Senti-eXLM.ipynb

{
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "系统找不到指定的路径。\n"
     ]
    }
   ],
   "source": [
    "!/opt/bin/nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "executionInfo": {
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    "outputId": "a3969145-adfe-4341-c2a5-409b2bd73067"
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   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-2-b173e515fca1>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-b173e515fca1>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    pip install huggingface-hub==0.0.12\u001b[0m\n\u001b[1;37m              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "pip install huggingface-hub==0.0.12\n",
    "pip install ipython==5.5.0\n",
    "pip install ipykernel==4.10\n",
    "pip install simpletransformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
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    "executionInfo": {
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   "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>uig</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>جۇڭگونىڭ گۈللىنىشى، مۇقىملىقى نېپال ئۈچۈنمۇ پۇ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>شۇ كۈنى كۇبا مەملىكەتلىك خەلق ھۆكۈمىتى ۋەكىللە...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بۈگۈنكى كۈندە، چېن لاۋچى كەنتىنىڭ قىياپىتى يېڭ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>كەنت ئاھالىلىرىنىڭ رەڭدار تۇرمۇشى ئاياللارنىڭ ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>«چىرايلىق كىيىملەرنى كىيىپ ئۆيدىن چىقىپ ئىككى ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ئەركىن سودا   رايونى قۇرۇلغاندىن بۇيان، ئىشلار...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ياش دوستلار، يولداشلار!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>باش تېمىلىق ناخشا «گۈزەل يۇرت - ماكان»دا يۇرت ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>چۈشتىن بۇرۇن سائەت 10دىن 30 مىنۇت ئۆتكەندە، شى...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6-ئاينىڭ 13-كۈنى (پەيشەنبە)دىن باشلاپ، ھاۋا يا...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 uig  labels\n",
       "0  جۇڭگونىڭ گۈللىنىشى، مۇقىملىقى نېپال ئۈچۈنمۇ پۇ...       1\n",
       "1  شۇ كۈنى كۇبا مەملىكەتلىك خەلق ھۆكۈمىتى ۋەكىللە...       0\n",
       "2  بۈگۈنكى كۈندە، چېن لاۋچى كەنتىنىڭ قىياپىتى يېڭ...       1\n",
       "3  كەنت ئاھالىلىرىنىڭ رەڭدار تۇرمۇشى ئاياللارنىڭ ...       1\n",
       "4  «چىرايلىق كىيىملەرنى كىيىپ ئۆيدىن چىقىپ ئىككى ...       0\n",
       "5  ئەركىن سودا   رايونى قۇرۇلغاندىن بۇيان، ئىشلار...       1\n",
       "6                            ياش دوستلار، يولداشلار!       1\n",
       "7  باش تېمىلىق ناخشا «گۈزەل يۇرت - ماكان»دا يۇرت ...       0\n",
       "8  چۈشتىن بۇرۇن سائەت 10دىن 30 مىنۇت ئۆتكەندە، شى...       0\n",
       "9  6-ئاينىڭ 13-كۈنى (پەيشەنبە)دىن باشلاپ، ھاۋا يا...       0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入依赖包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
    "from simpletransformers.classification import ClassificationModel\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#数据读取\n",
    "total_df = pd.read_csv('data_translated_with_mark.csv')\n",
    "\n",
    "total_df.columns = ['uig', 'label']\n",
    "total_df['label'] = total_df['label'].astype(int)\n",
    "#total_df = total_df.reindex(columns=['text', 'label'])\n",
    "#total_df['label'] = total_df['label'].map({'like': 0, 'disgust': 1, 'sadness':1, 'happiness': 0, 'anger': 1, 'surprise': 0, 'fear': 1})\n",
    "\n",
    "train_df, test_df = train_test_split(total_df, test_size=0.2, random_state=42, shuffle=True)\n",
    "\n",
    "train_df = train_df.reset_index(drop=True)\n",
    "test_df = test_df.reset_index(drop=True)\n",
    "\n",
    "train_df = train_df.rename(columns={'label':'labels'})\n",
    "test_df = test_df.rename(columns={'label':'labels'})\n",
    "\n",
    "train_df.head(10)"
   ]
  },
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    "executionInfo": {
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     "status": "ok",
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    },
    "id": "_BH2YLp01WGk",
    "outputId": "3d4d4005-1c29-42c3-930d-7a32b4424084"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
    "from transformers import AutoModel\n",
    "model_name = \"xlm-roberta-base\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1071,
     "status": "ok",
     "timestamp": 1636532451429,
     "user": {
      "displayName": "Lee Wealson",
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      "userId": "17425968964893436052"
     },
     "user_tz": -480
    },
    "id": "_lgWeiPG1liU",
    "outputId": "8fb4f85e-2d72-42fd-e1fe-4fd1d7ec3736"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['بىر', 'بىلەن', 'ۋە', 'ئۇ', 'دەپ', 'مەن', 'بولۇپ', '–', 'قىلىپ', 'بولغان', 'ياخشى', 'شۇ', 'ئۈچۈن', 'يەنە', 'بولسا', 'ئۇنىڭ', 'بولىدۇ', 'gt', 'ياكى', 'قىلىش', 'ئىدى', 'يوق', 'ئۇيغۇر', 'ھەر', 'ئۆز', 'ئەمەس', 'كۆپ', 'ئىش', 'قىلغان', 'بىز', 'ئەڭ', 'قانداق', 'باشقا', 'ئىككى', 'كېرەك']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "new_uig_tokens=[]\n",
    "with open('uig.txt','r', encoding = 'utf-8') as f:\n",
    "    for line in f:\n",
    "        token = str(line.strip('\\n').split(','))[2:-2]\n",
    "        if token.find('##'):\n",
    "            #token = token.replace('##', '▁')\n",
    "            new_uig_tokens.append(token)\n",
    "new_uig_tokens = new_uig_tokens[0:1027]\n",
    "print(new_uig_tokens)"
   ]
  },
  {
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   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 418,
     "status": "ok",
     "timestamp": 1636530863860,
     "user": {
      "displayName": "Lee Wealson",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgmKk9hAtSAuL7GoH6bzX3rw6NKMHADnzdAVyg-=s64",
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    },
    "id": "e24jK4uz1zAm",
    "outputId": "c79ffe9b-c3fe-4627-f1d8-41770705d024"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ BEFORE ] tokenizer vocab size: 250002\n",
      "[ AFTER ] tokenizer vocab size: 250027\n",
      "\n",
      "added_tokens: 25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"[ BEFORE ] tokenizer vocab size:\", len(tokenizer)) \n",
    "added_tokens = tokenizer.add_tokens(new_uig_tokens)\n",
    "\n",
    "print(\"[ AFTER ] tokenizer vocab size:\", len(tokenizer)) \n",
    "print('added_tokens:',added_tokens)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "executionInfo": {
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     "timestamp": 1636532594339,
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      "userId": "17425968964893436052"
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     "user_tz": -480
    },
    "id": "j8iUdU7-13AI",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']\n",
      "- This IS expected if you are initializing XLMRobertaForSequenceClassification 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 XLMRobertaForSequenceClassification 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 XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\simpletransformers\\classification\\classification_model.py:346: UserWarning: use_multiprocessing automatically disabled as xlmroberta fails when using multiprocessing for feature conversion.\n",
      "  f\"use_multiprocessing automatically disabled as {model_type}\"\n"
     ]
    }
   ],
   "source": [
    "model = ClassificationModel('xlmroberta', 'xlm-roberta-base', num_labels=3, args={\n",
    "      'sliding_window': False,\n",
    "      'save_steps': False, \n",
    "      'reprocess_input_data': True, \n",
    "      'overwrite_output_dir': True, \n",
    "      'logging_steps': 50, \n",
    "      'train_batch_size': 15, \n",
    "      'gradient_accumulation_steps': 1, \n",
    "      'stride': 0.8, 'max_seq_length': 185, \n",
    "      'num_train_epochs': 14, \n",
    "      'fp16': False, \n",
    "      'reprocess_input_data': True,\n",
    "      'weight_decay': 0.0005,\n",
    "      'optimizer': \"AdamW\",\n",
    "      'evaluate_during_training':True,\n",
    "      'special_tokens_list':new_uig_tokens,\n",
    "      'warmup_ratio': 0.06, \n",
    "      'learning_rate': 2e-6})"
   ]
  },
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     "output_type": "stream",
     "text": [
      "\n",
      "0.7470956210902592\n"
     ]
    }
   ],
   "source": [
    "print('get model')\n",
    "model.train_model(train_df, eval_df=test_df)\n",
    "test_predict = model.predict([row['uig'] for _, row in test_df.iterrows()])\n",
    "print(accuracy_score(test_df['labels'], test_predict[0]))"
   ]
  },
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     "text": [
      "['./outputs/checkpoint-299-epoch-1', './outputs/checkpoint-598-epoch-2', './outputs/checkpoint-897-epoch-3', './outputs/checkpoint-1196-epoch-4', './outputs/checkpoint-1495-epoch-5', './outputs/checkpoint-1794-epoch-6', './outputs/checkpoint-2093-epoch-7', './outputs/checkpoint-2392-epoch-8', './outputs/checkpoint-2691-epoch-9', './outputs/checkpoint-2990-epoch-10', './outputs/checkpoint-3289-epoch-11', './outputs/checkpoint-3588-epoch-12', './outputs/checkpoint-3887-epoch-13', './outputs/checkpoint-4186-epoch-14']\n",
      "Senti-eXLM-299-epoch-1\n"
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      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision is 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",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\simpletransformers\\classification\\classification_model.py:346: UserWarning: use_multiprocessing automatically disabled as xlmroberta fails when using multiprocessing for feature conversion.\n",
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      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\simpletransformers\\classification\\classification_model.py:346: UserWarning: use_multiprocessing automatically disabled as xlmroberta fails when using multiprocessing for feature conversion.\n",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\simpletransformers\\classification\\classification_model.py:346: UserWarning: use_multiprocessing automatically disabled as xlmroberta fails when using multiprocessing for feature conversion.\n",
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    "for i in range(1,15):\n",
    "    models_name = r\"./outputs/checkpoint-\"+str(299*i)+\"-epoch-\"+str(i)\n",
    "    models_list.append(models_name)\n",
    "print(models_list)\n",
    "\n",
    "counter = 1\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for model_addr in models_list:\n",
    "    info = \"Senti-eXLM-\"+str(299*counter)+\"-epoch-\"+str(counter)\n",
    "    #info = \"XLM-R-\"+str(2000*counter)+\"checkpoint\"\n",
    "    print(info)\n",
    "    model = ClassificationModel('xlmroberta', model_addr, num_labels=3, args={'sliding_window': False, 'reprocess_input_data': True, 'overwrite_output_dir': False, 'logging_steps': 50, 'train_batch_size': 32, 'gradient_accumulation_steps': 1, 'stride': 0.8, 'max_seq_length': 185, 'num_train_epochs': 50, 'fp16': False, 'reprocess_input_data': True, 'learning_rate': 1e-5})\n",
    "    test_predict = model.predict([row['uig'] for _, row in test_df.iterrows()])\n",
    "    print(accuracy_score(test_df['labels'], test_predict[0]))\n",
    "    \n",
    "    res_accuracy_score = accuracy_score(test_df['labels'].tolist(), test_predict[0])\n",
    "    res_precision_score = precision_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "    res_recall_score = recall_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "    res_f1_score = f1_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "\n",
    "    df = df.append([[20210712, info, res_accuracy_score, res_precision_score, res_recall_score, res_f1_score]])\n",
    "    counter = counter+1\n",
    "    \n",
    "df.columns=['time', 'model', 'accuracy_score', 'precision_score', 'recall_score' ,'f1_score']\n",
    "df.to_csv('XLM-info03-epochs.csv', index=False)"
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      "Senti-eXLM-299-epoch-1\n"
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    "\n",
    "print(models_list)\n",
    "\n",
    "counter = 1\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for model_addr in models_list:\n",
    "    info = \"Senti-eXLM-\"+str(299*counter)+\"-epoch-\"+str(counter)\n",
    "    #info = \"XLM-R-\"+str(2000*counter)+\"checkpoint\"\n",
    "    print(info)\n",
    "    model = ClassificationModel('xlmroberta', model_addr, num_labels=3, args={'sliding_window': False, 'reprocess_input_data': True, 'overwrite_output_dir': False, 'logging_steps': 50, 'train_batch_size': 32, 'gradient_accumulation_steps': 1, 'stride': 0.8, 'max_seq_length': 185, 'num_train_epochs': 50, 'fp16': False, 'reprocess_input_data': True, 'learning_rate': 1e-5})\n",
    "    test_predict = model.predict([row['uig'] for _, row in test_df.iterrows()])\n",
    "    print(accuracy_score(test_df['labels'], test_predict[0]))\n",
    "    \n",
    "    res_accuracy_score = accuracy_score(test_df['labels'].tolist(), test_predict[0])\n",
    "    res_precision_score = precision_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "    res_recall_score = recall_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "    res_f1_score = f1_score(test_df['labels'].tolist(), test_predict[0], average='weighted')\n",
    "\n",
    "    df = df.append([[20210712, info, res_accuracy_score, res_precision_score, res_recall_score, res_f1_score]])\n",
    "    counter = counter+1\n",
    "    \n",
    "df.columns=['time', 'model', 'accuracy_score', 'precision_score', 'recall_score' ,'f1_score']\n",
    "df.to_csv('XLM-info03-epochs.csv', index=False)"
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