learning-equality-st-train-sm.ipynb

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    "tags": []
   },
   "source": [
    "# Kaggle Challenge - Learning Equality"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
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    "tags": []
   },
   "source": [
    "# Data"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
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     "text": [
      "Collecting sentence-transformers\r\n",
      "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (4.20.1)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (4.64.0)\r\n",
      "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.11.0)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.12.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.21.6)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.0.2)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.7.3)\r\n",
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (3.8.1)\r\n",
      "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.1.97)\r\n",
      "Requirement already satisfied: huggingface-hub>=0.4.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.10.1)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2.28.1)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (6.0)\r\n",
      "Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (22.0)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.7.1)\r\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.13.0)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.1.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.11.10)\r\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.12.1)\r\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from nltk->sentence-transformers) (8.1.3)\r\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from nltk->sentence-transformers) (1.0.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sentence-transformers) (3.1.0)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->sentence-transformers) (9.1.1)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->huggingface-hub>=0.4.0->sentence-transformers) (3.8.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.3)\r\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.1.0)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.12.7)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (1.26.13)\r\n",
      "Building wheels for collected packages: sentence-transformers\r\n",
      "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=091429ab444041f24dda9af7c1de8d80300d3277e7cd0d8774c973ca33fc7b99\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\r\n",
      "Successfully built sentence-transformers\r\n",
      "Installing collected packages: sentence-transformers\r\n",
      "Successfully installed sentence-transformers-2.2.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -U sentence-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84ce3184",
   "metadata": {
    "execution": {
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    },
    "id": "b5f3a3b6",
    "papermill": {
     "duration": 0.014214,
     "end_time": "2023-03-07T19:30:18.465121",
     "exception": false,
     "start_time": "2023-03-07T19:30:18.450907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import string"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d140477",
   "metadata": {
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     "duration": 0.005566,
     "end_time": "2023-03-07T19:30:18.476403",
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     "start_time": "2023-03-07T19:30:18.470837",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6481fd0",
   "metadata": {
    "id": "e55f4587",
    "papermill": {
     "duration": 0.005495,
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     "exception": false,
     "start_time": "2023-03-07T19:30:18.482104",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a40b829",
   "metadata": {
    "id": "FJLGLdJ-wKZI",
    "papermill": {
     "duration": 0.007303,
     "end_time": "2023-03-07T19:30:18.500816",
     "exception": false,
     "start_time": "2023-03-07T19:30:18.493513",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load dataframes"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
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     "text": [
      "\n",
      "Loading dataframes...\n",
      "\n",
      "Loading dataframe from /kaggle/input/learning-equality-curriculum-recommendations/sample_submission.csv...\n",
      "\n",
      "Loading 'sample' dataframe...\n"
     ]
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     "output_type": "stream",
     "text": [
      "\n",
      "Loading dataframe from /kaggle/input/learning-equality-curriculum-recommendations/topics.csv...\n"
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       "      <td>2</td>\n",
       "      <td>en</td>\n",
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       "      <td>False</td>\n",
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       "      <td>Entradas e saídas de uma função</td>\n",
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       "      <th>4</th>\n",
       "      <td>t_0006d41a73a8</td>\n",
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       "      <td>Научи повече за графиките на сложните показате...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76967</th>\n",
       "      <td>t_fffb0bf2801d</td>\n",
       "      <td>4.3 Graph of functions</td>\n",
       "      <td></td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>76968</th>\n",
       "      <td>t_fffbe1d5d43c</td>\n",
       "      <td>Inscribed shapes problem solving</td>\n",
       "      <td>Use properties of inscribed angles to prove pr...</td>\n",
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       "      <td>source</td>\n",
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       "    <tr>\n",
       "      <th>76969</th>\n",
       "      <td>t_fffe14f1be1e</td>\n",
       "      <td>Lección 7</td>\n",
       "      <td></td>\n",
       "      <td>6e90a7</td>\n",
       "      <td>aligned</td>\n",
       "      <td>6</td>\n",
       "      <td>es</td>\n",
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       "    <tr>\n",
       "      <th>76970</th>\n",
       "      <td>t_fffe811a6da9</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>t_fffe88835149</td>\n",
       "      <td>Formation of shadows and eclipses (umbra and p...</td>\n",
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       "      <td>c7ca13</td>\n",
       "      <td>aligned</td>\n",
       "      <td>4</td>\n",
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       "<p>76972 rows × 9 columns</p>\n",
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      "Dataframes loaded.\n"
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   ],
   "source": [
    "challenge_files_path = '/kaggle/input/learning-equality-curriculum-recommendations'\n",
    "private_files_path = '/kaggle/input/learningequalityfiles'\n",
    "model_files_path = '/kaggle/input/sentence-transformer-package'\n",
    "\n",
    "print (f\"\\nLoading dataframes...\")\n",
    "\n",
    "for dirname, _, filenames in os.walk(challenge_files_path):\n",
    "    for filename in filenames:\n",
    "        filepath = os.path.join(dirname, filename)\n",
    "        print (f\"\\nLoading dataframe from {filepath}...\")\n",
    "        df = pd.read_csv (filepath)\n",
    "        if 'topics' in filepath:\n",
    "            topics_df = df.fillna({\"title\": \"\", \"description\": \"\"})\n",
    "            display(topics_df)\n",
    "        elif 'sample_submission' in filepath:\n",
    "            print (f\"\\nLoading 'sample' dataframe...\")\n",
    "            sample_df = df\n",
    "            display(sample_df)\n",
    "        elif 'correlations' in filepath:\n",
    "            correlations_df = df.fillna({\"title\": \"\", \"description\": \"\"})\n",
    "            display(correlations_df)\n",
    "            print (f\"\\nCreating exploded correlations 'corr' dataframe\")\n",
    "            corr_df = correlations_df.copy()\n",
    "            corr_df['content_ids'] = corr_df.content_ids.str.split(' ')\n",
    "            corr_df = corr_df.explode('content_ids')\n",
    "            display (corr_df)\n",
    "        elif 'content' in filepath:\n",
    "            contents_df = df.fillna({\"title\": \"\", \"description\": \"\", \"text\": \"\"})\n",
    "            display(contents_df)\n",
    "print (f\"\\nDataframes loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f86a9f",
   "metadata": {
    "id": "CP4lmcnV20yH",
    "papermill": {
     "duration": 0.007583,
     "end_time": "2023-03-07T19:30:41.585059",
     "exception": false,
     "start_time": "2023-03-07T19:30:41.577476",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be1fdc3",
   "metadata": {
    "id": "Hwk6JtmRbn42",
    "papermill": {
     "duration": 0.007519,
     "end_time": "2023-03-07T19:30:41.600315",
     "exception": false,
     "start_time": "2023-03-07T19:30:41.592796",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data cleaning functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "752f6ff8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:30:41.617590Z",
     "iopub.status.busy": "2023-03-07T19:30:41.617165Z",
     "iopub.status.idle": "2023-03-07T19:30:41.624476Z",
     "shell.execute_reply": "2023-03-07T19:30:41.623422Z"
    },
    "id": "xRgKUBVjbstB",
    "papermill": {
     "duration": 0.018845,
     "end_time": "2023-03-07T19:30:41.626975",
     "exception": false,
     "start_time": "2023-03-07T19:30:41.608130",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Clean up text\n",
    "def clean_text(text_col):\n",
    "    \"\"\"\n",
    "    Clean ponctuation and special chars from a dataframe column\n",
    "    \"\"\"\n",
    "    punctuations = string.punctuation\n",
    "    text_col = text_col.str.replace('\\W', ' ', regex=True)\n",
    "    for punct in string.punctuation:\n",
    "        text_col = text_col.str.replace(punct, ' ', regex=True)\n",
    "    return text_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a2fc35c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:30:41.643318Z",
     "iopub.status.busy": "2023-03-07T19:30:41.643014Z",
     "iopub.status.idle": "2023-03-07T19:30:55.777203Z",
     "shell.execute_reply": "2023-03-07T19:30:55.775376Z"
    },
    "id": "prh6uaPXlyMr",
    "papermill": {
     "duration": 14.14581,
     "end_time": "2023-03-07T19:30:55.780387",
     "exception": false,
     "start_time": "2023-03-07T19:30:41.634577",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Creating and cleaning topic features...\n",
      "\n",
      "Created 'topic_features'\n"
     ]
    },
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <td>计算两位数加两位数的不进位加法</td>\n",
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       "      <td>True</td>\n",
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       "      <td>f83dcf</td>\n",
       "      <td>source</td>\n",
       "      <td>Level 4</td>\n",
       "      <td>zh</td>\n",
       "      <td>True</td>\n",
       "      <td>加减法 二</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61517 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                        description  \\\n",
       "76970  t_fffe811a6da9                           5b9e5ca86571f90499ea987f   \n",
       "36893  t_7b5ca5d6bf6a                   Everyday English Phrases (Part 3   \n",
       "36781  t_7af06b12a79f                                                      \n",
       "3964   t_0d308a58d685                           5adefa698b01ea049a776386   \n",
       "36771  t_7ae88c23c58d                                                      \n",
       "...               ...                                                ...   \n",
       "59851  t_c7de37ff1773  掌握体积的概念以及认识体积单位，理解计算公式的推导过程，掌握体积计算的方法，并且理解容积和容...   \n",
       "22956  t_4d3ac853a17f                              通过一些练习来解决将分数乘以整数的应用题.   \n",
       "33496  t_6fe539174250                                                      \n",
       "30147  t_64e46e13bd1f                                    计算两位数加两位数的不进位加法   \n",
       "18146  t_3cd9edab970c                              计算两位数减一位数（不退位、退位）、整十数   \n",
       "\n",
       "      channel category    level language  has_content  \\\n",
       "76970  9fd860   source  Level 2       ar         True   \n",
       "36893  5139e9   source  Level 7       ar         True   \n",
       "36781  7b47c5   source  Level 4       ar         True   \n",
       "3964   9fd860   source  Level 3       ar         True   \n",
       "36771  5139e9   source  Level 5       ar         True   \n",
       "...       ...      ...      ...      ...          ...   \n",
       "59851  f83dcf   source  Level 4       zh         True   \n",
       "22956  f83dcf   source  Level 4       zh         True   \n",
       "33496  da1fa7   source  Level 3       zh         True   \n",
       "30147  f83dcf   source  Level 4       zh         True   \n",
       "18146  f83dcf   source  Level 4       zh         True   \n",
       "\n",
       "                                         topic_sentences  \n",
       "76970  تحديد العلاقة بين الإحداثي ات القطبية والإحداث...  \n",
       "36893                                Everyday Phrases     \n",
       "36781                             ر سال ة  م ن  الم ن فى  \n",
       "3964   يرسم المنحنى المتجم ع الصاعد والمنحنى المتجم ع...  \n",
       "36771           طريقة كتابة المصفوفات في البرنامج   عملي  \n",
       "...                                                  ...  \n",
       "59851                                         长方体和正方体的体积  \n",
       "22956                                        分数和整数的乘法应用题  \n",
       "33496                                             如何准备种子  \n",
       "30147                                              不进位加法  \n",
       "18146                                             加减法 二   \n",
       "\n",
       "[61517 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cleaning topics\n",
    "levels = {1: 'Level 1', 2: 'Level 2', 3: 'Level 3', 4: 'Level 4', 5: 'Level 5', 6: 'Level 6', 7: 'Level 7', \n",
    "          8: 'Level 8', 9: 'Level 9', 10: 'Level 10', 0: 'Level 0'}\n",
    "topics_cols = ['title']\n",
    "\n",
    "print (f\"\\nCreating and cleaning topic features...\")\n",
    "topic_features = topics_df.copy()[topics_df.has_content == True]\n",
    "topic_features = topic_features.replace ({'level': levels})\n",
    "for col in topics_cols:\n",
    "    topic_features[col] = clean_text(topic_features[col])\n",
    "topic_features.sort_values (by='language', inplace=True)\n",
    "topic_features['topic_sentences'] = topic_features[topics_cols].apply(lambda x: '.'.join(x.dropna().astype(str)), axis=1)\n",
    "topic_features = topic_features.drop(columns=['parent'] + topics_cols) \n",
    "print (f\"\\nCreated 'topic_features'\")\n",
    "display (topic_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4431d8f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:30:55.800660Z",
     "iopub.status.busy": "2023-03-07T19:30:55.798756Z",
     "iopub.status.idle": "2023-03-07T19:31:31.187476Z",
     "shell.execute_reply": "2023-03-07T19:31:31.185678Z"
    },
    "papermill": {
     "duration": 35.41013,
     "end_time": "2023-03-07T19:31:31.199168",
     "exception": false,
     "start_time": "2023-03-07T19:30:55.789038",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Creating and cleaning content features...\n",
      "\n",
      "Created 'content_features'\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "      <td>المفردات والتراكيب</td>\n",
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       "      <td>ar</td>\n",
       "      <td>المفردات والتراكيب</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15012</th>\n",
       "      <td>c_18fc5ad44e5d</td>\n",
       "      <td>构建比较级和最高级</td>\n",
       "      <td>学习如何改装比较级和最高级修饰语\\n\\n</td>\n",
       "      <td>video</td>\n",
       "      <td></td>\n",
       "      <td>zh</td>\n",
       "      <td>构建比较级和最高级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128758</th>\n",
       "      <td>c_d5e56175090a</td>\n",
       "      <td>主谓一致简介</td>\n",
       "      <td>主谓一致指匹配一个句子的主语和动词！这里介绍它是怎么一回事.\\n\\n</td>\n",
       "      <td>video</td>\n",
       "      <td></td>\n",
       "      <td>zh</td>\n",
       "      <td>主谓一致简介</td>\n",
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       "      <th>32558</th>\n",
       "      <td>c_35f7982b250f</td>\n",
       "      <td>双臂和胯部热身运动</td>\n",
       "      <td>在本视频中，您将学会三个热身练习，锻炼您上半身的肌肉，尤其是双臂和胯部的肌肉。</td>\n",
       "      <td>document</td>\n",
       "      <td>\\n双臂和胯部热身运动\\n双臂和胯部热身运动\\n在本视频中，您将学会三个热身练习，锻炼您上...</td>\n",
       "      <td>zh</td>\n",
       "      <td>双臂和胯部热身运动</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15053</th>\n",
       "      <td>c_19148c5d7ab3</td>\n",
       "      <td>出血</td>\n",
       "      <td>微博：http://www.weibo.com/sikana\\r\\n脸书：https://w...</td>\n",
       "      <td>document</td>\n",
       "      <td>\\n出血\\n本视频由红十字会和Sikana共同出品\\n急救\\n出血\\n在这个视频您将学习怎...</td>\n",
       "      <td>zh</td>\n",
       "      <td>出血</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117915</th>\n",
       "      <td>c_c3b030650102</td>\n",
       "      <td>踩单车动作</td>\n",
       "      <td>本视频面向初学者，教给您如何完成足球中的踩单车动作。</td>\n",
       "      <td>video</td>\n",
       "      <td></td>\n",
       "      <td>zh</td>\n",
       "      <td>踩单车动作</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154047 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id                                              title  \\\n",
       "133868  c_de43cff8dd60                                 المفردات والتراكيب   \n",
       "63589   c_699d4bd5d5bb                                 المفردات والتراكيب   \n",
       "17214   c_1cab88377df5                                 المفردات والتراكيب   \n",
       "145550  c_f1b8123b975e                                          الس  ف ر    \n",
       "17202   c_1ca6079deb47  يعي ن معكوس مصفوفة مرب عة من الرتبة الثالثة با...   \n",
       "...                ...                                                ...   \n",
       "15012   c_18fc5ad44e5d                                          构建比较级和最高级   \n",
       "128758  c_d5e56175090a                                             主谓一致简介   \n",
       "32558   c_35f7982b250f                                          双臂和胯部热身运动   \n",
       "15053   c_19148c5d7ab3                                                 出血   \n",
       "117915  c_c3b030650102                                              踩单车动作   \n",
       "\n",
       "                                              description      kind  \\\n",
       "133868                                                     exercise   \n",
       "63589                                                      exercise   \n",
       "17214                                                      exercise   \n",
       "145550  يركب \"رامي\" الطّائرة للمرّة الأولى فيحقّق أمله...     html5   \n",
       "17202                            5ad46b8a6b9064043d8b4158  exercise   \n",
       "...                                                   ...       ...   \n",
       "15012                                学习如何改装比较级和最高级修饰语\\n\\n     video   \n",
       "128758                 主谓一致指匹配一个句子的主语和动词！这里介绍它是怎么一回事.\\n\\n     video   \n",
       "32558             在本视频中，您将学会三个热身练习，锻炼您上半身的肌肉，尤其是双臂和胯部的肌肉。  document   \n",
       "15053   微博：http://www.weibo.com/sikana\\r\\n脸书：https://w...  document   \n",
       "117915                         本视频面向初学者，教给您如何完成足球中的踩单车动作。     video   \n",
       "\n",
       "                                                     text language  \\\n",
       "133868  ما المقصود بعبارة: \"هل هناك من خطب\"؟\\n\\n- هل ه...       ar   \n",
       "63589   ما ضدّ \"لا تملّ\" في الجملة الآتية: \"تُعيدُ الأ...       ar   \n",
       "17214   ما مرادف \"يتمّم\" في ما يلي: \"يَقِفُ أَحْيانًا ...       ar   \n",
       "145550  السَّفَرُ\\n\\nبِحِرْصٍ شَديدٍ، حَزَمَ \"رامي\" أَ...       ar   \n",
       "17202   ![](${☣ CONTENTSTORAGE}/40cf62978d6581e03a5085...       ar   \n",
       "...                                                   ...      ...   \n",
       "15012                                                           zh   \n",
       "128758                                                          zh   \n",
       "32558   \\n双臂和胯部热身运动\\n双臂和胯部热身运动\\n在本视频中，您将学会三个热身练习，锻炼您上...       zh   \n",
       "15053   \\n出血\\n本视频由红十字会和Sikana共同出品\\n急救\\n出血\\n在这个视频您将学习怎...       zh   \n",
       "117915                                                          zh   \n",
       "\n",
       "                                        content_sentences  \n",
       "133868                                 المفردات والتراكيب  \n",
       "63589                                  المفردات والتراكيب  \n",
       "17214                                  المفردات والتراكيب  \n",
       "145550                                          الس  ف ر   \n",
       "17202   يعي ن معكوس مصفوفة مرب عة من الرتبة الثالثة با...  \n",
       "...                                                   ...  \n",
       "15012                                           构建比较级和最高级  \n",
       "128758                                             主谓一致简介  \n",
       "32558                                           双臂和胯部热身运动  \n",
       "15053                                                  出血  \n",
       "117915                                              踩单车动作  \n",
       "\n",
       "[154047 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cleaning contents\n",
    "content_cols = ['title']\n",
    "\n",
    "print (f\"\\nCreating and cleaning content features...\")\n",
    "content_features = contents_df.copy()\n",
    "for col in content_cols:\n",
    "    content_features[col] = clean_text(content_features[col])\n",
    "#content_features['sentences'] =  content_features[content_cols].apply(lambda x: '.'.join(x.dropna().astype(str)), axis=1)\n",
    "content_features.sort_values (by='language', inplace=True)\n",
    "content_features.drop(columns=['copyright_holder', 'license'], inplace=True)\n",
    "content_features['content_sentences'] = content_features[content_cols].apply(lambda x: '.'.join(x.dropna().astype(str)), axis=1) \n",
    "print (f\"\\nCreated 'content_features'\")\n",
    "display(content_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ade38d",
   "metadata": {
    "papermill": {
     "duration": 0.00858,
     "end_time": "2023-03-07T19:31:31.216740",
     "exception": false,
     "start_time": "2023-03-07T19:31:31.208160",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Setting Up Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83813f5b",
   "metadata": {
    "id": "WmkMQLZHaDmd",
    "papermill": {
     "duration": 0.007577,
     "end_time": "2023-03-07T19:31:31.232805",
     "exception": false,
     "start_time": "2023-03-07T19:31:31.225228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Features\n",
    "\n",
    "*   Filter by category (do not use 'aligned', use only for validation)\n",
    "*   Clean strings: ponctuation and special chars (\\n\\t...)\n",
    "*   Merge columns in 'df_topics' from 'topic_cols' into 1 sentence (Sentence1)\n",
    "*   Merge columns in 'df_content' from 'topic_cols' into 1 sentence (Sentence1)\n",
    "*   Merge both datasets using y (correlations.csv)\n",
    "\n",
    "By merging both sentences column using correlations, we get a pair of similar sentences with size of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c9c5a6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:31:31.250242Z",
     "iopub.status.busy": "2023-03-07T19:31:31.249880Z",
     "iopub.status.idle": "2023-03-07T19:31:31.999882Z",
     "shell.execute_reply": "2023-03-07T19:31:31.998873Z"
    },
    "papermill": {
     "duration": 0.761543,
     "end_time": "2023-03-07T19:31:32.002218",
     "exception": false,
     "start_time": "2023-03-07T19:31:31.240675",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "279919"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = topic_features.merge (corr_df, left_on = 'id', right_on='topic_id', how='left')\n",
    "features = features.merge (content_features, left_on='content_ids', right_on='id', how='left')\n",
    "features = features[['topic_sentences', 'content_sentences']]\n",
    "n = len(features)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1495a61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:31:32.023671Z",
     "iopub.status.busy": "2023-03-07T19:31:32.021663Z",
     "iopub.status.idle": "2023-03-07T19:31:32.181106Z",
     "shell.execute_reply": "2023-03-07T19:31:32.179898Z"
    },
    "papermill": {
     "duration": 0.172713,
     "end_time": "2023-03-07T19:31:32.184086",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.011373",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "\n",
    "n = 100000\n",
    "features = features.sample(n=n)\n",
    "del contents_df\n",
    "del topics_df\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a407055f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:31:32.205216Z",
     "iopub.status.busy": "2023-03-07T19:31:32.204822Z",
     "iopub.status.idle": "2023-03-07T19:31:32.210685Z",
     "shell.execute_reply": "2023-03-07T19:31:32.209394Z"
    },
    "papermill": {
     "duration": 0.019152,
     "end_time": "2023-03-07T19:31:32.213138",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.193986",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_features = features[:int(n*0.8)]\n",
    "test_features = features[int(n*0.8+1):]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b98e2e0",
   "metadata": {
    "id": "0866f1f5",
    "papermill": {
     "duration": 0.009103,
     "end_time": "2023-03-07T19:31:32.231478",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.222375",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4d04ab",
   "metadata": {
    "id": "iI-1sqraZhLv",
    "papermill": {
     "duration": 0.008902,
     "end_time": "2023-03-07T19:31:32.249552",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.240650",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e54a6ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:31:32.269446Z",
     "iopub.status.busy": "2023-03-07T19:31:32.269053Z",
     "iopub.status.idle": "2023-03-07T19:31:32.275560Z",
     "shell.execute_reply": "2023-03-07T19:31:32.274408Z"
    },
    "id": "HJou8xoM4H2A",
    "papermill": {
     "duration": 0.021156,
     "end_time": "2023-03-07T19:31:32.279897",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.258741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loaded model training parameters...\n"
     ]
    }
   ],
   "source": [
    "backbone = 'all-MiniLM-L6-v2' #explore also msmarco models and multilanguage: paraphrase-multilingual-mpnet-base-v2\n",
    "#backbone = 'paraphrase-multilingual-mpnet-base-v2'\n",
    "batch_size = 64\n",
    "shuffle = True\n",
    "num_epochs = 4\n",
    "warmup_steps = int(len(features) * num_epochs * 0.2)\n",
    "warmup_steps\n",
    "print (\"\\nLoaded model training parameters...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0da29b8",
   "metadata": {
    "papermill": {
     "duration": 0.008201,
     "end_time": "2023-03-07T19:31:32.297843",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.289642",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Sentence Transformer train\n",
    "\n",
    "- Using 'all-MiniLM-L6-v2' as from discussions it seems to be best model, but may use other models\n",
    "- Using pair of sentences and label\n",
    "- Sentence1: topic_title + topic_description\n",
    "- Sentence2: content title + content description + content text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d913265",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-07T19:31:32.316885Z",
     "iopub.status.busy": "2023-03-07T19:31:32.316552Z",
     "iopub.status.idle": "2023-03-07T19:31:50.049977Z",
     "shell.execute_reply": "2023-03-07T19:31:50.047510Z"
    },
    "papermill": {
     "duration": 17.746869,
     "end_time": "2023-03-07T19:31:50.053577",
     "exception": false,
     "start_time": "2023-03-07T19:31:32.306708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading model all-MiniLM-L6-v2...\n"
     ]
    },
    {
     "data": {
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       "model_id": "2b629cec504f4e4381aa44af0832b594",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting 'features' to proper format...\n",
      "\n",
      "Defining model loss function...\n",
      "\n",
      "All set to train model\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer, InputExample, losses, evaluation\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "print (f\"\\nLoading model {backbone}...\")\n",
    "model = SentenceTransformer(backbone)\n",
    "train_sentences = []\n",
    "matches = len(train_features)\n",
    "\n",
    "print (\"Converting 'features' to proper format...\")\n",
    "for i in range(matches):\n",
    "    train_sentences.append(InputExample(texts=[train_features.iloc[i, 0], train_features.iloc[i, 1]]))\n",
    "\n",
    "train_dataloader = DataLoader(train_sentences, shuffle=shuffle, batch_size=batch_size)\n",
    "\n",
    "#print (\"Setting multi GPU process...\")\n",
    "#pool = model.start_multi_process_pool()\n",
    "\n",
    "print (\"\\nDefining model loss function...\")\n",
    "train_loss = losses.MultipleNegativesRankingLoss(model=model)\n",
    "print (f\"\\nAll set to train model\")"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Training model with 80000 sentences and MultipleNegativesRankingLoss(\n",
      "  (model): SentenceTransformer(\n",
      "    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n",
      "    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
      "    (2): Normalize()\n",
      "  )\n",
      "  (cross_entropy_loss): CrossEntropyLoss()\n",
      ")...\n",
      "0        1.0\n",
      "1        1.0\n",
      "2        1.0\n",
      "3        1.0\n",
      "4        1.0\n",
      "        ... \n",
      "79995    1.0\n",
      "79996    1.0\n",
      "79997    1.0\n",
      "79998    1.0\n",
      "79999    1.0\n",
      "Length: 80000, dtype: float64\n"
     ]
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       "Epoch:   0%|          | 0/4 [00:00<?, ?it/s]"
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     "name": "stdout",
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     "text": [
      "Model saved.\n"
     ]
    }
   ],
   "source": [
    "print (f\"\\nTraining model with {warmup_steps} sentences and {train_loss}...\")\n",
    "\n",
    "labels = pd.Series (np.ones(matches))\n",
    "print (labels)\n",
    "#evaluator = evaluation.MSEEvaluator(features.iloc[:, 0].to_list(), features.iloc[:, 1].to_list(), teacher_model=model)\n",
    "model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
    "          epochs=num_epochs,\n",
    "          output_path=f\"/kaggle/working/ST-{backbone}-trained\",\n",
    "          warmup_steps=warmup_steps)\n",
    "\n",
    "print (\"Model saved.\")"
   ]
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       "1.0"
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   ],
   "source": [
    "labels2 = pd.Series (np.ones(len(test_features)))\n",
    "evaluator = evaluation.BinaryClassificationEvaluator(test_features.iloc[:, 0].to_list(), test_features.iloc[:, 1].to_list(), labels=labels2)\n",
    "model.evaluate (evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79032197",
   "metadata": {
    "execution": {
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   "source": [
    "# MSE: -0.4197012633085251 4 epochs 64 batch size (mas agora crasha sem memória)\n",
    "# MSE: -0.4188443999737501 2 epochs (mas agora crasha sem memória)\n",
    "# MSE: -0.8855 2 epochs 32 batch size"
   ]
  }
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