{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8aaedbdb",
   "metadata": {
    "papermill": {
     "duration": 0.005717,
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     "exception": false,
     "start_time": "2024-06-11T01:22:18.341727",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ae39628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:18.359092Z",
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    },
    "papermill": {
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     "end_time": "2024-06-11T01:22:35.969116",
     "exception": false,
     "start_time": "2024-06-11T01:22:18.352869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-11 01:22:27.464735: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-06-11 01:22:27.464834: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-06-11 01:22:27.562994: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from transformers import AutoTokenizer\n",
    "from datasets import Dataset\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd69f5f1",
   "metadata": {
    "papermill": {
     "duration": 0.00525,
     "end_time": "2024-06-11T01:22:35.980187",
     "exception": false,
     "start_time": "2024-06-11T01:22:35.974937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "478805d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:35.992687Z",
     "iopub.status.busy": "2024-06-11T01:22:35.992098Z",
     "iopub.status.idle": "2024-06-11T01:22:38.238822Z",
     "shell.execute_reply": "2024-06-11T01:22:38.237922Z"
    },
    "papermill": {
     "duration": 2.255884,
     "end_time": "2024-06-11T01:22:38.241687",
     "exception": false,
     "start_time": "2024-06-11T01:22:35.985803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>i seriously hate one subject to death but now ...</td>\n",
       "      <td>hate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>im so full of life i feel appalled</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>i sit here to write i start to dig out my feel...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ive been really angry with r and i feel like a...</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>i feel suspicious if there is no one outside l...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                               text  Emotion\n",
       "0           0  i seriously hate one subject to death but now ...     hate\n",
       "1           1                 im so full of life i feel appalled  neutral\n",
       "2           2  i sit here to write i start to dig out my feel...  neutral\n",
       "3           3  ive been really angry with r and i feel like a...    anger\n",
       "4           4  i feel suspicious if there is no one outside l...  neutral"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/emotion-analysis-based-on-text/emotion_sentimen_dataset.csv', encoding='utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84c05cdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:38.262176Z",
     "iopub.status.busy": "2024-06-11T01:22:38.261879Z",
     "iopub.status.idle": "2024-06-11T01:22:38.294664Z",
     "shell.execute_reply": "2024-06-11T01:22:38.293786Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 删除不需要的列\n",
    "df = df.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8073f3e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:38.309165Z",
     "iopub.status.busy": "2024-06-11T01:22:38.308884Z",
     "iopub.status.idle": "2024-06-11T01:22:39.440519Z",
     "shell.execute_reply": "2024-06-11T01:22:39.439722Z"
    },
    "papermill": {
     "duration": 1.140551,
     "end_time": "2024-06-11T01:22:39.442886",
     "exception": false,
     "start_time": "2024-06-11T01:22:38.302335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = Dataset.from_pandas(df)\n",
    "\n",
    "train_test_split = dataset.train_test_split(test_size=0.2)\n",
    "train_dataset = train_test_split['train']\n",
    "test_dataset = train_test_split['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa910c2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:39.457103Z",
     "iopub.status.busy": "2024-06-11T01:22:39.456377Z",
     "iopub.status.idle": "2024-06-11T01:22:56.052223Z",
     "shell.execute_reply": "2024-06-11T01:22:56.051520Z"
    },
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     "end_time": "2024-06-11T01:22:56.054019",
     "exception": false,
     "start_time": "2024-06-11T01:22:39.449169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db14dc447fa54873bb27bf03acb03b23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/671644 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff0b4f14c4c848c8a016b990b3803914",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/167911 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 实例化标签编码器\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# 训练和编码标签\n",
    "train_dataset = train_dataset.map(lambda examples: {'labels': label_encoder.fit_transform(examples['Emotion'])}, batched=True)\n",
    "test_dataset = test_dataset.map(lambda examples: {'labels': label_encoder.fit_transform(examples['Emotion'])}, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a71b9604",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:56.067913Z",
     "iopub.status.busy": "2024-06-11T01:22:56.067215Z",
     "iopub.status.idle": "2024-06-11T01:22:58.707925Z",
     "shell.execute_reply": "2024-06-11T01:22:58.707162Z"
    },
    "papermill": {
     "duration": 2.649853,
     "end_time": "2024-06-11T01:22:58.710140",
     "exception": false,
     "start_time": "2024-06-11T01:22:56.060287",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb43299a4ae34a54889cfeff8d675434",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43be016cd4854705a216631a669b6a0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3d8d02dfc9b4974bc951e199e8478d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68593b13d43f4b3286606a7e79ad227f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-cased\")\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1888162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:22:58.725432Z",
     "iopub.status.busy": "2024-06-11T01:22:58.724764Z",
     "iopub.status.idle": "2024-06-11T01:28:18.876070Z",
     "shell.execute_reply": "2024-06-11T01:28:18.875280Z"
    },
    "papermill": {
     "duration": 320.160922,
     "end_time": "2024-06-11T01:28:18.878019",
     "exception": false,
     "start_time": "2024-06-11T01:22:58.717097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ddc1c6c00764ae796b818355de7ebe3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/671644 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e40b047adfe4d2f8de31cbe1a22f50e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/167911 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_dataset = train_dataset.map(tokenize_function, batched=True)\n",
    "test_dataset = test_dataset.map(tokenize_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec73d49",
   "metadata": {
    "papermill": {
     "duration": 0.006903,
     "end_time": "2024-06-11T01:28:18.892413",
     "exception": false,
     "start_time": "2024-06-11T01:28:18.885510",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 加载预训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f68100dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:28:18.907964Z",
     "iopub.status.busy": "2024-06-11T01:28:18.907368Z",
     "iopub.status.idle": "2024-06-11T01:28:30.938789Z",
     "shell.execute_reply": "2024-06-11T01:28:30.937959Z"
    },
    "papermill": {
     "duration": 12.041497,
     "end_time": "2024-06-11T01:28:30.940931",
     "exception": false,
     "start_time": "2024-06-11T01:28:18.899434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fd3d3c1bd6745bbbb76f188e4114885",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at google-bert/bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\"google-bert/bert-base-cased\", num_labels=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed544d0",
   "metadata": {
    "papermill": {
     "duration": 0.007375,
     "end_time": "2024-06-11T01:28:30.956160",
     "exception": false,
     "start_time": "2024-06-11T01:28:30.948785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "954e6f67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:28:30.971709Z",
     "iopub.status.busy": "2024-06-11T01:28:30.971384Z",
     "iopub.status.idle": "2024-06-11T01:28:30.976322Z",
     "shell.execute_reply": "2024-06-11T01:28:30.975514Z"
    },
    "papermill": {
     "duration": 0.014857,
     "end_time": "2024-06-11T01:28:30.978226",
     "exception": false,
     "start_time": "2024-06-11T01:28:30.963369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return {\n",
    "        'accuracy': accuracy_score(labels, predictions),\n",
    "        'f1': precision_recall_fscore_support(labels, predictions, average='weighted')[2]\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1080bdfb",
   "metadata": {
    "papermill": {
     "duration": 0.007043,
     "end_time": "2024-06-11T01:28:30.992559",
     "exception": false,
     "start_time": "2024-06-11T01:28:30.985516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 微调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0e65fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:28:31.008422Z",
     "iopub.status.busy": "2024-06-11T01:28:31.008164Z",
     "iopub.status.idle": "2024-06-11T01:28:31.349847Z",
     "shell.execute_reply": "2024-06-11T01:28:31.349054Z"
    },
    "papermill": {
     "duration": 0.351932,
     "end_time": "2024-06-11T01:28:31.351958",
     "exception": false,
     "start_time": "2024-06-11T01:28:31.000026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',\n",
    "    eval_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    num_train_epochs=1,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=16,\n",
    "    warmup_steps=500,\n",
    "    weight_decay=0.01,\n",
    "    logging_dir='./logs',\n",
    "    logging_steps=10,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model='accuracy',\n",
    "    report_to=\"none\"  # 禁用wandb\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=test_dataset,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ba7af05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-11T01:28:31.368371Z",
     "iopub.status.busy": "2024-06-11T01:28:31.368078Z",
     "iopub.status.idle": "2024-06-11T12:51:51.113531Z",
     "shell.execute_reply": "2024-06-11T12:51:51.112597Z"
    },
    "papermill": {
     "duration": 40999.756133,
     "end_time": "2024-06-11T12:51:51.115583",
     "exception": false,
     "start_time": "2024-06-11T01:28:31.359450",
     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='83956' max='83956' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [83956/83956 11:23:18, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.251200</td>\n",
       "      <td>1.222117</td>\n",
       "      <td>0.692843</td>\n",
       "      <td>0.567131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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    {
     "name": "stderr",
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: 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"
     ]
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    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=83956, training_loss=1.0833058757935106, metrics={'train_runtime': 40999.4522, 'train_samples_per_second': 16.382, 'train_steps_per_second': 2.048, 'total_flos': 1.7673441503943475e+17, 'train_loss': 1.0833058757935106, 'epoch': 1.0})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "trainer.train()"
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  },
  {
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    "tags": []
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   "source": [
    "## 保存"
   ]
  },
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     "status": "completed"
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