "0666489862624a01b30bf27f97e8da38",
            "ef64f84d66c542fc87cf3d102b53f9db"
          ]
        },
        "id": "jiZRLqIvbLx2",
        "outputId": "15b3cd45-8788-45d8-9a41-233a69f0283f"
      },
      "outputs": [],
      "source": [
        "raw_dataset = load_dataset('csv', data_files={'train':'train_data.csv', \n",
        "                                              'validation': 'val_data.csv',\n",
        "                                              'test': 'test_data.csv'\n",
        "                                              })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVNh4u90bUtN",
        "outputId": "dfd5946f-3163-44e4-e8dd-b4919a0805b7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence', 'label'],\n",
              "        num_rows: 2205\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence', 'label'],\n",
              "        num_rows: 368\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence', 'label'],\n",
              "        num_rows: 368\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "6JZ86FVCg_qf"
      },
      "outputs": [],
      "source": [
        "checkpoint = 'HooshvareLab/bert-fa-zwnj-base'\n",
        "checkpoint_sentiment = 'HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi'\n",
        "checkpoint_sentiment_binary = 'HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "suMvhAvthls3"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "GxSRFpG5hy-K"
      },
      "outputs": [],
      "source": [
        "def tokenize_fn(batch):\n",
        "  return tokenizer(batch['sentence'], truncation=True, max_length=64, padding='max_length')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "649532f12fc7422cbf353bbea99472db",
            "46f119088a96459b8e53e1956cf7d061",
            "5f40eba8577b49d1b00097245ff6f70d",
            "121f0d57812f4a6294cfb454243630be",
            "506d1b71d6d64a81a1988a030196dce9",
            "a7c5c5ea72bb444cb391c301557d508e",
            "e5b979c846514fd3a4becceed652362f",
            "5a8e1f409fcd4637b38820de683c9e64",
            "ac6c6ed40f3a470480000e3bb2c7b3ff",
            "5b62d45021a34c109ba2a27fc09b230c",
            "409b70e25af94b7692a11008af26fb2b",
            "941687b49a404d67894629f5270d5837",
            "28c5912b6fdf44f8a4913dd57f4121b2",
            "e9b51b1882cc4849b3d1a58f9255b2c4",
            "b888be2ad0ae4ba88aa95431b4fb24b6",
            "174b26b8b11940b4b3728666b58cd6a1",
            "a771e64e9dbd45cb982dca8ece780177",
            "9e253d8605cc409dac2b3dcc4c372377",
            "768a57d683484bf98fb09144f0108d9c",
            "6a4fd0b7073e4817a00e7e914acf6e3e",
            "d328c174d3c44008bbb1448d4f40f400",
            "8f57daa825494acc9eab39e5c47622ad",
            "69455d3735a24f5bb9d08c86cab87d77",
            "3020d2e63347471fa5bdb2440e2f49ef",
            "2d4d531eea9a4733bacccc3e6c360244",
            "a2408d60678845bb85ed88dd58cbeb59",
            "9f0e3a0be860471baa4186f9d551f5d1",
            "ddee2386f6aa49f7aee148cc98a7453c",
            "359a0c289e2945b68aa75382fb72166f",
            "74327b072b854769800948fe7594db56",
            "8d0cdcc82c7f4d10aeb95a238f26843c",
            "ff6e2dded3af4d23abb9b6ad4dc46199",
            "819e298ae02e4a6795579a35fda035de"
          ]
        },
        "id": "IJlcW8MgiC27",
        "outputId": "2e653473-f61e-4736-c1a3-e0e7dba9bb32"
      },
      "outputs": [],
      "source": [
        "tokenized_datasets = raw_dataset.map(tokenize_fn, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "mZTBcDlJiMMX"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "U1xlz6DseRSc"
      },
      "outputs": [],
      "source": [
        "config = AutoConfig.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSn89hbzglGt",
        "outputId": "1c92a5fc-56f2-496f-ee42-7883157ff694"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 'LABEL_0', 1: 'LABEL_1'}"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config.id2label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOF_Infsgp4W",
        "outputId": "3d96f8dd-60ca-4c35-f76b-dcb47c96d175"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'LABEL_0': 0, 'LABEL_1': 1}"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config.label2id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "_Uem08HlgwXS"
      },
      "outputs": [],
      "source": [
        "target_map = {'negative': 0, 'positive': 1}\n",
        "config.id2label = {v:k for k, v in target_map.items()}\n",
        "config.label2id = target_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "g7vqqrqHqJQO"
      },
      "outputs": [],
      "source": [
        "config.num_labels = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5LiZIuyFkmQ3"
      },
      "outputs": [],
      "source": [
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "e343c7da91d44219b2c3f7b9d4e42cba",
            "99da7350a4114abba9efd3bbfdcaf535",
            "5092364dd89643adb244cc10418dd80a",
            "0b59c2fe008e434abfec733be96470e3",
            "15a6d211137e4c4a85a3e25d21eb3ee9",
            "5b5ac406c8c445ddbe40fa7a342f6614",
            "abd1781d52a64317bf5424b65075d782",
            "108c2ac9a3264b788cf4b08d1e80439d",
            "ac300779a0284bbfbd9d9508db3a78fc",
            "de532c23b4a0487e9e8d6158594fbeaa",
            "a99a6628ebf54b429a47899dc559fbc5"
          ]
        },
        "id": "IEibDX-hiY_s",
        "outputId": "932f6cdc-9d26-4777-a3fc-b009890c3dd1"
      },
      "outputs": [],
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, config=config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LV-9HsMizez",
        "outputId": "4264a649-37fb-45ab-fb61-5534aabe97d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "================================================================================\n",
              "Layer (type:depth-idx)                                  Param #\n",
              "================================================================================\n",
              "BertForSequenceClassification                           --\n",
              "├─BertModel: 1-1                                        --\n",
              "│    └─BertEmbeddings: 2-1                              --\n",
              "│    │    └─Embedding: 3-1                              32,256,000\n",
              "│    │    └─Embedding: 3-2                              393,216\n",
              "│    │    └─Embedding: 3-3                              1,536\n",
              "│    │    └─LayerNorm: 3-4                              1,536\n",
              "│    │    └─Dropout: 3-5                                --\n",
              "│    └─BertEncoder: 2-2                                 --\n",
              "│    │    └─ModuleList: 3-6                             85,054,464\n",
              "│    └─BertPooler: 2-3                                  --\n",
              "│    │    └─Linear: 3-7                                 590,592\n",
              "│    │    └─Tanh: 3-8                                   --\n",
              "├─Dropout: 1-2                                          --\n",
              "├─Linear: 1-3                                           1,538\n",
              "================================================================================\n",
              "Total params: 118,298,882\n",
              "Trainable params: 118,298,882\n",
              "Non-trainable params: 0\n",
              "================================================================================"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summary(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "-_k9FRn-i12f"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='training_dir',\n",
        "    evaluation_strategy='epoch',\n",
        "    save_strategy='epoch',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=32,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "KGWbT2hcjiIe"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(logits_and_labels):\n",
        "  logits, labels = logits_and_labels\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  acc = np.mean(predictions == labels)\n",
        "  f1 = f1_score(labels, predictions, average='macro')\n",
        "  return {'accuracy': acc, 'f1': f1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "p0ZsqI-ekPPO"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['validation'],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "vC-XpVQImQ-l",
        "outputId": "5c5a9d4b-ef1a-4657-9f93-56bf1931331c"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnuWw3uBmU1y",
        "outputId": "cada8e9f-7782-4a18-bb62-98b10e0d7dd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "checkpoint-138\tcheckpoint-276\tcheckpoint-414\truns\n"
          ]
        }
      ],
      "source": [
        "!ls training_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bCPzq8xm5Qx"
      },
      "outputs": [],
      "source": [
        "savedmodel = pipeline('text-classification',\n",
        "                      model='training_dir/checkpoint-276',\n",
        "                      device=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "id": "pQ0PLNvZnrdG"
      },
      "outputs": [],
      "source": [
        "test_prediction = savedmodel(raw_dataset['test']['sentence'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "id": "AqWk2eoPn239"
      },
      "outputs": [],
      "source": [
        "test_prob = [d['score'] if d['label'].startswith('p') else 1 - d['score'] for d in test_prediction]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "-jVHsLcUn-Oa"
      },
      "outputs": [],
      "source": [
        "def get_label(d):\n",
        "  return 1 if d['label'] == 'positive' else 0\n",
        "\n",
        "test_pred = [get_label(d) for d in test_prediction]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GQ7abB0FCtPi"
      },
      "outputs": [],
      "source": [
        "for i in range(len(test_pred)):\n",
        "  if raw_dataset['test']['label'][i] == 1:\n",
        "    print(raw_dataset['test']['sentence'][i] , test_pred[i], raw_dataset['test']['label'][i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ya83Xe9DoTp5",
        "outputId": "8fba7708-c414-47e2-efd5-559c26ae2e31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "acc: 0.7255434782608695\n"
          ]
        }
      ],
      "source": [
        "print('acc:' , accuracy_score(raw_dataset['test']['label'], test_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-C9aH4EoXSl",
        "outputId": "b8f8ab0a-3e0c-4766-cda6-dc04a8c1d783"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "f1: 0.7187970493663703\n"
          ]
        }
      ],
      "source": [
        "print('f1:' , f1_score(raw_dataset['test']['label'], test_pred, average='macro'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "yYKwNnj4o0Vd"
      },
      "outputs": [],
      "source": [
        "def plot_cm(cm):\n",
        "  classes = ['negative','positive']\n",
        "  df_cm = pd.DataFrame(cm, index=classes, columns=classes)\n",
        "  ax = sn.heatmap(df_cm, annot=True, fmt='g')\n",
        "  ax.set_xlabel('Predicted')\n",
        "  ax.set_ylabel('Target')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Ooj6iWchptCq",
        "outputId": "48a29165-5f9d-4802-8e18-38d13d806cde"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(raw_dataset['test']['label'], test_pred, normalize='true')\n",
        "plot_cm(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzt_awm-p7ZZ",
        "outputId": "b3e351a1-ab4c-43f5-a6e4-53469c2e566a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "roc 0.7598815692079941\n"
          ]
        }
      ],
      "source": [
        "print('roc' , roc_auc_score(raw_dataset['test']['label'], test_prob))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHp9vCxRNiZF",
        "outputId": "775d2bf4-de5d-4640-da7a-641be36c0e98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "f1: 0.7187970493663703\n"
          ]
        }
      ],
      "source": [
        "print('f1:' , f1_score(1 - df3['label'], 1 - np.array(test_pred), average='macro'))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
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      "display_name": "Python 3.9.7 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
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