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-{"cells":[{"cell_type":"markdown","metadata":{"id":"wz8ZtpV_vkWU"},"source":["# Sentiment Analysis with Hugging Face"]},{"cell_type":"markdown","metadata":{"id":"ZAK-6LHovkWW"},"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":"k2oUBGMNvkWW"},"source":["## Application of Hugging Face Text classification model Fune-tuning"]},{"cell_type":"markdown","metadata":{"id":"O9VRm8uzvkWX"},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":68115,"status":"ok","timestamp":1694771314703,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"r9Jx-No4vkWX","outputId":"a0bf1328-270a-4b53-c558-235d7049c653"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting 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(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[31m1.6 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","Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.17.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n","Requirement already satisfied: 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requests->huggingface_hub) (2023.7.22)\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\n","!pip install huggingface_hub"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E8Xq8UJZvkWZ"},"outputs":[],"source":["##for handling path of my datasets\n","import os\n","from google.colab import drive\n","\n","##for data handling:\n","import pandas as pd\n","import numpy as np\n","from datasets import load_dataset\n","from sklearn.model_selection import train_test_split\n","\n","##for visualization\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","from wordcloud import WordCloud\n","import neattext.functions as nfx\n","import re\n","\n","##modelling:\n","import nltk\n","from nltk.corpus import stopwords\n","import string\n","from transformers import AutoTokenizer, TrainingArguments\n","from transformers import AutoModelForSequenceClassification\n","from sklearn.metrics import mean_squared_error\n","from datasets import load_metric\n","from sklearn.metrics import f1_score\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u2ZtZuKRvkWa"},"outputs":[],"source":["# Disabe W&B\n","os.environ[\"WANDB_DISABLED\"] = \"true\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":46770,"status":"ok","timestamp":1694771405644,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"EbNmKL0kwctF","outputId":"ccbd7253-66b0-4087-a11f-6c49b5139d01"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"Y9bb_VlsvkWa"},"source":["#### LOADING DATASET"]},{"cell_type":"markdown","metadata":{"id":"SFfTfAmRvkWi"},"source":["#### IMPORTING CLEANED DATASET"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6RqOQ02IvkWi"},"outputs":[],"source":["# Disabe W&B\n","os.environ[\"WANDB_DISABLED\"] = \"true\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2461,"status":"ok","timestamp":1694771426315,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"aognDNfDzLYs","outputId":"82220338-7b9d-46b3-f397-a65c179d4958"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Career_Accelerator_P5-NLP'...\n","remote: Enumerating objects: 207, done.\u001b[K\n","remote: Counting objects: 100% (31/31), done.\u001b[K\n","remote: Compressing objects: 100% (6/6), done.\u001b[K\n","remote: Total 207 (delta 26), reused 25 (delta 25), pack-reused 176\u001b[K\n","Receiving objects: 100% (207/207), 856.88 KiB | 1.67 MiB/s, done.\n","Resolving deltas: 100% (111/111), done.\n"]}],"source":["!git clone https://github.com/Azubi-Africa/Career_Accelerator_P5-NLP.git"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ioZ73PcQzNca"},"outputs":[],"source":["#Load Dataset\n","df = pd.read_csv('/content/Career_Accelerator_P5-NLP/zindi_challenge/data/Train.csv')\n","\n","# A way to eliminate rows containing NaN values\n","df = df[~df.isna().any(axis=1)]"]},{"cell_type":"markdown","metadata":{"id":"cIGfCU-ivkWi"},"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":"markdown","metadata":{"id":"WvjdOXSgvkWj"},"source":["#### TRAIN TEST SPLIT"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WVLT4Vk_vkWk"},"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":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1694773517681,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"6qCrpvDivkWk","outputId":"d35eaa3d-00f8-4235-8b3c-b0b1aba40463"},"outputs":[{"output_type":"execute_result","data":{"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  "],"text/html":["\n","  <div id=\"df-d01cb8d4-bcc8-4de9-9f8b-ccfc7c774e1f\" 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>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>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d01cb8d4-bcc8-4de9-9f8b-ccfc7c774e1f')\"\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-d01cb8d4-bcc8-4de9-9f8b-ccfc7c774e1f button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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data\n","train.to_csv(\"/content/Career_Accelerator_P5-NLP/zindi_challenge/data/train_subset.csv\", index=False)\n","eval.to_csv(\"/content/Career_Accelerator_P5-NLP/zindi_challenge/data/eval_subset.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["fdbebd47ebe74a52b302bce840f2a960","e8852cd000dd483589b1204835844895","45fb00689e754afeb56591ea5387d872","9fa8a783ac214bdfb6d7f2775e63e2ea","abc5b4c442d5477f80948b26606f0ba0","657864c8a1c4498da459bfaa2efd621a","ab17450a56264991a70d9c9b0cacd176","4bb914f902c6451381da7b03be3d4ff3","6ba58668a1dc4be49fc6092e31da8cc1","e0eaea5793f04f86ba3924b49a807692","19aca0fefb5f4f39a952f3699ed6090d","6a6aef31e8a0488493d1ae7816ff0336","949af11f6a884b30aa0d33bbee57548e","04d51643bae8434d8eb73347ebb45151","402ad1f6cd1d401681d40e8c6517ba59","106a3aabdfaa42289d933bded3a0ee4f","b9ffe4924be149ad92d66f128eae4e9c","91e63d57a8ae482c92fb9ffb4b37efce","31de6eccf4ae493e8de9800020b4745c","8e3e0b1f2e8346a18dee158cc1b333d5","4c4f616fcc394f6aaec2fb0891829d64","0e05bb49af654d1b9833ef78e465bfd8","60daf93669a1482da5c168a14ef4b1a6","aca4849782ca415cb28d3299d3324dca","e9f506c3ba30405e97ecfb22db885231","8c4c16ba3cff4772acb4b2a9c409e43b","ac3bbef64627490cab6f1a7e3ea4dc62","7231952565b84386a6f23be7835d0224","d1ece32310804c52844d626eb89d9dc5","145b8e7545f744c98aadb471a0fad393","0d6b3a7058be4f50b0edd350f8892f62","9fa31cabb396429b9ed270261879efc4","fc53da18a4394d90b9cc2abb971a0427","bf2dbc4138eb4394870c51b138df8e68","a0f98e84d5d44333831d7cf80ce3e069","0fb9356065ce4fff8050cfd010e6661c","3c9485df73d04d97ad8e282248f5c885","0d87de1056844976b607792f7c4fab63","2fab8060d69542b59295e5e3bb39adc1","268aa5419f454eafa7153ee0161b1e3b","b5e57920ef284bfaa48279fca3fa4960","c62daffa23ce4074bc2da0f4edf9e428","ea770872061040dcbf2df5a01ce5b32c","746c18ecc9384450960f42803a60453c"]},"executionInfo":{"elapsed":2157,"status":"ok","timestamp":1694773564959,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"LW9B-PKW0NAk","outputId":"9219de1d-921a-48b7-ca37-cade363ef340"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fdbebd47ebe74a52b302bce840f2a960"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a6aef31e8a0488493d1ae7816ff0336"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"60daf93669a1482da5c168a14ef4b1a6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating eval split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf2dbc4138eb4394870c51b138df8e68"}},"metadata":{}}],"source":["dataset = load_dataset('csv',\n","                       data_files={'train': '/content/Career_Accelerator_P5-NLP/zindi_challenge/data/train_subset.csv',\n","                                   'eval': '/content/Career_Accelerator_P5-NLP/zindi_challenge/data/eval_subset.csv'}, encoding = \"ISO-8859-1\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["900068f31eb247699b4f34c8d0c22621","5660eebca3464092992f5f8a78e5d461","e172e84c11e04cc4b00e772e811b7d01","5ea55bc6246b4383b09f04c6f537ca3d","fd19aa022a344953bd3049a8a427e26c","d3fc0ed961bd4137aa1bd0061a5b0014","f7237698ab224ea9aefd9f0a93be4b71","b462fa6d41a041a3b6bd0517f8cd1b30","87976ceac6174f47a9f7b4ab33aede20","f7bb492fa8bd4b7e99d06028ea582af3","1d89a418a274489cba14fbeccc4fed64","f01a21e8436148b186f25376d4d2c193","deb21c3b12094bfdbc1aaf4499f315a4","5ccc27d28c7d436396a99e7759649691","17865f1d68904f4fb4ee6446eb74ac0c","14c36cdd4f084a7785a87358e6384cd2","846f9c3e7a4144f2b82e3821a97c0ccd","64e03d4f9c634b81a3811155edba2cf7","59bfc071027c47119062bcff5cc49173","698f824df81c4211809cdd1ee9ba47c1","ee38558a1752469487092363737fcbec","d185011c0f1f4101a87b74da9aaed6fb","5a2a4571158a4f548ef83e662da04789","964a2fd4f354428a83b5a558c8b12cb3","fda12a674cb440a8a423cafd51ce9167","772c1bf9959c44bfb47cebd6c45392ce","956b7203287e46658fb46b90075ab4ea","217e44cbdadb47a6a717eadaab4bb48c","227abef830fd44f5b0f8f88ede0f8a52","6de13c5aed6f43e99bf0589859b2b9ba","2bbef3b4f9e74f9e8ac21cb88066b843","3196e552647a4bd8939b3962f92a26c9","8fecd9441b4f4369bb8f565de06895cd","c5996761316a48f1a46ce27357fff84b","28d83b9c007f47ffb8b14c52f5e3f4f2","b1e2237f9dcc4a7987df52bbddc42fa0","c2b42d5706bf4eb0b43567fdcbae9c32","bb761d75f99e40cc9a2c9a4a3e958981","f627417642164b509deda21a8e573f43","95478d8a690c4fb6a8f257f03d6ebc5b","a5f19e9a2b3c4f34b16f86c3b5f873f5","9d5751e3045d413c82d56a9cb1a002ee","448b6d2e9f534d698ef9c6de5b67a163","b4ba80c9059a4e4e8fce9d2a3de38d5e"]},"executionInfo":{"elapsed":4981,"status":"ok","timestamp":1694773970328,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"CYgBhVjfvkWl","outputId":"aa8489cd-fcb3-4044-9999-fb526de0eefa"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"900068f31eb247699b4f34c8d0c22621"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f01a21e8436148b186f25376d4d2c193"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a2a4571158a4f548ef83e662da04789"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c5996761316a48f1a46ce27357fff84b"}},"metadata":{}}],"source":["#Setting our tokenizer\n","tokenizer = AutoTokenizer.from_pretrained('roberta-base')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["465f5522d45c4bcf94f7c37ca51a492e","38d40ae4c13e423b8662a9161de9a916","380a6d53f5aa4d93ae41f7a031b1802d","c5befff03c724782b02780e89a2a13d6","7b6c7480b7024b3e92df1dc0947690bf","87f7fdc8be8740998a5b8f809dab902b","ff437f4ded8243458c1a7d756e2754d7","9d31887fd411427bb5a2b6bb6f3fba21","b5fd473c6d50429b802607ba7777b6cf","161e84caf79141f0886f95ee8e5dcfc9","b61227a7edd1460bacca659508e10b5e","6ec440f769ca4bc18d792d01908ca972","b4ac387b29424a33bc0da59820574c26","03150436461c4b84b4fddd57adc34fad","828bcc0ba8844c8697a85c446d84ab91","9e9f68ccf02c41e5a6ee23a8f91e8b5d","d334d841de6542a2af019df072059546","8b7de9c9eb5b4090a08eaff4e7d5e40d","a2a0921b37794d24a3566db095b25b40","b82c0846acd94b73a21d5280186469ba","ad5a8eae45324c7fb9418703e142f4a8","200be3390e1448e6ba1b9af286f01491","6e777eb32cfd426a819d7caf15024bd9","c6edcaacbe5144008da90f9c8fecea0e","c2df5022fdc0470f87468f3e83265462","26833d1d514f4c34a57a19c92b50551d","07e1ee27ffd44238808e330419b4ba9b","69fffa46ad6c40858bf760c708815727","0d5dcfdf8e7946f9b5b3f94e20c37af8","2c40f30d799d4d0aaa119241c49a300b","66ed83a3bf2042c39e3029c1665e2e15","2d4bf2afc40b4002bf513bfbe555cde2","7b698f6bae194e4cb70f3d13c8bd1f85","a1ddb4dbdcb74d13a76ad1767a8a8018","492f217b70ee4bb2934f291aa6f96242","f7ff838e67494ae2bae30346f1eca6a0","3ff7f380ffac447290503d4f5188d99b","dcbe236e687640a3aeda88a12b4d4bfc","996b462545b242e1876dd38857788b4e","f207bb55b4e44b1bad5a6c3c2dd8b12f","f0e5c5265e594491b3dc2214ef3b0b52","d9e6b4f1bacd4fe9a5fc297d808af091","2f8a5ed3eec44da38a2bd77056a4bf02","ba6b369d7a244740af3513bb71341d28"]},"executionInfo":{"elapsed":3209,"status":"ok","timestamp":1694773978169,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"c3vlOEOBvkWm","outputId":"90969d5e-2106-4cde-feab-036eafc92791"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"465f5522d45c4bcf94f7c37ca51a492e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ec440f769ca4bc18d792d01908ca972"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6e777eb32cfd426a819d7caf15024bd9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1ddb4dbdcb74d13a76ad1767a8a8018"}},"metadata":{}}],"source":["# Function to transform labels\n","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","# Function to tokenize data\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 unwanted 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":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1694774009143,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"JvALNT0yvkWm","outputId":"4aa9613d-3083-41f3-b896-af21fc9130a4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 7999\n","    })\n","    eval: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 2000\n","    })\n","})"]},"metadata":{},"execution_count":18}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E6FAB8-tvkWm"},"outputs":[],"source":["# dataset['train']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":996,"status":"ok","timestamp":1694774048328,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"kGYtPPcvvkWn","outputId":"ac39d45a-c70b-46ac-cb1c-1ecba418f09b"},"outputs":[{"output_type":"stream","name":"stderr","text":["Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"]}],"source":["from transformers import TrainingArguments\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(\n","\n","    output_dir=\"Sentiment-Analysis-Model\",\n","    num_train_epochs=5,\n","    per_device_train_batch_size=8,\n","    per_device_eval_batch_size=8,\n","    load_best_model_at_end=True,\n","    evaluation_strategy=\"steps\",\n","    save_strategy=\"steps\",\n","    push_to_hub=True\n","    )"]},{"cell_type":"markdown","metadata":{"id":"iK53TOHovkWn"},"source":["#### LOADING PRETRAINED MODEL"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["ea3ab73068ae4856a996d9427b05d83a","168f62c2b00d4692b5887a01eaf5495f","3161216ba6074989ba4655248d5beff0","3974f7cb6ed24f66ab2979a7f05e7ecc","9f3663513d454525a80940b030725ac2","d4b3be6b1af346d3832ba8712d8f62d7","d4951a7fcd76487bb9ff28693aa7d01a","b24cfccd7c514ca084f7abf17acbe666","6193fa2854644fdfbcf35a538e4279b9","9af59d86959f4c7386c34d9b2c413f65","36d0a5fecb844e6cbe97f8051f749dfe"]},"executionInfo":{"elapsed":8224,"status":"ok","timestamp":1694774071455,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"NDIKxAcfvkWn","outputId":"eb617d05-2f68-4687-c6d6-6a9bbd2eaa73"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ea3ab73068ae4856a996d9427b05d83a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["# Loading a pretrain model while specifying the number of labels in our dataset for fine-tuning\n","model = AutoModelForSequenceClassification.from_pretrained(\"roberta-base\", num_labels=3)"]},{"cell_type":"markdown","metadata":{"id":"t6sAGKwyvkWn"},"source":["#### SPLITTING TRAIN SET"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2O5iPn9LvkWo"},"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":"markdown","metadata":{"id":"IbMPxkHQvkWo"},"source":["#### EVALUATION METRIC"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["bd68b6ecdbfd4bd591a0f74037a63daf","8bd6bb5dbcf640c8a61cf86941cb8ea8","979da260b4ee4e549b0d695d8c0bd075","c19aa956c54e4c1e80b4f51eeeb8abf0","f43bb8e3b923463f92ca70868e53498d","e91979bf82254abcb2439c685c4068bc","a08d2fcf446b435499e8ff242c390d7b","9acfa315042341949029353942eaf722","9dd271298ccd4c3c9a3d3db373b36e21","eaa83716ec1a44fb9466f543d46fbc6b","0bd22fe326b8414e8fa068b2bc99dc5c"]},"executionInfo":{"elapsed":2187,"status":"ok","timestamp":1694774095866,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"LevxW5kVvkWo","outputId":"f7591d52-0f6f-4116-f5c0-5063173e2603"},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-22-e0bf8a787253>:14: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n","  metric = load_metric(\"accuracy\")\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bd68b6ecdbfd4bd591a0f74037a63daf"}},"metadata":{}}],"source":["# def compute_metrics(eval_pred):\n","#     logits, labels = eval_pred\n","#     predictions = np.argmax(logits, axis=-1)\n","#     return {\"rmse\": mean_squared_error(labels, predictions, squared=False)}\n","\n","\n","\n","# def compute_f1_score(pred):\n","#     labels = pred.label_ids\n","#     preds = np.argmax(pred.predictions, axis=1)\n","#     f1 = f1_score(labels, preds, average=\"weighted\")  # Use \"weighted\" for multiclass tasks\n","#     return {\"f1_score\": f1}\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)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["19c9d2b8c5e04b3e90db1f25a483ebf7","74cdb5aa86d84a6488e4147070a4e457","8aebca2d050a4a38a1485afb16e45e65","f64aa10c14484b81ba8bcca798fd503f","8a6ec265be2246f6a716b6336b689c58","096fefb0e28e436d89d5c699a23b52ff","f1b20d8372b440feb11bbf4cbadf0aec","f32648424b5246ada76cafedc5673fa9","49588ac786ea45db837998f37bdab80f","03dca8d41d3b4fc69c2909c57f8da8cd","502009c46b3d46549082802e3dc69edb","7b34f18931bc4a2e84766cc5330aa20a","6032bf1d7e4d4d039e7ae71c829f4544","5104a44089a349ed8688d0cdb025111e","e704298e682f4ce4919aaf4d835642a8","ddced10cee734b60ad86a65047fbb866","eb83101cbca84e018b2f5a850f66ca67","73805636a80d46cfa266fe6fbbb6a3d4","48b4062cd5cf4867a135de6cb1e5842c","76ce8fe7c98e4423ac7ffda037923906","9b35d63e5d394f49b10a385d78c8da56","5374b34a11474f6fa3ad1c7a0630b7a7","d1f416fd22854b578295f109a2e2a885","7adfd14e07cc46e88350a7b0b1d84934","8c431488eb5a4bdfb8e772ae4a44eada","6dcb6a637d414d408f7b873848726d57","06db155a12854c71aa25b3332a64357e","2ddf5a9148a844ef86ffa82c7ab35ddf","940b9779d0ad416ca8b019df8101754b","3cb45bfbc7cd48beb413bc14fe61f6de","025eb5080344427d9d32a8bdbeb7c18d","478c069eab244bb2a28aed90c434da1b"]},"executionInfo":{"elapsed":499,"status":"ok","timestamp":1694774102657,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"i1rlEkrJqzr4","outputId":"db6c1ab7-4cd1-4806-8950-970c7517d6b8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"19c9d2b8c5e04b3e90db1f25a483ebf7"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"markdown","metadata":{"id":"_rC1mt1lvkWo"},"source":["#### MODEL TRAINING SETUP"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E7xx_da4vkWo"},"outputs":[],"source":["from transformers import Trainer\n","\n","\n","# Model Training Setup\n","trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=train_dataset,\n","    eval_dataset=eval_dataset,\n","    tokenizer=tokenizer,\n","    compute_metrics=compute_metrics,\n","\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":461},"id":"9uEivleEvkWp","executionInfo":{"status":"ok","timestamp":1694778730473,"user_tz":0,"elapsed":4583586,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"}},"outputId":"cfd28d92-18c5-4039-86ad-68ac17db3a11"},"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='5000' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [5000/5000 1:15:28, Epoch 5/5]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>0.864800</td>\n","      <td>0.984842</td>\n","      <td>0.703000</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.836700</td>\n","      <td>0.876359</td>\n","      <td>0.683000</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.781500</td>\n","      <td>0.779163</td>\n","      <td>0.714500</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.775100</td>\n","      <td>0.751614</td>\n","      <td>0.709500</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.808100</td>\n","      <td>0.778295</td>\n","      <td>0.705500</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.814200</td>\n","      <td>0.812543</td>\n","      <td>0.688000</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.849700</td>\n","      <td>0.838273</td>\n","      <td>0.657500</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.800600</td>\n","      <td>0.741184</td>\n","      <td>0.705000</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.736300</td>\n","      <td>0.729933</td>\n","      <td>0.718000</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.715100</td>\n","      <td>0.699995</td>\n","      <td>0.716500</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=5000, training_loss=0.7981971923828125, metrics={'train_runtime': 4531.8917, 'train_samples_per_second': 8.825, 'train_steps_per_second': 1.103, 'total_flos': 1.052322114203136e+16, 'train_loss': 0.7981971923828125, 'epoch': 5.0})"]},"metadata":{},"execution_count":25}],"source":["# Launch the learning process: training\n","trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WFIJifl8vkWp"},"outputs":[],"source":["\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":{"colab":{"base_uri":"https://localhost:8080/","height":141},"executionInfo":{"elapsed":61907,"status":"ok","timestamp":1694780652524,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"},"user_tz":0},"id":"K-PmokkevkWp","outputId":"8880335a-bc05-4c32-ad0b-88a51ffa8bac"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [250/250 01:00]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.699994683265686,\n"," 'eval_accuracy': 0.7165,\n"," 'eval_runtime': 61.2388,\n"," 'eval_samples_per_second': 32.659,\n"," 'eval_steps_per_second': 4.082,\n"," 'epoch': 5.0}"]},"metadata":{},"execution_count":27}],"source":["# Launch the final evaluation\n","trainer.evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AB4d6zyEvkWq","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1694780670738,"user_tz":0,"elapsed":6495,"user":{"displayName":"Kofi Amoako","userId":"18255665820642908010"}},"outputId":"d69a3e10-eb98-4fc2-b0d8-8bd08afd3990"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'https://huggingface.co/fusersam/Sentiment-Analysis-Model/tree/main/'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":28}],"source":["trainer.push_to_hub()"]},{"cell_type":"markdown","metadata":{"id":"_Lfpa0IXvkWq"},"source":["Some checkpoints of the model are automatically saved locally in `test_trainer/` during the training."]},{"cell_type":"markdown","metadata":{"id":"nh467hRXvkWq"},"source":["You may also upload the model on the Hugging Face Platform... 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