学术论文分类.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 比赛题目：学术论文分类挑战赛\n",
    "- 比赛链接：http://challenge.xfyun.cn/topic/info?type=academic-paper-classification\n",
    "- 比赛任务：本次赛题希望参赛选手利用论文信息：论文id、标题、摘要，划分论文具体类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install simpletransformers -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,f1_score\n",
    "import gc\n",
    "import os\n",
    "import torch\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # 设置显卡\n",
    "# 配置\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina' # 主题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x293eb3d4e10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "rcParams['figure.figsize'] = 25, 20\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "torch.manual_seed(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train/train.csv', sep='\\t')\n",
    "test = pd.read_csv('data/test/test.csv', sep='\\t')\n",
    "sub = pd.read_csv('data/sample_submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>paperid</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_00000</td>\n",
       "      <td>Hard but Robust, Easy but Sensitive: How Encod...</td>\n",
       "      <td>Neural machine translation (NMT) typically a...</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_00001</td>\n",
       "      <td>An Easy-to-use Real-world Multi-objective Opti...</td>\n",
       "      <td>Although synthetic test problems are widely ...</td>\n",
       "      <td>cs.NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_00002</td>\n",
       "      <td>Exploration of reproducibility issues in scien...</td>\n",
       "      <td>This is the first part of a small-scale expl...</td>\n",
       "      <td>cs.DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_00003</td>\n",
       "      <td>Scheduled Sampling for Transformers</td>\n",
       "      <td>Scheduled sampling is a technique for avoidi...</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_00004</td>\n",
       "      <td>Hybrid Forests for Left Ventricle Segmentation...</td>\n",
       "      <td>Machine learning models produce state-of-the...</td>\n",
       "      <td>cs.CV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       paperid                                              title  \\\n",
       "0  train_00000  Hard but Robust, Easy but Sensitive: How Encod...   \n",
       "1  train_00001  An Easy-to-use Real-world Multi-objective Opti...   \n",
       "2  train_00002  Exploration of reproducibility issues in scien...   \n",
       "3  train_00003                Scheduled Sampling for Transformers   \n",
       "4  train_00004  Hybrid Forests for Left Ventricle Segmentation...   \n",
       "\n",
       "                                            abstract categories  \n",
       "0    Neural machine translation (NMT) typically a...      cs.CL  \n",
       "1    Although synthetic test problems are widely ...      cs.NE  \n",
       "2    This is the first part of a small-scale expl...      cs.DL  \n",
       "3    Scheduled sampling is a technique for avoidi...      cs.CL  \n",
       "4    Machine learning models produce state-of-the...      cs.CV  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 4), (10000, 3), (10000, 2))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据大小\n",
    "train.shape,test.shape,sub.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paperid       0\n",
       "title         0\n",
       "abstract      0\n",
       "categories    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看是否有缺失值\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paperid     0\n",
       "title       0\n",
       "abstract    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   paperid     50000 non-null  object\n",
      " 1   title       50000 non-null  object\n",
      " 2   abstract    50000 non-null  object\n",
      " 3   categories  50000 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   paperid   10000 non-null  object\n",
      " 1   title     10000 non-null  object\n",
      " 2   abstract  10000 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train null nums\n",
      "paperid       0\n",
      "title         0\n",
      "abstract      0\n",
      "categories    0\n",
      "dtype: int64\n",
      "test null nums\n",
      "paperid     0\n",
      "title       0\n",
      "abstract    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"train null nums\")\n",
    "print(train.shape[0]-train.count())\n",
    "print(\"test null nums\")\n",
    "print(test.shape[0]-test.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 标签分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cs.CV    11038\n",
       "cs.CL     4260\n",
       "cs.NI     3218\n",
       "cs.CR     2798\n",
       "cs.AI     2706\n",
       "cs.DS     2509\n",
       "cs.DC     1994\n",
       "cs.SE     1940\n",
       "cs.RO     1884\n",
       "cs.LO     1741\n",
       "cs.LG     1352\n",
       "cs.SY     1292\n",
       "cs.CY     1228\n",
       "cs.DB      998\n",
       "cs.GT      984\n",
       "cs.HC      943\n",
       "cs.PL      841\n",
       "cs.IR      770\n",
       "cs.CC      719\n",
       "cs.NE      704\n",
       "cs.CG      683\n",
       "cs.OH      677\n",
       "cs.SI      603\n",
       "cs.DL      537\n",
       "cs.DM      523\n",
       "cs.FL      469\n",
       "cs.AR      363\n",
       "cs.CE      362\n",
       "cs.GR      314\n",
       "cs.MM      261\n",
       "cs.ET      230\n",
       "cs.MA      210\n",
       "cs.NA      176\n",
       "cs.SC      172\n",
       "cs.SD      140\n",
       "cs.PF      139\n",
       "cs.MS      105\n",
       "cs.OS       99\n",
       "cs.GL       18\n",
       "Name: categories, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['categories'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'categories count')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1141,
       "width": 1473
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.categories)\n",
    "plt.xlabel('categories count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拼接title与abstract\n",
    "train['text']=train['title']+' '+train['abstract']\n",
    "test['text']=test['title']+' '+test['abstract']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 文本长度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['text_len']=train['text'].map(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50000.00000\n",
       "mean      1131.28478\n",
       "std        387.14365\n",
       "min         69.00000\n",
       "25%        860.00000\n",
       "50%       1117.00000\n",
       "75%       1393.00000\n",
       "max       3713.00000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['text'].map(len).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean      1127.097700\n",
       "std        388.662603\n",
       "min         74.000000\n",
       "25%        855.750000\n",
       "50%       1111.000000\n",
       "75%       1385.250000\n",
       "max       3501.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['text'].map(len).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1123,
       "width": 1477
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['text_len'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标签与唯一标签映射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_id2cate=dict(enumerate(train.categories.unique()))\n",
    "label_cate2id={value: key for key,value in label_id2cate.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'cs.CL',\n",
       " 1: 'cs.NE',\n",
       " 2: 'cs.DL',\n",
       " 3: 'cs.CV',\n",
       " 4: 'cs.LG',\n",
       " 5: 'cs.DS',\n",
       " 6: 'cs.IR',\n",
       " 7: 'cs.RO',\n",
       " 8: 'cs.DM',\n",
       " 9: 'cs.CR',\n",
       " 10: 'cs.AR',\n",
       " 11: 'cs.NI',\n",
       " 12: 'cs.AI',\n",
       " 13: 'cs.SE',\n",
       " 14: 'cs.CG',\n",
       " 15: 'cs.LO',\n",
       " 16: 'cs.SY',\n",
       " 17: 'cs.GR',\n",
       " 18: 'cs.PL',\n",
       " 19: 'cs.SI',\n",
       " 20: 'cs.OH',\n",
       " 21: 'cs.HC',\n",
       " 22: 'cs.MA',\n",
       " 23: 'cs.GT',\n",
       " 24: 'cs.ET',\n",
       " 25: 'cs.FL',\n",
       " 26: 'cs.CC',\n",
       " 27: 'cs.DB',\n",
       " 28: 'cs.DC',\n",
       " 29: 'cs.CY',\n",
       " 30: 'cs.CE',\n",
       " 31: 'cs.MM',\n",
       " 32: 'cs.NA',\n",
       " 33: 'cs.PF',\n",
       " 34: 'cs.OS',\n",
       " 35: 'cs.SD',\n",
       " 36: 'cs.SC',\n",
       " 37: 'cs.MS',\n",
       " 38: 'cs.GL'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_id2cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cs.CL': 0,\n",
       " 'cs.NE': 1,\n",
       " 'cs.DL': 2,\n",
       " 'cs.CV': 3,\n",
       " 'cs.LG': 4,\n",
       " 'cs.DS': 5,\n",
       " 'cs.IR': 6,\n",
       " 'cs.RO': 7,\n",
       " 'cs.DM': 8,\n",
       " 'cs.CR': 9,\n",
       " 'cs.AR': 10,\n",
       " 'cs.NI': 11,\n",
       " 'cs.AI': 12,\n",
       " 'cs.SE': 13,\n",
       " 'cs.CG': 14,\n",
       " 'cs.LO': 15,\n",
       " 'cs.SY': 16,\n",
       " 'cs.GR': 17,\n",
       " 'cs.PL': 18,\n",
       " 'cs.SI': 19,\n",
       " 'cs.OH': 20,\n",
       " 'cs.HC': 21,\n",
       " 'cs.MA': 22,\n",
       " 'cs.GT': 23,\n",
       " 'cs.ET': 24,\n",
       " 'cs.FL': 25,\n",
       " 'cs.CC': 26,\n",
       " 'cs.DB': 27,\n",
       " 'cs.DC': 28,\n",
       " 'cs.CY': 29,\n",
       " 'cs.CE': 30,\n",
       " 'cs.MM': 31,\n",
       " 'cs.NA': 32,\n",
       " 'cs.PF': 33,\n",
       " 'cs.OS': 34,\n",
       " 'cs.SD': 35,\n",
       " 'cs.SC': 36,\n",
       " 'cs.MS': 37,\n",
       " 'cs.GL': 38}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_cate2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['label']=train['categories'].map(label_cate2id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4  模型训练与评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train[['text','label']]\n",
    "train_y=train[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45000, 2), (5000, 2))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=train[['text','label']][:45000]\n",
    "eval_df=train[['text','label']][45000:]\n",
    "train_df.shape,eval_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training roberta-base.........\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "478e16e8c9714cf7b9b5b4dd56abf9c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=45000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e02223a1a0404b95bfd60a2a7c98b559",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=1.0, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b6798c4d6904d3782b26494d8039f6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 0 of 1', max=5625.0, style=ProgressStyle(de…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6c8673f71a7409996f2f052d9d79e9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "540eaabf170e4a20b02fe2280aa88929",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Running Evaluation', max=625.0, style=ProgressStyle(descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'mcc': 0.6061955662242928, 'acc': 0.6392, 'eval_loss': 1.335857630133629}\n"
     ]
    }
   ],
   "source": [
    "model_args = ClassificationArgs()\n",
    "model_args.max_seq_length = 512\n",
    "model_args.train_batch_size = 8\n",
    "model_args.num_train_epochs = 5\n",
    "model_args.fp16 = False\n",
    "model_args.evaluate_during_training = False\n",
    "model_args.overwrite_output_dir = True\n",
    "\n",
    "model_type = 'roberta'\n",
    "model_name = 'roberta-base'\n",
    "print(\"training {}.........\".format(model_name))\n",
    "model_args.cache_dir = './caches' + '/' + model_name.split('/')[-1]\n",
    "model_args.output_dir = './outputs' + '/' + model_name.split('/')[-1]\n",
    "\n",
    "model = ClassificationModel(\n",
    "    model_type,\n",
    "    model_name,\n",
    "    num_labels=39,\n",
    "    args=model_args)\n",
    "\n",
    "model.train_model(train_df, eval_df=eval_df)\n",
    "result, _, _ = model.eval_model(eval_df, acc=accuracy_score)\n",
    "print(result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8a2fd8ba2fc49f9ae414e9d8ddbff1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=10000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a16606f7162741c280d26aee90665250",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1250.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for index, row in test.iterrows():\n",
    "    data.append(str(row['text']))\n",
    "predictions, raw_outputs = model.predict(data)\n",
    "sub = pd.read_csv('data/sample_submit.csv')\n",
    "sub['categories'] = predictions\n",
    "sub['categories'] = sub['categories'].map(label_id2cate)\n",
    "sub.to_csv('result/submit_{}.csv'.format(model_name), index=False)\n",
    "del model\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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