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-{"cells":[{"cell_type":"markdown","metadata":{"id":"6aLBz-6S8VD8"},"source":["# Sentiment Analysis with Hugging Face"]},{"cell_type":"markdown","metadata":{"id":"6iRdGzUs8VD_"},"source":["Hugging Face is an open-source and platform provider of machine learning technologies. You can use install their package to access some interesting pre-built models to use them directly or to fine-tune (retrain it on your dataset leveraging the prior knowledge coming with the first training), then host your trained models on the platform, so that you may use them later on other devices and apps.\n","\n","Please, [go to the website and sign-in](https://huggingface.co/) to access all the features of the platform.\n","\n","[Read more about Text classification with Hugging Face](https://huggingface.co/tasks/text-classification)\n","\n","The Hugging face models are Deep Learning based, so will need a lot of computational GPU power to train them. Please use [Colab](https://colab.research.google.com/) to do it, or your other GPU cloud provider, or a local machine having NVIDIA GPU."]},{"cell_type":"markdown","metadata":{"id":"18VUhSFb8VEA"},"source":["## Application of Hugging Face Text classification model Fune-tuning"]},{"cell_type":"markdown","metadata":{"id":"Jo22OfiO8VEA"},"source":["Find below a simple example, with just `3 epochs of fine-tuning`.\n","\n","Read more about the fine-tuning concept : [here](https://deeplizard.com/learn/video/5T-iXNNiwIs#:~:text=Fine%2Dtuning%20is%20a%20way,perform%20a%20second%20similar%20task.)"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i8YU9bf68VEB","executionInfo":{"status":"ok","timestamp":1693241792055,"user_tz":0,"elapsed":72594,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"91cd8382-2cc6-4bf6-8d65-d8985c105055"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n","Collecting dill<0.3.8,>=0.3.0 (from datasets)\n","  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pandas in 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(from huggingface_hub) (2.31.0)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.7.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n","Collecting neattext\n","  Downloading neattext-0.1.3-py3-none-any.whl (114 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.7/114.7 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: neattext\n","Successfully installed neattext-0.1.3\n"]}],"source":["#Install the datasets library\n","!pip install datasets\n","!pip install sentencepiece\n","!pip install transformers datasets\n","!pip install transformers[torch]\n","!pip install accelerate\n","!pip install accelerate>=0.20.1\n","!pip install huggingface_hub\n","!pip install  -q transformers datasets\n","!pip install neattext"]},{"cell_type":"code","source":["pip install datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KUv5OMAuF6FA","executionInfo":{"status":"ok","timestamp":1693242101088,"user_tz":0,"elapsed":17331,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"3926ddd6-9d79-4879-c23c-5f9080d938f2"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.4)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n","Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n","Requirement already satisfied: requests>=2.19.0 in 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/usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.2)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"]}]},{"cell_type":"markdown","source":["### IMPORTING PACKAGES"],"metadata":{"id":"uAoyYIRY9cF2"}},{"cell_type":"code","execution_count":9,"metadata":{"id":"qvY8Tu2d8VEC","executionInfo":{"status":"ok","timestamp":1693242918865,"user_tz":0,"elapsed":445,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["import os\n","import pandas as pd\n","from datasets import load_dataset\n","from sklearn.model_selection import train_test_split\n","\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","\n","from wordcloud import WordCloud\n","import neattext.functions as nfx\n","import re\n","\n","import nltk\n","from nltk.corpus import stopwords\n","\n","import io"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"MffMoeGW8VEC","executionInfo":{"status":"ok","timestamp":1693242150494,"user_tz":0,"elapsed":529,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["# Disabe W&B\n","os.environ[\"WANDB_DISABLED\"] = \"true\""]},{"cell_type":"code","source":["from google.colab import files\n","uploaded = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":108},"id":"J8nPD2vYImd-","executionInfo":{"status":"ok","timestamp":1693243003833,"user_tz":0,"elapsed":43846,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"ddd1a1ac-70bd-4bbd-9ac2-a7d84efb6516"},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-9d863f59-3e2c-458f-bc8b-f14d72940f21\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-9d863f59-3e2c-458f-bc8b-f14d72940f21\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Test.csv to Test.csv\n","Saving Train.csv to Train (1).csv\n"]}]},{"cell_type":"code","source":["\n","df_train = pd.read_csv(io.BytesIO(uploaded['Train (1).csv']))\n","\n","df_test = pd.read_csv(io.BytesIO(uploaded['Test.csv']))"],"metadata":{"id":"_oIiNNtII7zv","executionInfo":{"status":"ok","timestamp":1693243339521,"user_tz":0,"elapsed":437,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"execution_count":14,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"E0IkAyO18VEC"},"source":["#### LOADING DATASET"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"qDcabrYn8VEC","executionInfo":{"status":"ok","timestamp":1693243352623,"user_tz":0,"elapsed":468,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["# Load the dataset and display some values\n","#df_train = pd.read_csv('../data/Train.csv')\n","\n","# A way to eliminate rows containing NaN values\n","df_train = df_train[~df_train.isna().any(axis=1)]\n","\n","\n","# Load the dataset and display some values\n","#df_test = pd.read_csv('../data/Test.csv')\n","\n","# A way to eliminate rows containing NaN values\n","df_test = df_test[~df_test.isna().any(axis=1)]"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"eP2GT1KX8VED","executionInfo":{"status":"ok","timestamp":1693243422326,"user_tz":0,"elapsed":422,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["##creating a copy\n","\n","train_data= df_train.copy()\n","test_data= df_test.copy()"]},{"cell_type":"markdown","metadata":{"id":"ueV8pzSM8VED"},"source":["## CRISP-DM Framework\n","\n","- Data Understanding\n","- Data Preparation\n","- Modelling\n","- Evaluation\n","- Deployment\n"]},{"cell_type":"markdown","metadata":{"id":"332UWGID8VED"},"source":["#### DATA UNDERSTANDING"]},{"cell_type":"markdown","metadata":{"id":"V8GYA0rq8VED"},"source":["##### EXPLORATORY DATA ANALYSIS (EDA)"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"9GdrF39R8VED","executionInfo":{"status":"ok","timestamp":1693243444251,"user_tz":0,"elapsed":17,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"952d3e55-5b8d-4ce3-cb83-1b770a85c27c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      tweet_id                                          safe_text  label  \\\n","6132  GHFBXAJY  “<user> On avg, ppl who complain live longer- ...    0.0   \n","4656  Q46D5JHV  Further more, not vaccinating is also a public...    1.0   \n","9781  BPI3X7HR  <url> Whistle blowing scientists report cdc co...   -1.0   \n","2022  2QK6T0YW  It's almost time!!! #RebootNutrition live chat...    0.0   \n","7567  ZC63FQIO                       Vaccinate your kids y'all!!!    1.0   \n","\n","      agreement  \n","6132        1.0  \n","4656        1.0  \n","9781        1.0  \n","2022        1.0  \n","7567        1.0  "],"text/html":["\n","  <div id=\"df-795d0f7c-37c1-472e-a8b9-e47e379e2dfa\" class=\"colab-df-container\">\n","    <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>tweet_id</th>\n","      <th>safe_text</th>\n","      <th>label</th>\n","      <th>agreement</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>6132</th>\n","      <td>GHFBXAJY</td>\n","      <td>“&lt;user&gt; On avg, ppl who complain live longer- ...</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4656</th>\n","      <td>Q46D5JHV</td>\n","      <td>Further more, not vaccinating is also a public...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>9781</th>\n","      <td>BPI3X7HR</td>\n","      <td>&lt;url&gt; Whistle blowing scientists report cdc co...</td>\n","      <td>-1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2022</th>\n","      <td>2QK6T0YW</td>\n","      <td>It's almost time!!! #RebootNutrition live chat...</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7567</th>\n","      <td>ZC63FQIO</td>\n","      <td>Vaccinate your kids y'all!!!</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-795d0f7c-37c1-472e-a8b9-e47e379e2dfa')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-795d0f7c-37c1-472e-a8b9-e47e379e2dfa button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Perry's only 2 gestures of ...\n","1533  AJ1BWJMD  1,800 people die of MALARIA each, day; malaria..."],"text/html":["\n","  <div id=\"df-56b2827b-d4a7-4cb0-aa91-63c0f75be8a1\" class=\"colab-df-container\">\n","    <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>tweet_id</th>\n","      <th>safe_text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4386</th>\n","      <td>UCFFCW3Z</td>\n","      <td>CDC announced 2014s flu vaccine is ineffective...</td>\n","    </tr>\n","    <tr>\n","      <th>3647</th>\n","      <td>P5NOOKR1</td>\n","      <td>Health Department reacts to CDC’s flu vaccine ...</td>\n","    </tr>\n","    <tr>\n","      <th>1110</th>\n","      <td>7LHO50GN</td>\n","      <td>&lt;user&gt; Okay, whose Kid made the #immunityneckl...</td>\n","    </tr>\n","    <tr>\n","      <th>3339</th>\n","      <td>N3S5E7HR</td>\n","      <td>Macabre, sadistic. Perry's only 2 gestures of ...</td>\n","    </tr>\n","    <tr>\n","      <th>1533</th>\n","      <td>AJ1BWJMD</td>\n","      <td>1,800 people die of MALARIA each, day; malaria...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56b2827b-d4a7-4cb0-aa91-63c0f75be8a1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-56b2827b-d4a7-4cb0-aa91-63c0f75be8a1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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KB\n","the info df_train dataset are:  \n","\n"," None \n","\n"," ------------------------------------------------------------\n","<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 5176 entries, 0 to 5176\n","Data columns (total 2 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   tweet_id   5176 non-null   object\n"," 1   safe_text  5176 non-null   object\n","dtypes: object(2)\n","memory usage: 121.3+ KB\n","the info df_test dataset are:  \n","\n"," None \n","\n"," ------------------------------------------------------------\n"]}],"source":["#Checking Info for our train and test data\n","data=[train_data, test_data]\n","names=[\"df_train\", \"df_test\"]\n","\n","for m, i in zip(data, names):\n","  print(f\"the info\", i,\"dataset are: \", \"\\n\\n\", m.info(), \"\\n\\n\", \"---\"*20 )"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzYJXhAL8VEE","executionInfo":{"status":"ok","timestamp":1693243458144,"user_tz":0,"elapsed":454,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"d86d5e11-0c79-48be-9a93-2e25b190e76b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0.0    4908\n"," 1.0    4053\n","-1.0    1038\n","Name: label, dtype: int64"]},"metadata":{},"execution_count":20}],"source":["# We look at the number of positive, negative and neutral reviews\n","train_data.label.value_counts()"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"eK4cCoh_8VEE","executionInfo":{"status":"ok","timestamp":1693243462847,"user_tz":0,"elapsed":557,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"0cbbe33e-04d5-472b-97b3-7efd8411840b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plot the distribution of labels\n","label_counts = train_data['label'].value_counts()\n","plt.bar(label_counts.index, label_counts.values)\n","plt.xlabel('Label')\n","plt.ylabel('Count')\n","plt.title('Distribution of Labels')\n","plt.show()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_doxHCc48VEE","executionInfo":{"status":"ok","timestamp":1693243475054,"user_tz":0,"elapsed":424,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"19f215c4-b3ab-49f3-8cfb-c3f9d9b57dcf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.000000    5866\n","0.666667    3894\n","0.333333     239\n","Name: agreement, dtype: int64"]},"metadata":{},"execution_count":22}],"source":["# The count of the 'agreement'\n","train_data.agreement.value_counts()"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"r8fS5CFj8VEF","executionInfo":{"status":"ok","timestamp":1693243477856,"user_tz":0,"elapsed":727,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"b188cd82-d58f-432c-a002-5e2285567295"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plot the distribution of 'agreement'\n","plt.hist(train_data['agreement'])\n","plt.xlabel('Agreement')\n","plt.ylabel('Count')\n","plt.title('Distribution of Agreement')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"5pAf2_bg8VEF"},"source":["The distribution of sentiments in the dataset, as depicted by the count plot, shows the prevalence of different sentiment labels within the Twitter posts related to COVID-19 vaccinations.\n","* Sentiment Label 0 (Neutral):\n","The sentiment label \"0\" (neutral) has the highest count, with approximately 5000 instances. This suggests that a significant portion of the collected tweets exhibit a neutral sentiment when it comes to discussing COVID-19 vaccinations. Neutral sentiments often indicate that the tweets may not strongly express positive or negative opinions but rather present factual information or observations.\n","\n","* Sentiment Label 1 (Positive):\n","The sentiment label \"1\" (positive) follows with around 4000 instances. This indicates that a substantial number of tweets show a positive sentiment towards COVID-19 vaccinations. These tweets might express support for vaccinations, share positive experiences, or provide information about vaccination availability and benefits.\n","\n","* Sentiment Label -1 (Negative):\n","The sentiment label \"-1\" (negative) has the lowest count, with approximately 1000 instances. This suggests that a relatively smaller portion of the collected tweets exhibit a negative sentiment towards COVID-19 vaccinations. Negative sentiments can encompass concerns, skepticism, or criticism about the vaccines, their safety, or potential side effects."]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QPb9SyEi8VEF","executionInfo":{"status":"ok","timestamp":1693243485659,"user_tz":0,"elapsed":498,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"e2ac51ae-aea8-4410-dca5-1ec2f17ac051"},"outputs":[{"output_type":"stream","name":"stdout","text":["Correlation: 0.13815479087588003\n"]}],"source":["# Calculate the correlation between 'label' and 'agreement'\n","correlation = df_train['label'].corr(df_train['agreement'])\n","\n","# Print the correlation value\n","print(f\"Correlation: {correlation}\")"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QBvcipxj8VEF","executionInfo":{"status":"ok","timestamp":1693243489179,"user_tz":0,"elapsed":12,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"72f047c0-f6ba-49de-81b7-be1975559767"},"outputs":[{"output_type":"stream","name":"stdout","text":["max review_legnth : 154\n","min review_legnth : 3\n"]}],"source":["#Checking the length of the reviews\n","review_legnth = train_data.safe_text.str.len()\n","\n","max(review_legnth)\n","\n","#Legnth of the shortest review\n","min(review_legnth)\n","\n","print(f\"max review_legnth : {max(review_legnth)}\")\n","print(f\"min review_legnth : {min(review_legnth)}\")"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":440},"id":"NYhj-rqj8VEF","executionInfo":{"status":"ok","timestamp":1693243495944,"user_tz":0,"elapsed":3351,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"66df329e-a581-42f4-c8d5-9b883e4f881b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[('<url>', 4612), ('<user>', 4517), ('to', 3407), ('the', 3388), ('of', 2196), ('a', 2133), ('in', 1897), ('and', 1827), ('measles', 1747), ('I', 1604)]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#Having a word count\n","\n","# Concatenate all the 'safe_text' into a single string\n","text = ' '.join(df_train['safe_text'])\n","\n","# Split the text into words\n","words = text.split()\n","\n","# Count the frequency of each word\n","word_counts = Counter(words)\n","\n","# Display the most common words\n","print(word_counts.most_common(10))\n","\n","# Generate the word cloud with a white background\n","cloud_two_cities = WordCloud(width=800, height=400, background_color='white').generate(text)\n","\n","# Display the word cloud\n","plt.figure(figsize=(8, 5))\n","plt.imshow(cloud_two_cities, interpolation='bilinear')\n","plt.axis('off')\n","plt.tight_layout(pad=1)\n","plt.show()\n"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"1UlTZUbr8VEG","executionInfo":{"status":"ok","timestamp":1693243519430,"user_tz":0,"elapsed":1254,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"459ea8c6-8177-4e03-b65f-a3345a909343"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Calculate the length of each text in 'safe_text'\n","text_lengths = train_data['safe_text'].apply(len)\n","\n","# Plot the distribution of text lengths\n","plt.hist(text_lengths)\n","plt.xlabel('Text Length')\n","plt.ylabel('Count')\n","plt.title('Distribution of Text Lengths')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"xG5vuV5G8VEG"},"source":["#### DATA CLEANING"]},{"cell_type":"markdown","metadata":{"id":"sZqasFji8VEG"},"source":["Issues to treat:\n","\n","\n","*   Remove unneccesary columns.\n","*   Remove emojis and other characters from safe text column.\n","*   Remove punctuations from the safe text column\n","*   Changing all text to lower cases.\n"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tic_qCRk8VEG","executionInfo":{"status":"ok","timestamp":1693243531994,"user_tz":0,"elapsed":433,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"3241e0a0-9aa8-4780-a0e6-fe0794f78e01"},"outputs":[{"output_type":"stream","name":"stdout","text":["the missing values in the df_train dataset are:  \n","\n"," tweet_id     0\n","safe_text    0\n","label        0\n","agreement    0\n","dtype: int64 \n","\n"," ------------------------------------------------------------\n","the missing values in the df_test dataset are:  \n","\n"," tweet_id     0\n","safe_text    0\n","dtype: int64 \n","\n"," ------------------------------------------------------------\n"]}],"source":["data=[train_data, test_data]\n","names=[\"df_train\", \"df_test\"]\n","\n","for m, i in zip(data, names):\n","  print(f\"the missing values in the\", i,\"dataset are: \", \"\\n\\n\", m.isna().sum(), \"\\n\\n\", \"---\"*20 )"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GO6CoLSm8VEG","executionInfo":{"status":"ok","timestamp":1693243538302,"user_tz":0,"elapsed":509,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"755b887d-229b-4b6d-eb42-73d634558a50"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":30}],"source":["#check for duplicates\n","train_data.duplicated().sum()"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"6MYV4dIT8VEG","executionInfo":{"status":"ok","timestamp":1693243541645,"user_tz":0,"elapsed":419,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["import string"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5tVUGXI_8VEG","executionInfo":{"status":"ok","timestamp":1693243544139,"user_tz":0,"elapsed":676,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"f5051e0d-894d-4bc5-e6cd-10596b7b24da"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    me amp the big homie meanboy3000 meanboy mb mb...\n","1    im 100 thinking of devoting my career to provi...\n","2    whatcausesautism vaccines do not vaccinate you...\n","3    i mean if they immunize my kid with something ...\n","4    thanks to user catch me performing at la nuit ...\n","5    user a nearly 67 year old study when mental he...\n","6    study of more than 95000 kids finds no link be...\n","7                      psa vaccinate your fucking kids\n","8    coughing extra on the shuttle and everyone thi...\n","9    aids vaccine created at oregon health amp scie...\n","Name: safe_text, dtype: object"]},"metadata":{},"execution_count":32}],"source":["# Clean the 'safe_text' column (example: remove URLs and special characters)\n","train_data['safe_text'] = train_data['safe_text'].str.replace(r'<url>', '')  # Remove <url> tag\n","test_data['safe_text'] = test_data['safe_text'].str.replace(r'<url>', '')  # Remove <url> tag\n","\n","# Remove emojis and other special characters\n","emojis = re.compile(r'[^\\w\\s@#$%^*()<>/|}{~:&]')\n","train_data[\"safe_text\"] = train_data[\"safe_text\"].str.replace(emojis, '')\n","test_data[\"safe_text\"] = test_data[\"safe_text\"].str.replace(emojis, '')\n","\n","# # Remove punctuation\n","punctuation = string.punctuation\n","train_data[\"safe_text\"] = train_data[\"safe_text\"].str.translate(str.maketrans('', '', punctuation))\n","test_data[\"safe_text\"] = test_data[\"safe_text\"].str.translate(str.maketrans('', '', punctuation))\n","\n","# remove hashtags\n","train_data['safe_text'] = train_data['safe_text'].apply(nfx.remove_hashtags)\n","test_data['safe_text'] = test_data['safe_text'].apply(nfx.remove_hashtags)\n","\n","# Turn the safe_text column into lowercase\n","train_data[\"safe_text\"] = train_data[\"safe_text\"].str.lower()\n","test_data[\"safe_text\"] = test_data[\"safe_text\"].str.lower()\n","\n","# remove multiple white spaces\n","def stripSpace(text):\n","    return text.strip()\n","train_data['safe_text'] = train_data['safe_text'].apply(nfx.remove_multiple_spaces)\n","train_data['safe_text'] = train_data['safe_text'].apply(stripSpace)\n","\n","# remove RT and user handles\n","def removeRT(text):\n","    return text.replace(\"RT\" , \"\")\n","train_data['safe_text'] = train_data['safe_text'].apply(lambda x: nfx.remove_userhandles(x))\n","train_data['safe_text'] = train_data['safe_text'].apply(removeRT)\n","\n","#Preview of the safe text column\n","train_data['safe_text'].head(10)"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nfYuLTrE8VEH","executionInfo":{"status":"ok","timestamp":1693243552155,"user_tz":0,"elapsed":463,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"9199e457-ec22-4343-c39b-ed590ecaa062"},"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":33}],"source":["#REMOVING STOPWORDS\n","# Download the stop words (only required for the first time)\n","nltk.download('stopwords')"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"3Z6IxElC8VEH","executionInfo":{"status":"ok","timestamp":1693243555748,"user_tz":0,"elapsed":491,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}}},"outputs":[],"source":["# Remove stop words\n","stop_words = set(stopwords.words('english'))\n","train_data['safe_text'] = train_data['safe_text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n","test_data['safe_text'] = test_data['safe_text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))\n"]},{"cell_type":"markdown","metadata":{"id":"qKmpLjRp8VEH"},"source":["# Export DataFrame as CSV"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":390},"id":"ex6P20El8VEH","executionInfo":{"status":"error","timestamp":1693243559047,"user_tz":0,"elapsed":13,"user":{"displayName":"Michael Dzifa Kumassah","userId":"10952293488118390561"}},"outputId":"75c289ef-944b-4609-f610-694f2a806765"},"outputs":[{"output_type":"error","ename":"OSError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)","\u001b[0;32m<ipython-input-35-c96b928879b6>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Save df_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../data/train_data.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Save df_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../data/test_data.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3718\u001b[0m         )\n\u001b[1;32m   3719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3720\u001b[0;31m         return DataFrameRenderer(formatter).to_csv(\n\u001b[0m\u001b[1;32m   3721\u001b[0m             \u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3722\u001b[0m             \u001b[0mlineterminator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlineterminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m   1187\u001b[0m             \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m         )\n\u001b[0;32m-> 1189\u001b[0;31m         \u001b[0mcsv_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcreated_buffer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/formats/csvs.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \"\"\"\n\u001b[1;32m    240\u001b[0m         \u001b[0;31m# apply compression and byte/text conversion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         with get_handle(\n\u001b[0m\u001b[1;32m    242\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    732\u001b[0m     \u001b[0;31m# Only for write methods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m\"r\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m         \u001b[0mcheck_parent_directory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    595\u001b[0m     \u001b[0mparent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 597\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mrf\"Cannot save file into a non-existent directory: '{parent}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: '../data'"]}],"source":["# Save df_train\n","train_data.to_csv('../data/train_data.csv', index=False)\n","\n","# Save df_test\n","test_data.to_csv('../data/test_data.csv', index=False)"]},{"cell_type":"markdown","metadata":{"id":"D6sE_xje8VEH"},"source":["#### IMPORTING CLEANED DATASET"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5Ewd0EbG8VEH"},"outputs":[],"source":["# Load the dataset and display some values\n","df = pd.read_csv('../data/train_data.csv')\n","\n","# A way to eliminate rows containing NaN values\n","df = df[~df.isna().any(axis=1)]"]},{"cell_type":"markdown","metadata":{"id":"YHpIS9L98VEH"},"source":[]},{"cell_type":"markdown","metadata":{"id":"DRv4fVbo8VEH"},"source":[]},{"cell_type":"markdown","metadata":{"id":"7Z855CAX8VEI"},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"67EkUsZu8VEI"},"outputs":[],"source":["# Disabe W&B\n","os.environ[\"WANDB_DISABLED\"] = \"true\""]},{"cell_type":"markdown","metadata":{"id":"6DnhJFTj8VEI"},"source":["I manually split the training set to have a training subset ( a dataset the model will learn on), and an evaluation subset ( a dataset the model with use to compute metric scores to help use to avoid some training problems like [the overfitting](https://www.ibm.com/cloud/learn/overfitting) one ).\n","\n","There are multiple ways to do split the dataset. You'll see two commented line showing you another one."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2bbkYj4U8VEI"},"outputs":[],"source":["# Split the train data => {train, eval}\n","train, eval = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YShmYGFK8VEI","outputId":"1f1f99a5-6c42-4e1e-8a26-19b8d4e32296"},"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>tweet_id</th>\n","      <th>safe_text</th>\n","      <th>label</th>\n","      <th>agreement</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>9305</th>\n","      <td>YMRMEDME</td>\n","      <td>Mickey's Measles has gone international &lt;url&gt;</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3907</th>\n","      <td>5GV8NEZS</td>\n","      <td>S1256 [NEW] Extends exemption from charitable ...</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>795</th>\n","      <td>EI10PS46</td>\n","      <td>&lt;user&gt;  your ignorance on vaccines isn't just ...</td>\n","      <td>1.0</td>\n","      <td>0.666667</td>\n","    </tr>\n","    <tr>\n","      <th>5793</th>\n","      <td>OM26E6DG</td>\n","      <td>Pakistan partly suspends polio vaccination pro...</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3431</th>\n","      <td>NBBY86FX</td>\n","      <td>In other news I've gone up like 1000 mmr</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      tweet_id                                          safe_text  label  \\\n","9305  YMRMEDME      Mickey's Measles has gone international <url>    0.0   \n","3907  5GV8NEZS  S1256 [NEW] Extends exemption from charitable ...    0.0   \n","795   EI10PS46  <user>  your ignorance on vaccines isn't just ...    1.0   \n","5793  OM26E6DG  Pakistan partly suspends polio vaccination pro...    0.0   \n","3431  NBBY86FX           In other news I've gone up like 1000 mmr    0.0   \n","\n","      agreement  \n","9305   1.000000  \n","3907   1.000000  \n","795    0.666667  \n","5793   1.000000  \n","3431   1.000000  "]},"execution_count":123,"metadata":{},"output_type":"execute_result"}],"source":["train.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ryxxwwmo8VEJ","outputId":"a3a6e82f-4955-4085-8618-49f52f982894"},"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>tweet_id</th>\n","      <th>safe_text</th>\n","      <th>label</th>\n","      <th>agreement</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>6571</th>\n","      <td>R7JPIFN7</td>\n","      <td>Children's Museum of Houston to Offer Free Vac...</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1754</th>\n","      <td>2DD250VN</td>\n","      <td>&lt;user&gt; no. I was properly immunized prior to t...</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3325</th>\n","      <td>ESEVBTFN</td>\n","      <td>&lt;user&gt; thx for posting vaccinations are impera...</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1485</th>\n","      <td>S17ZU0LC</td>\n","      <td>This Baby Is Exactly Why Everyone Needs To Vac...</td>\n","      <td>1.0</td>\n","      <td>0.666667</td>\n","    </tr>\n","    <tr>\n","      <th>4175</th>\n","      <td>IIN5D33V</td>\n","      <td>Meeting tonight, 8:30pm in room 322 of the stu...</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      tweet_id                                          safe_text  label  \\\n","6571  R7JPIFN7  Children's Museum of Houston to Offer Free Vac...    1.0   \n","1754  2DD250VN  <user> no. I was properly immunized prior to t...    1.0   \n","3325  ESEVBTFN  <user> thx for posting vaccinations are impera...    1.0   \n","1485  S17ZU0LC  This Baby Is Exactly Why Everyone Needs To Vac...    1.0   \n","4175  IIN5D33V  Meeting tonight, 8:30pm in room 322 of the stu...    1.0   \n","\n","      agreement  \n","6571   1.000000  \n","1754   1.000000  \n","3325   1.000000  \n","1485   0.666667  \n","4175   1.000000  "]},"execution_count":124,"metadata":{},"output_type":"execute_result"}],"source":["eval.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eNw4jnbN8VEJ","outputId":"e201bfa0-59ad-4bcd-99f4-7a576b308914"},"outputs":[{"name":"stdout","output_type":"stream","text":["new dataframe shapes: train is (7999, 4), eval is (2000, 4)\n"]}],"source":["print(f\"new dataframe shapes: train is {train.shape}, eval is {eval.shape}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BWPpGJwe8VEJ"},"outputs":[],"source":["# Save splitted subsets\n","train.to_csv(\"../data/train_subset.csv\", index=False)\n","eval.to_csv(\"../data/eval_subset.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"referenced_widgets":["128e6ea009d64015aeec620f344c6f59","5b86b568f7e54068ad8e01ed20e5c6c4","4b43a35acc154b2e8788a280200433d6","a3be03a63e564dd4a69781edeb82e0b8"]},"id":"VtRC1_by8VEK","outputId":"e9161577-c774-4677-a151-f003849084b5"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"128e6ea009d64015aeec620f344c6f59","version_major":2,"version_minor":0},"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5b86b568f7e54068ad8e01ed20e5c6c4","version_major":2,"version_minor":0},"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4b43a35acc154b2e8788a280200433d6","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a3be03a63e564dd4a69781edeb82e0b8","version_major":2,"version_minor":0},"text/plain":["Generating eval split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["dataset = load_dataset('csv',\n","                        data_files={'train': '../data/train_subset.csv',\n","                        'eval': '../data/eval_subset.csv'}, encoding = \"ISO-8859-1\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0iKhc1ce8VEK"},"outputs":[],"source":["from transformers import AutoTokenizer\n","tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"referenced_widgets":["5c6dd00cc99943908957fa83d6333827","ab64e8e296b04ee397e8183195380179","e3e90fc5541e43cc875d201b8e32df60","2d5cadc5f30440578d23a3f6232ec45e"]},"id":"8FtV-rRo8VEL","outputId":"86703573-5d63-428d-fa96-f0e12c5294ac"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5c6dd00cc99943908957fa83d6333827","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ab64e8e296b04ee397e8183195380179","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e3e90fc5541e43cc875d201b8e32df60","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2d5cadc5f30440578d23a3f6232ec45e","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["def transform_labels(label):\n","\n","    label = label['label']\n","    num = 0\n","    if label == -1: #'Negative'\n","        num = 0\n","    elif label == 0: #'Neutral'\n","        num = 1\n","    elif label == 1: #'Positive'\n","        num = 2\n","\n","    return {'labels': num}\n","\n","def tokenize_data(example):\n","    return tokenizer(example['safe_text'], padding='max_length')\n","\n","# Change the tweets to tokens that the models can exploit\n","dataset = dataset.map(tokenize_data, batched=True)\n","\n","# Transform\tlabels and remove the useless columns\n","remove_columns = ['tweet_id', 'label', 'safe_text', 'agreement']\n","dataset = dataset.map(transform_labels, remove_columns=remove_columns)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R3vqr00T8VEL","outputId":"45b82989-5caa-4d88-b133-386992e50c8b"},"outputs":[{"data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n","        num_rows: 7999\n","    })\n","    eval: Dataset({\n","        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n","        num_rows: 2000\n","    })\n","})"]},"execution_count":130,"metadata":{},"output_type":"execute_result"}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Iy1tdoH28VEL"},"outputs":[],"source":["# dataset['train']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GtOULtzR8VEL","outputId":"eae84b53-ba75-404a-b677-e2e2be76d6cf"},"outputs":[{"ename":"ValueError","evalue":"--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation strategy: no\n- Save strategy: steps","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5404\\1591288342.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Configure the trianing parameters like `num_train_epochs`:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# the number of time the model will repeat the training loop over the dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtraining_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"test_trainer\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_train_epochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mload_best_model_at_end\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\transformers\\training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, sharded_ddp, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memory, skip_memory_metrics, use_legacy_prediction_loop, push_to_hub, resume_from_checkpoint, hub_model_id, hub_strategy, hub_token, hub_private_repo, hub_always_push, gradient_checkpointing, include_inputs_for_metrics, fp16_backend, push_to_hub_model_id, push_to_hub_organization, push_to_hub_token, mp_parameters, auto_find_batch_size, full_determinism, torchdynamo, ray_scope, ddp_timeout, torch_compile, torch_compile_backend, torch_compile_mode, dispatch_batches)\u001b[0m\n","\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\transformers\\training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1297\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_best_model_at_end\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluation_strategy\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_strategy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1299\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m   1300\u001b[0m                     \u001b[1;34m\"--load_best_model_at_end requires the save and eval strategy to match, but found\\n- Evaluation \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m                     \u001b[1;34mf\"strategy: {self.evaluation_strategy}\\n- Save strategy: {self.save_strategy}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mValueError\u001b[0m: --load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation strategy: no\n- Save strategy: steps"]}],"source":["from transformers import TrainingArguments\n","\n","# Configure the trianing parameters like `num_train_epochs`:\n","# the number of time the model will repeat the training loop over the dataset\n","training_args = TrainingArguments(\"test_trainer\", num_train_epochs=3000, load_best_model_at_end=True,)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_lOHKLBs8VEL","outputId":"dedd0801-040d-412b-f1ce-dbe626f9fc9b"},"outputs":[{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\n","- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.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":["from transformers import AutoModelForSequenceClassification\n","\n","# Loading a pretrain model while specifying the number of labels in our dataset for fine-tuning\n","model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IpDKH2LT8VEM"},"outputs":[],"source":["train_dataset = dataset['train'].shuffle(seed=10) #.select(range(40000)) # to select a part\n","eval_dataset = dataset['eval'].shuffle(seed=10)\n","\n","## other way to split the train set ... in the range you must use:\n","# # int(num_rows*.8 ) for [0 - 80%] and  int(num_rows*.8 ),num_rows for the 20% ([80 - 100%])\n","# train_dataset = dataset['train'].shuffle(seed=10).select(range(40000))\n","# eval_dataset = dataset['train'].shuffle(seed=10).select(range(40000, 41000))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s-OQPzBh8VEM"},"outputs":[],"source":["from transformers import Trainer\n","\n","trainer = Trainer(\n","    model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WXZqWcsi8VEM","outputId":"331e51aa-6333-411a-9be2-8b8dc174d5b5"},"outputs":[{"name":"stderr","output_type":"stream","text":["***** Running training *****\n","  Num examples = 7999\n","  Num Epochs = 3\n","  Instantaneous batch size per device = 8\n","  Total train batch size (w. parallel, distributed & accumulation) = 8\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 3000\n","                                                   \n","  1%|          | 16/3000 [4:25:07<6:59:23,  8.43s/it] Saving model checkpoint to test_trainer/checkpoint-500\n","Configuration saved in test_trainer/checkpoint-500/config.json\n"]},{"name":"stdout","output_type":"stream","text":["{'loss': 0.7607, 'learning_rate': 4.166666666666667e-05, 'epoch': 0.5}\n"]},{"name":"stderr","output_type":"stream","text":["Model weights saved in test_trainer/checkpoint-500/pytorch_model.bin\n","                                                     \n","  1%|          | 16/3000 [7:16:40<6:59:23,  8.43s/it]  Saving model checkpoint to test_trainer/checkpoint-1000\n","Configuration saved in test_trainer/checkpoint-1000/config.json\n"]},{"name":"stdout","output_type":"stream","text":["{'loss': 0.6572, 'learning_rate': 3.3333333333333335e-05, 'epoch': 1.0}\n"]},{"name":"stderr","output_type":"stream","text":["Model weights saved in test_trainer/checkpoint-1000/pytorch_model.bin\n"]},{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn [18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:1498\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1493\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_wrapped \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m   1495\u001b[0m inner_training_loop \u001b[39m=\u001b[39m find_executable_batch_size(\n\u001b[1;32m   1496\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inner_training_loop, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_train_batch_size, args\u001b[39m.\u001b[39mauto_find_batch_size\n\u001b[1;32m   1497\u001b[0m )\n\u001b[0;32m-> 1498\u001b[0m \u001b[39mreturn\u001b[39;00m inner_training_loop(\n\u001b[1;32m   1499\u001b[0m     args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   1500\u001b[0m     resume_from_checkpoint\u001b[39m=\u001b[39;49mresume_from_checkpoint,\n\u001b[1;32m   1501\u001b[0m     trial\u001b[39m=\u001b[39;49mtrial,\n\u001b[1;32m   1502\u001b[0m     ignore_keys_for_eval\u001b[39m=\u001b[39;49mignore_keys_for_eval,\n\u001b[1;32m   1503\u001b[0m )\n","File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:1740\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1738\u001b[0m         tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining_step(model, inputs)\n\u001b[1;32m   1739\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1740\u001b[0m     tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtraining_step(model, inputs)\n\u001b[1;32m   1742\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   1743\u001b[0m     args\u001b[39m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   1744\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m   1745\u001b[0m     \u001b[39mand\u001b[39;00m (torch\u001b[39m.\u001b[39misnan(tr_loss_step) \u001b[39mor\u001b[39;00m torch\u001b[39m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   1746\u001b[0m ):\n\u001b[1;32m   1747\u001b[0m     \u001b[39m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   1748\u001b[0m     tr_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tr_loss \u001b[39m/\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mglobal_step \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_globalstep_last_logged)\n","File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/transformers/trainer.py:2488\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2486\u001b[0m     loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdeepspeed\u001b[39m.\u001b[39mbackward(loss)\n\u001b[1;32m   2487\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2488\u001b[0m     loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[1;32m   2490\u001b[0m \u001b[39mreturn\u001b[39;00m loss\u001b[39m.\u001b[39mdetach()\n","File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/torch/_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[1;32m    388\u001b[0m     \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    389\u001b[0m         Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[1;32m    390\u001b[0m         (\u001b[39mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    394\u001b[0m         create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[1;32m    395\u001b[0m         inputs\u001b[39m=\u001b[39minputs)\n\u001b[0;32m--> 396\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n","File \u001b[0;32m~/Documents/Github/LP_NLP/venv/lib/python3.9/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    168\u001b[0m     retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[1;32m    170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m    171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward(  \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    174\u001b[0m     tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[1;32m    175\u001b[0m     allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# Launch the learning process: training\n","trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"4HixiwbC8VEN"},"source":["Don't worry the above issue, it is a `KeyboardInterrupt` that means I stopped the training to avoid taking a long time to finish."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"268TvAJu8VEN"},"outputs":[],"source":["import numpy as np\n","from datasets import load_metric\n","\n","metric = load_metric(\"accuracy\")\n","\n","def compute_metrics(eval_pred):\n","    logits, labels = eval_pred\n","    predictions = np.argmax(logits, axis=-1)\n","    return metric.compute(predictions=predictions, references=labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3PlCc9rq8VEO"},"outputs":[],"source":["trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=train_dataset,\n","    eval_dataset=eval_dataset,\n","    compute_metrics=compute_metrics,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TcijbFcP8VEO","outputId":"300d5a97-ab55-4697-8a02-1ecb9a4aff91"},"outputs":[{"name":"stderr","output_type":"stream","text":["\n","Downloading builder script: 4.21kB [00:00, 932kB/s]                    \n","***** Running Evaluation *****\n","  Num examples = 2000\n","  Batch size = 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[Read more](https://huggingface.co/docs/hub/models-uploading)"]},{"cell_type":"markdown","metadata":{"id":"hq_xHPwj8VEO"},"source":["This notebook is inspired by an article: [Fine-Tuning Bert for Tweets Classification ft. Hugging Face](https://medium.com/mlearning-ai/fine-tuning-bert-for-tweets-classification-ft-hugging-face-8afebadd5dbf)"]},{"cell_type":"markdown","metadata":{"id":"XI5rJmpo8VEP"},"source":["Do not hesitaite to read more and to ask questions, the Learning is a lifelong activity."]}],"metadata":{"kernelspec":{"display_name":"Python 3.9.6 ('venv': venv)","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":"3.9.13"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"1ab24538aa0da4b2d8c48eaca591ff7ffc54671225fb0511b432fd9e26a098ba"}},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}
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