commonlit-roberta-baseline-model.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/yaaangzhou/commonlit-easy-for-beginner-roberta-baseline?scriptVersionId=142316330\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"f122fd1a","metadata":{"papermill":{"duration":0.010174,"end_time":"2023-09-08T08:40:47.408091","exception":false,"start_time":"2023-09-08T08:40:47.397917","status":"completed"},"tags":[]},"source":["**Created by Yang Zhou**\n","\n","**[CommonLit]Easy for Beginner:Roberta Baseline**\n","\n","**7 Sep 2023**"]},{"cell_type":"markdown","id":"eeaa4d46","metadata":{"papermill":{"duration":0.009205,"end_time":"2023-09-08T08:40:47.426821","exception":false,"start_time":"2023-09-08T08:40:47.417616","status":"completed"},"tags":[]},"source":["# <center style=\"font-family: consolas; font-size: 32px; font-weight: bold;\">[CommonLit]Easy for Beginner:Roberta Baseline</center>\n","<p><center style=\"color:#949494; font-family: consolas; font-size: 20px;\">Automatically assess summaries written by students in grades 3-12</center></p>\n","\n","***\n","\n","**The goal of this competition is to generate a model to automatically score student summaries.The goal of the competition is to help teachers and learning platforms provide better feedback to students on their writing.**\n","\n","**I used ML modeles in another [notebook](https://www.kaggle.com/code/yaaangzhou/commonlit-machine-learning-baseline-model), but it was obvious that it did not work well as a baseline model, so I considered further using LLM to complete the task.**"]},{"cell_type":"markdown","id":"1aba186f","metadata":{"papermill":{"duration":0.009163,"end_time":"2023-09-08T08:40:47.445227","exception":false,"start_time":"2023-09-08T08:40:47.436064","status":"completed"},"tags":[]},"source":["# 0. Imports"]},{"cell_type":"code","execution_count":1,"id":"33a4c8db","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-09-08T08:40:47.465623Z","iopub.status.busy":"2023-09-08T08:40:47.46504Z","iopub.status.idle":"2023-09-08T08:41:00.708635Z","shell.execute_reply":"2023-09-08T08:41:00.707441Z"},"papermill":{"duration":13.257246,"end_time":"2023-09-08T08:41:00.711737","exception":false,"start_time":"2023-09-08T08:40:47.454491","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n","  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"]}],"source":["import numpy as np\n","import pandas as pd\n","\n","import transformers\n","from transformers import AutoModel, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification\n","from transformers import DataCollatorWithPadding\n","from transformers import TrainingArguments, Trainer\n","\n","from datasets import Dataset as Dataset_HF\n","from torch.utils.data import Dataset\n","\n","import torch\n","import gc\n","import re\n","\n","# Metrics\n","from sklearn.metrics import mean_squared_error\n","from sklearn.model_selection import train_test_split\n","\n","import warnings\n","warnings.simplefilter(\"ignore\")"]},{"cell_type":"markdown","id":"c24c4a6d","metadata":{"papermill":{"duration":0.009341,"end_time":"2023-09-08T08:41:00.731065","exception":false,"start_time":"2023-09-08T08:41:00.721724","status":"completed"},"tags":[]},"source":["# 1. Load Datas"]},{"cell_type":"code","execution_count":2,"id":"ea280bf5","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:00.751551Z","iopub.status.busy":"2023-09-08T08:41:00.75093Z","iopub.status.idle":"2023-09-08T08:41:00.863896Z","shell.execute_reply":"2023-09-08T08:41:00.862949Z"},"papermill":{"duration":0.125668,"end_time":"2023-09-08T08:41:00.866216","exception":false,"start_time":"2023-09-08T08:41:00.740548","status":"completed"},"tags":[]},"outputs":[],"source":["data_dir = \"/kaggle/input/commonlit-evaluate-student-summaries/\"\n","train_pro = pd.read_csv(data_dir + 'prompts_train.csv')\n","train_sum = pd.read_csv(data_dir + 'summaries_train.csv')\n","\n","test_pro = pd.read_csv(data_dir + 'prompts_test.csv')\n","test_sum = pd.read_csv(data_dir + 'summaries_test.csv')\n","\n","submission = pd.read_csv(data_dir + 'sample_submission.csv')"]},{"cell_type":"code","execution_count":3,"id":"4a52e19e","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:00.886938Z","iopub.status.busy":"2023-09-08T08:41:00.886647Z","iopub.status.idle":"2023-09-08T08:41:00.916355Z","shell.execute_reply":"2023-09-08T08:41:00.915428Z"},"papermill":{"duration":0.04213,"end_time":"2023-09-08T08:41:00.918349","exception":false,"start_time":"2023-09-08T08:41:00.876219","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Full train dataset shape is (7165, 8)\n"]}],"source":["train = train_sum.merge(train_pro, how=\"left\", on=\"prompt_id\")\n","test = test_sum.merge(test_pro, how=\"left\", on=\"prompt_id\")\n","\n","print(\"Full train dataset shape is {}\".format(train.shape))"]},{"cell_type":"code","execution_count":4,"id":"b5ce185d","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:00.938936Z","iopub.status.busy":"2023-09-08T08:41:00.938654Z","iopub.status.idle":"2023-09-08T08:41:00.956465Z","shell.execute_reply":"2023-09-08T08:41:00.955588Z"},"papermill":{"duration":0.030403,"end_time":"2023-09-08T08:41:00.958598","exception":false,"start_time":"2023-09-08T08:41:00.928195","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>student_id</th>\n","      <th>prompt_id</th>\n","      <th>text</th>\n","      <th>content</th>\n","      <th>wording</th>\n","      <th>prompt_question</th>\n","      <th>prompt_title</th>\n","      <th>prompt_text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>000e8c3c7ddb</td>\n","      <td>814d6b</td>\n","      <td>The third wave was an experimentto see how peo...</td>\n","      <td>0.205683</td>\n","      <td>0.380538</td>\n","      <td>Summarize how the Third Wave developed over su...</td>\n","      <td>The Third Wave</td>\n","      <td>Background \\r\\nThe Third Wave experiment took ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0020ae56ffbf</td>\n","      <td>ebad26</td>\n","      <td>They would rub it up with soda to make the sme...</td>\n","      <td>-0.548304</td>\n","      <td>0.506755</td>\n","      <td>Summarize the various ways the factory would u...</td>\n","      <td>Excerpt from The Jungle</td>\n","      <td>With one member trimming beef in a cannery, an...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>004e978e639e</td>\n","      <td>3b9047</td>\n","      <td>In Egypt, there were many occupations and soci...</td>\n","      <td>3.128928</td>\n","      <td>4.231226</td>\n","      <td>In complete sentences, summarize the structure...</td>\n","      <td>Egyptian Social Structure</td>\n","      <td>Egyptian society was structured like a pyramid...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     student_id prompt_id                                               text  \\\n","0  000e8c3c7ddb    814d6b  The third wave was an experimentto see how peo...   \n","1  0020ae56ffbf    ebad26  They would rub it up with soda to make the sme...   \n","2  004e978e639e    3b9047  In Egypt, there were many occupations and soci...   \n","\n","    content   wording                                    prompt_question  \\\n","0  0.205683  0.380538  Summarize how the Third Wave developed over su...   \n","1 -0.548304  0.506755  Summarize the various ways the factory would u...   \n","2  3.128928  4.231226  In complete sentences, summarize the structure...   \n","\n","                prompt_title  \\\n","0             The Third Wave   \n","1    Excerpt from The Jungle   \n","2  Egyptian Social Structure   \n","\n","                                         prompt_text  \n","0  Background \\r\\nThe Third Wave experiment took ...  \n","1  With one member trimming beef in a cannery, an...  \n","2  Egyptian society was structured like a pyramid...  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train.head(3)"]},{"cell_type":"markdown","id":"79709de2","metadata":{"papermill":{"duration":0.00963,"end_time":"2023-09-08T08:41:00.978238","exception":false,"start_time":"2023-09-08T08:41:00.968608","status":"completed"},"tags":[]},"source":["# 2. Data Prepocessing\n","\n","First, we need to remove some special symbols in the text because it has no semantic meaning."]},{"cell_type":"code","execution_count":5,"id":"831d5f5c","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:00.99895Z","iopub.status.busy":"2023-09-08T08:41:00.998663Z","iopub.status.idle":"2023-09-08T08:41:01.129552Z","shell.execute_reply":"2023-09-08T08:41:01.128655Z"},"papermill":{"duration":0.143681,"end_time":"2023-09-08T08:41:01.131597","exception":false,"start_time":"2023-09-08T08:41:00.987916","status":"completed"},"tags":[]},"outputs":[],"source":["train['text'] = train[\"text\"].replace(re.compile(r'[\\n\\r\\t]'), ' ', regex=True)\n","test['text'] = test[\"text\"].replace(re.compile(r'[\\n\\r\\t]'), ' ', regex=True)"]},{"cell_type":"markdown","id":"41231704","metadata":{"papermill":{"duration":0.009555,"end_time":"2023-09-08T08:41:01.151211","exception":false,"start_time":"2023-09-08T08:41:01.141656","status":"completed"},"tags":[]},"source":["# 3. Set Configuration"]},{"cell_type":"code","execution_count":6,"id":"4485f3e1","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-09-08T08:41:01.172584Z","iopub.status.busy":"2023-09-08T08:41:01.171796Z","iopub.status.idle":"2023-09-08T08:41:11.096858Z","shell.execute_reply":"2023-09-08T08:41:11.095832Z"},"papermill":{"duration":9.938474,"end_time":"2023-09-08T08:41:11.099675","exception":false,"start_time":"2023-09-08T08:41:01.161201","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at /kaggle/input/distilroberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.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"]}],"source":["class CFG:\n","    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","    max_length=512\n","    hidden_dropout_prob=0.005\n","    attention_probs_dropout_prob=0.005\n","    model_name = \"distilroberta-base\"\n","    tokenizer = AutoTokenizer.from_pretrained('/kaggle/input/distilroberta-base')\n","    model = AutoModelForSequenceClassification.from_pretrained('/kaggle/input/distilroberta-base',num_labels=2,problem_type=\"regression\").to(device)"]},{"cell_type":"code","execution_count":7,"id":"9f64eece","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.121544Z","iopub.status.busy":"2023-09-08T08:41:11.121212Z","iopub.status.idle":"2023-09-08T08:41:11.130722Z","shell.execute_reply":"2023-09-08T08:41:11.129747Z"},"papermill":{"duration":0.022789,"end_time":"2023-09-08T08:41:11.132744","exception":false,"start_time":"2023-09-08T08:41:11.109955","status":"completed"},"tags":[]},"outputs":[],"source":["model_config = AutoConfig.from_pretrained('/kaggle/input/distilroberta-base')\n","model_config.update({\n","        \"hidden_dropout_prob\": CFG.hidden_dropout_prob,\n","        \"attention_probs_dropout_prob\": CFG.attention_probs_dropout_prob,\n","        \"num_labels\": 2,\n","        \"problem_type\": \"regression\",\n","    })"]},{"cell_type":"markdown","id":"a1ceb460","metadata":{"papermill":{"duration":0.009885,"end_time":"2023-09-08T08:41:11.152762","exception":false,"start_time":"2023-09-08T08:41:11.142877","status":"completed"},"tags":[]},"source":["## Data Collector"]},{"cell_type":"code","execution_count":8,"id":"d070506a","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.174462Z","iopub.status.busy":"2023-09-08T08:41:11.173604Z","iopub.status.idle":"2023-09-08T08:41:11.178289Z","shell.execute_reply":"2023-09-08T08:41:11.177454Z"},"papermill":{"duration":0.017574,"end_time":"2023-09-08T08:41:11.180262","exception":false,"start_time":"2023-09-08T08:41:11.162688","status":"completed"},"tags":[]},"outputs":[],"source":["data_collator = DataCollatorWithPadding(tokenizer=CFG.tokenizer)"]},{"attachments":{"1760ad27-a14a-43f9-8fd1-5cc8242c4c13.png":{"image/png":"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"}},"cell_type":"markdown","id":"483b8a66","metadata":{"papermill":{"duration":0.00969,"end_time":"2023-09-08T08:41:11.199728","exception":false,"start_time":"2023-09-08T08:41:11.190038","status":"completed"},"tags":[]},"source":["## Evaluation Function\n","\n","![image.png](attachment:1760ad27-a14a-43f9-8fd1-5cc8242c4c13.png)"]},{"cell_type":"code","execution_count":9,"id":"2394d4c0","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.221003Z","iopub.status.busy":"2023-09-08T08:41:11.220093Z","iopub.status.idle":"2023-09-08T08:41:11.226313Z","shell.execute_reply":"2023-09-08T08:41:11.225464Z"},"papermill":{"duration":0.018929,"end_time":"2023-09-08T08:41:11.228321","exception":false,"start_time":"2023-09-08T08:41:11.209392","status":"completed"},"tags":[]},"outputs":[],"source":["def caculate_mcrmse(eval_pred):\n","    predictions, labels = eval_pred\n","    squared_errors = np.square(predictions - labels)\n","    mean_squared_errors = np.mean(squared_errors, axis=0)\n","    rmse = np.sqrt(mean_squared_errors)\n","\n","    mcrmse_value = np.mean(rmse)\n","    content_rmse = rmse[0]\n","    wording_rmse = rmse[1]\n","    \n","    return {\n","        \"mcrmse\": mcrmse_value,\n","        \"content_rmse\": content_rmse,\n","        \"wording_rmse\": wording_rmse\n","    }"]},{"cell_type":"code","execution_count":10,"id":"16427bea","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.249398Z","iopub.status.busy":"2023-09-08T08:41:11.24872Z","iopub.status.idle":"2023-09-08T08:41:11.266622Z","shell.execute_reply":"2023-09-08T08:41:11.265772Z"},"papermill":{"duration":0.030496,"end_time":"2023-09-08T08:41:11.268642","exception":false,"start_time":"2023-09-08T08:41:11.238146","status":"completed"},"tags":[]},"outputs":[],"source":["df_train, df_valid = train_test_split(train, test_size=0.2, random_state=42, stratify=train['prompt_id'])"]},{"cell_type":"markdown","id":"068af750","metadata":{"papermill":{"duration":0.00963,"end_time":"2023-09-08T08:41:11.288107","exception":false,"start_time":"2023-09-08T08:41:11.278477","status":"completed"},"tags":[]},"source":["# 4. Dataset for different targets"]},{"cell_type":"code","execution_count":11,"id":"1b551a28","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.309686Z","iopub.status.busy":"2023-09-08T08:41:11.309344Z","iopub.status.idle":"2023-09-08T08:41:11.322521Z","shell.execute_reply":"2023-09-08T08:41:11.321538Z"},"papermill":{"duration":0.025906,"end_time":"2023-09-08T08:41:11.324476","exception":false,"start_time":"2023-09-08T08:41:11.29857","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>student_id</th>\n","      <th>prompt_id</th>\n","      <th>text</th>\n","      <th>content</th>\n","      <th>wording</th>\n","      <th>prompt_question</th>\n","      <th>prompt_title</th>\n","      <th>prompt_text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3916</th>\n","      <td>8a31b8cc1996</td>\n","      <td>3b9047</td>\n","      <td>In the social pyramid of ancient Egypt the pha...</td>\n","      <td>-0.077267</td>\n","      <td>0.424365</td>\n","      <td>In complete sentences, summarize the structure...</td>\n","      <td>Egyptian Social Structure</td>\n","      <td>Egyptian society was structured like a pyramid...</td>\n","    </tr>\n","    <tr>\n","      <th>3985</th>\n","      <td>8c9411cfc953</td>\n","      <td>39c16e</td>\n","      <td>Aristotle claims that an ideal tragedy should ...</td>\n","      <td>0.559070</td>\n","      <td>-0.634924</td>\n","      <td>Summarize at least 3 elements of an ideal trag...</td>\n","      <td>On Tragedy</td>\n","      <td>Chapter 13 \\r\\nAs the sequel to what has alrea...</td>\n","    </tr>\n","    <tr>\n","      <th>1888</th>\n","      <td>4387107feb4d</td>\n","      <td>3b9047</td>\n","      <td>The ancient Egyptian system of government was ...</td>\n","      <td>1.376083</td>\n","      <td>2.389443</td>\n","      <td>In complete sentences, summarize the structure...</td>\n","      <td>Egyptian Social Structure</td>\n","      <td>Egyptian society was structured like a pyramid...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        student_id prompt_id  \\\n","3916  8a31b8cc1996    3b9047   \n","3985  8c9411cfc953    39c16e   \n","1888  4387107feb4d    3b9047   \n","\n","                                                   text   content   wording  \\\n","3916  In the social pyramid of ancient Egypt the pha... -0.077267  0.424365   \n","3985  Aristotle claims that an ideal tragedy should ...  0.559070 -0.634924   \n","1888  The ancient Egyptian system of government was ...  1.376083  2.389443   \n","\n","                                        prompt_question  \\\n","3916  In complete sentences, summarize the structure...   \n","3985  Summarize at least 3 elements of an ideal trag...   \n","1888  In complete sentences, summarize the structure...   \n","\n","                   prompt_title  \\\n","3916  Egyptian Social Structure   \n","3985                 On Tragedy   \n","1888  Egyptian Social Structure   \n","\n","                                            prompt_text  \n","3916  Egyptian society was structured like a pyramid...  \n","3985  Chapter 13 \\r\\nAs the sequel to what has alrea...  \n","1888  Egyptian society was structured like a pyramid...  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_train.head(3)"]},{"cell_type":"code","execution_count":12,"id":"f60d44aa","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.346368Z","iopub.status.busy":"2023-09-08T08:41:11.345558Z","iopub.status.idle":"2023-09-08T08:41:11.353218Z","shell.execute_reply":"2023-09-08T08:41:11.352343Z"},"papermill":{"duration":0.020513,"end_time":"2023-09-08T08:41:11.355204","exception":false,"start_time":"2023-09-08T08:41:11.334691","status":"completed"},"tags":[]},"outputs":[],"source":["train_content = df_train[[\"prompt_question\", \"text\", \"content\", \"wording\"]]\n","valid_content = df_valid[[\"prompt_question\", \"text\", \"content\", \"wording\"]]\n","\n","df_test = test[[\"prompt_question\",\"text\"]]"]},{"cell_type":"markdown","id":"af2bbe8e","metadata":{"papermill":{"duration":0.010047,"end_time":"2023-09-08T08:41:11.375249","exception":false,"start_time":"2023-09-08T08:41:11.365202","status":"completed"},"tags":[]},"source":["**Now we need to transform the dataframe to dataset.**"]},{"cell_type":"code","execution_count":13,"id":"28de1841","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.397001Z","iopub.status.busy":"2023-09-08T08:41:11.396148Z","iopub.status.idle":"2023-09-08T08:41:11.428653Z","shell.execute_reply":"2023-09-08T08:41:11.427772Z"},"papermill":{"duration":0.045602,"end_time":"2023-09-08T08:41:11.430784","exception":false,"start_time":"2023-09-08T08:41:11.385182","status":"completed"},"tags":[]},"outputs":[],"source":["train_dataset_content = Dataset_HF.from_pandas(train_content, preserve_index=False) \n","valid_dataset_content = Dataset_HF.from_pandas(valid_content, preserve_index=False) \n","test_dataset = Dataset_HF.from_pandas(df_test, preserve_index=False) "]},{"cell_type":"markdown","id":"1259b2fc","metadata":{"papermill":{"duration":0.009908,"end_time":"2023-09-08T08:41:11.451046","exception":false,"start_time":"2023-09-08T08:41:11.441138","status":"completed"},"tags":[]},"source":["# 5. Tokenizer\n","\n","**We need to tokenize the text before put it into a model. We are gonna distinguish between training set and test set. If it is a training set, the tokenizer will return a dictionary including input id, attention mask, and labels.**\n","\n","**If it is a training set, only the input id and attention mask will be returned.**"]},{"cell_type":"code","execution_count":14,"id":"6406ef78","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.472995Z","iopub.status.busy":"2023-09-08T08:41:11.472149Z","iopub.status.idle":"2023-09-08T08:41:11.479186Z","shell.execute_reply":"2023-09-08T08:41:11.478314Z"},"papermill":{"duration":0.020174,"end_time":"2023-09-08T08:41:11.481179","exception":false,"start_time":"2023-09-08T08:41:11.461005","status":"completed"},"tags":[]},"outputs":[],"source":["def tokenize_function(examples,dataset='train'):\n","    if dataset == 'train':\n","        labels = [examples[\"content\"], examples[\"wording\"]]\n","        tokenized = CFG.tokenizer(examples[\"text\"],\n","                                  examples[\"prompt_question\"],\n","                                  padding=False,\n","                                  truncation=True,\n","                                  max_length=CFG.max_length)\n","        return {**tokenized,\"labels\": labels}\n","        \n","    elif dataset == 'test':\n","        tokenized = CFG.tokenizer(examples[\"text\"],\n","                                  examples[\"prompt_question\"],\n","                                  padding=False,\n","                                  truncation=True,\n","                                  max_length=CFG.max_length)\n","        \n","        return tokenized"]},{"cell_type":"code","execution_count":15,"id":"e7795477","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:11.50286Z","iopub.status.busy":"2023-09-08T08:41:11.502028Z","iopub.status.idle":"2023-09-08T08:41:15.843368Z","shell.execute_reply":"2023-09-08T08:41:15.842434Z"},"papermill":{"duration":4.357982,"end_time":"2023-09-08T08:41:15.849119","exception":false,"start_time":"2023-09-08T08:41:11.491137","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"729b111d6deb49999c40d4bceb8222c9","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/5732 [00:00<?, ?ex/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3fdcabdadab641fdb5e98daa763909cf","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/1433 [00:00<?, ?ex/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f6f979feb81f4ae8aebb62502071af80","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/4 [00:00<?, ?ex/s]"]},"metadata":{},"output_type":"display_data"}],"source":["train_tokenized_datasets_content = train_dataset_content.map(lambda example: tokenize_function(example, dataset='train'), batched=False)\n","valid_tokenized_datasets_content = valid_dataset_content.map(lambda example: tokenize_function(example, dataset='train'), batched=False)\n","test_tokenized_datasets_content = test_dataset.map(lambda example: tokenize_function(example,dataset='test'),batched=False)"]},{"cell_type":"code","execution_count":16,"id":"96295431","metadata":{"execution":{"iopub.execute_input":"2023-09-08T08:41:15.873309Z","iopub.status.busy":"2023-09-08T08:41:15.871862Z","iopub.status.idle":"2023-09-08T08:41:16.132305Z","shell.execute_reply":"2023-09-08T08:41:16.131314Z"},"papermill":{"duration":0.274245,"end_time":"2023-09-08T08:41:16.134354","exception":false,"start_time":"2023-09-08T08:41:15.860109","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["174"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["gc.collect()"]},{"cell_type":"markdown","id":"7b216409","metadata":{"papermill":{"duration":0.011341,"end_time":"2023-09-08T08:41:16.156791","exception":false,"start_time":"2023-09-08T08:41:16.14545","status":"completed"},"tags":[]},"source":["# 6. Training a model"]},{"cell_type":"code","execution_count":17,"id":"02e4a2a8","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-09-08T08:41:16.179813Z","iopub.status.busy":"2023-09-08T08:41:16.178973Z","iopub.status.idle":"2023-09-08T09:03:38.134327Z","shell.execute_reply":"2023-09-08T09:03:38.133295Z"},"papermill":{"duration":1341.969449,"end_time":"2023-09-08T09:03:38.136848","exception":false,"start_time":"2023-09-08T08:41:16.167399","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='10755' max='10755' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [10755/10755 22:19, Epoch 15/15]\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>Mcrmse</th>\n","      <th>Content Rmse</th>\n","      <th>Wording Rmse</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.442800</td>\n","      <td>0.287768</td>\n","      <td>0.530185</td>\n","      <td>0.448504</td>\n","      <td>0.611866</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.277700</td>\n","      <td>0.298986</td>\n","      <td>0.541776</td>\n","      <td>0.467848</td>\n","      <td>0.615703</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.228000</td>\n","      <td>0.348440</td>\n","      <td>0.586228</td>\n","      <td>0.517118</td>\n","      <td>0.655338</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>0.189800</td>\n","      <td>0.268272</td>\n","      <td>0.515009</td>\n","      <td>0.459896</td>\n","      <td>0.570122</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>0.162200</td>\n","      <td>0.251474</td>\n","      <td>0.497914</td>\n","      <td>0.438284</td>\n","      <td>0.557544</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>0.136100</td>\n","      <td>0.262401</td>\n","      <td>0.509353</td>\n","      <td>0.454938</td>\n","      <td>0.563768</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>0.120100</td>\n","      <td>0.269065</td>\n","      <td>0.516094</td>\n","      <td>0.464021</td>\n","      <td>0.568167</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>0.102700</td>\n","      <td>0.269348</td>\n","      <td>0.516989</td>\n","      <td>0.471483</td>\n","      <td>0.562494</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>0.089500</td>\n","      <td>0.285207</td>\n","      <td>0.530260</td>\n","      <td>0.466768</td>\n","      <td>0.593753</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>0.079900</td>\n","      <td>0.279349</td>\n","      <td>0.525849</td>\n","      <td>0.472630</td>\n","      <td>0.579067</td>\n","    </tr>\n","    <tr>\n","      <td>11</td>\n","      <td>0.072400</td>\n","      <td>0.261721</td>\n","      <td>0.508247</td>\n","      <td>0.449888</td>\n","      <td>0.566606</td>\n","    </tr>\n","    <tr>\n","      <td>12</td>\n","      <td>0.065300</td>\n","      <td>0.272801</td>\n","      <td>0.518735</td>\n","      <td>0.457784</td>\n","      <td>0.579685</td>\n","    </tr>\n","    <tr>\n","      <td>13</td>\n","      <td>0.060200</td>\n","      <td>0.275882</td>\n","      <td>0.521602</td>\n","      <td>0.459848</td>\n","      <td>0.583356</td>\n","    </tr>\n","    <tr>\n","      <td>14</td>\n","      <td>0.056500</td>\n","      <td>0.285510</td>\n","      <td>0.530388</td>\n","      <td>0.465589</td>\n","      <td>0.595187</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>0.053500</td>\n","      <td>0.278449</td>\n","      <td>0.524441</td>\n","      <td>0.466043</td>\n","      <td>0.582839</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"}],"source":["training_args = TrainingArguments(\n","    output_dir=\"output\",             \n","    per_device_train_batch_size=8,   \n","    per_device_eval_batch_size=4,    \n","    learning_rate=1.5e-5,            \n","    lr_scheduler_type=\"linear\",      \n","    warmup_ratio=0.01,               \n","    num_train_epochs=15,              \n","    save_strategy=\"epoch\",           \n","    logging_strategy=\"epoch\",        \n","    evaluation_strategy=\"epoch\",    \n","    load_best_model_at_end=True,     \n","    metric_for_best_model=\"mcrmse\",  \n","    greater_is_better=False,         \n","    fp16=False,                      \n","    report_to='none',                \n","    save_total_limit=1               \n",")\n","\n","trainer = Trainer(\n","    model=CFG.model,\n","    train_dataset=train_tokenized_datasets_content,\n","    eval_dataset=valid_tokenized_datasets_content,\n","    data_collator=data_collator,\n","    args=training_args,\n","    compute_metrics=caculate_mcrmse,\n","    tokenizer=CFG.tokenizer\n",")\n","trainer.train()\n","\n","trainer.save_model(\"best_model\")"]},{"cell_type":"markdown","id":"db54f6ad","metadata":{"papermill":{"duration":0.011413,"end_time":"2023-09-08T09:03:38.15976","exception":false,"start_time":"2023-09-08T09:03:38.148347","status":"completed"},"tags":[]},"source":["# 7. Prediction"]},{"cell_type":"code","execution_count":18,"id":"c8997272","metadata":{"execution":{"iopub.execute_input":"2023-09-08T09:03:38.184528Z","iopub.status.busy":"2023-09-08T09:03:38.18284Z","iopub.status.idle":"2023-09-08T09:03:38.206955Z","shell.execute_reply":"2023-09-08T09:03:38.205984Z"},"papermill":{"duration":0.038283,"end_time":"2023-09-08T09:03:38.208977","exception":false,"start_time":"2023-09-08T09:03:38.170694","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":[],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["PredictionOutput(predictions=array([[-1.3094829, -1.1639411],\n","       [-1.3149625, -1.1748605],\n","       [-1.322452 , -1.1852924],\n","       [-1.3239895, -1.1803738]], dtype=float32), label_ids=None, metrics={'test_runtime': 0.011, 'test_samples_per_second': 362.758, 'test_steps_per_second': 90.69})"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["predictions = trainer.predict(test_tokenized_datasets_content)\n","predictions"]},{"cell_type":"code","execution_count":19,"id":"73d97755","metadata":{"execution":{"iopub.execute_input":"2023-09-08T09:03:38.234907Z","iopub.status.busy":"2023-09-08T09:03:38.233491Z","iopub.status.idle":"2023-09-08T09:03:38.238704Z","shell.execute_reply":"2023-09-08T09:03:38.237833Z"},"papermill":{"duration":0.019447,"end_time":"2023-09-08T09:03:38.240647","exception":false,"start_time":"2023-09-08T09:03:38.2212","status":"completed"},"tags":[]},"outputs":[],"source":["content_list = predictions.predictions[:, 0].tolist()\n","wording_list = predictions.predictions[:, 1].tolist()"]},{"cell_type":"code","execution_count":20,"id":"0bbda013","metadata":{"execution":{"iopub.execute_input":"2023-09-08T09:03:38.264673Z","iopub.status.busy":"2023-09-08T09:03:38.264113Z","iopub.status.idle":"2023-09-08T09:03:38.280033Z","shell.execute_reply":"2023-09-08T09:03:38.278993Z"},"papermill":{"duration":0.030158,"end_time":"2023-09-08T09:03:38.282131","exception":false,"start_time":"2023-09-08T09:03:38.251973","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>student_id</th>\n","      <th>content</th>\n","      <th>wording</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>000000ffffff</td>\n","      <td>-1.309483</td>\n","      <td>-1.163941</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>111111eeeeee</td>\n","      <td>-1.314963</td>\n","      <td>-1.174860</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>222222cccccc</td>\n","      <td>-1.322452</td>\n","      <td>-1.185292</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>333333dddddd</td>\n","      <td>-1.323990</td>\n","      <td>-1.180374</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     student_id   content   wording\n","0  000000ffffff -1.309483 -1.163941\n","1  111111eeeeee -1.314963 -1.174860\n","2  222222cccccc -1.322452 -1.185292\n","3  333333dddddd -1.323990 -1.180374"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["submission[\"content\"] = content_list\n","submission[\"wording\"] = wording_list\n","\n","submission.to_csv(\"submission.csv\", index=False)\n","submission.head()"]}],"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.4"},"metadata":{"kernelspec":{"display_name":"Python 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