Recipe_Generation_DistilGPT.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"m7l5pjgjfmZk"},"source":["##Importing necessary libraries and functions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24799,"status":"ok","timestamp":1683906102832,"user":{"displayName":"Ashvath Balgovind","userId":"07649613257406864791"},"user_tz":240},"id":"NLt1nQ0SPcUM","outputId":"590df1af-3d3c-4eb2-cb31-d33bb90b9e1b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.29.1-py3-none-any.whl (7.1 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) 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already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n","Installing collected packages: tokenizers, huggingface-hub, transformers\n","Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.1\n"]}],"source":["#Installing Transformers library \n","!pip install transformers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"S5Z1yn1QoAgA"},"outputs":[],"source":["#Importing necessary libraries\n","import pandas as pd\n","import numpy as np \n","import random\n","import torch\n","from transformers import AutoTokenizer, DataCollatorForLanguageModeling, AutoModelForCausalLM, TrainingArguments, Trainer\n","from tqdm import tqdm, trange\n","import math"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21245,"status":"ok","timestamp":1683906135803,"user":{"displayName":"Ashvath Balgovind","userId":"07649613257406864791"},"user_tz":240},"id":"mGD03ang-NES","outputId":"ca49d437-dff6-479b-cd39-49999b568f73"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["#Mounting Google Drive\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"Yo88jLbBwRpJ"},"source":["## Data Preprocessing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rPLV5JlquAmB"},"outputs":[],"source":["#Loading the final concatenated preprocessed Recipe dataset\n","df = pd.read_csv('/content/drive/MyDrive/685 Project/Final_dataset')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jtNS-Y_fufGq"},"outputs":[],"source":["#Dropping the title column\n","df.drop(['title'], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1683653380430,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"MF0bGrLuuPo1","outputId":"c2fcbaf1-6960-4c48-9bf4-8725b71db590"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-49dfb253-0198-4db8-9028-63c0cf886962\">\n","    <div 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>ingredients</th>\n","      <th>instructions</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[brown sugar, milk, vanilla, nuts, butter, bit...</td>\n","      <td>In a heavy 2-quart saucepan, mix brown sugar, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[chicken, chicken gravy, cream of mushroom sou...</td>\n","      <td>Boil and debone chicken. Put bite size pieces ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[pineapple, condensed milk, lemons, pecans, gr...</td>\n","      <td>Empty Cool Whip into a bowl. Drain juice from ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[dark sweet pitted cherries, ginger ale, flavo...</td>\n","      <td>Drain cherries, measuring syrup. Cut cherries ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[powdered sugar, butter, peanut butter, paraff...</td>\n","      <td>Mix sugar, butter and peanut butter. 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Put bite size pieces ...  \n","2  Empty Cool Whip into a bowl. Drain juice from ...  \n","3  Drain cherries, measuring syrup. Cut cherries ...  \n","4  Mix sugar, butter and peanut butter. Roll into...  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{"id":"4ZxML-7Vwkxr"},"source":["## Data Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["0bef29f6364f4676b2a65f9eb2b05529","a5f46e3745b747f8a63c7db927c38442","b741c5c4453845bfbfb79d4676f294e8","e37f0816b82b4c3b8a1a3f8abf60081a","a6a5744e6947440c9260cf4df88ce6ae","29439a7e83fb4dfcace791f8bcce8097","87e84b7aae6a46b185d57927bb2ba8a5","55481a87ac344936802dd5d19d6454b8","2d3d3e449ef54165a6fce800da025e01","900240f2609b431384965b048f7b6664","c2bbfd9f9fa24792a0a9172ede5d2dba","8aec8301d12e47a097d6e22072c6c9b1","2aaa6897b6e346b5aaf3c29eb6d72850","ee3a377a6128423c82e0561a8097d695","04478a5a0c74448ca44837fb4575fbb4","1ca6c4e0a3ca49f78077eec3eb8a5384","681f966431384985a6d8a72de0bff643","567c4b932260436bb9c11ccf3ecb2d9f","ae42b8b39fa547c0b735628997fd513f","088349841cdc4fcab59056980304af8a","a642da10d6bc4dd78a2fbeb4a0b7a3f2","e06b93de4d7e4143a165b1c0f5c7081f","a2dfe0aa345745f4b1bbc951ec96ba0c","71356e2f84bd4171af369e94d7762513","5ce2536fcb334bf4a793ebe28529bceb","3155f48bd8624c8db92a6469542ba084","94c5416c286948aebad58f658db43a91","3d1a71d0ad1b43f59db8a3508c89bc3c","656cc37d21374edd8c67eb15b311566e","f1148ff98100491298b3f2cd05732a2e","b4f210851a4d43f69bd5f7b31cc18e93","8fe3654e82e94b34a560256b37ea26cd","908e902027bd469c8d2dbb080e09aba8","6e8fbb4da67d4bbea8861d7e4fbbd82d","c2bb2160e2aa47898e7f2a6545fb03ff","dca7245a0d5147cc92002426aa7ab47b","d29a3108f91a47779b78ebe66fc38655","de330c172aac46339f9f80c8b464c72a","ed3132a32dff4c3b976d49660b6364ac","af71b77dc5014808b1b8a8a39a03642b","ecd56c25dd204ac697fa2ee6515dc170","860c3a30b9da4779b618f3797efdf1fe","7a7fca6cde8e4aea92ced0ae9fcbe448","8beed1c08e6b40c5ba8440f93080ae67"]},"executionInfo":{"elapsed":1235,"status":"ok","timestamp":1683861209456,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"XEKpsgMxGy5g","outputId":"419ea3a7-8f46-40ee-b7b6-45af49df7a5f"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0bef29f6364f4676b2a65f9eb2b05529","version_major":2,"version_minor":0},"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8aec8301d12e47a097d6e22072c6c9b1","version_major":2,"version_minor":0},"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a2dfe0aa345745f4b1bbc951ec96ba0c","version_major":2,"version_minor":0},"text/plain":["Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6e8fbb4da67d4bbea8861d7e4fbbd82d","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["# We load the GPT2 Fast Tokenizer\n","base_tokenizer = AutoTokenizer.from_pretrained('distilgpt2')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bcgcxrDsHFZI"},"outputs":[],"source":["# Checking the size of Tokenizer's vocabulary \n","print('Words in vocabulary: ', base_tokenizer.vocab_size)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7667,"status":"ok","timestamp":1683861390441,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"v0_PxFcHL2aa","outputId":"39475cfe-9eff-43ae-b7df-f7ccfb7fe07d"},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 579191 recipes for training and 64355 for validation\n"]}],"source":["from sklearn.model_selection import train_test_split\n","\n","#Processing different columns into a single paragraph (column) - better for pretraining objective (next word prediction - causal language modeling) \n","prepare_text = lambda x: ' '.join([\"Ingredients:\" , x['ingredients'], \"Recipe:\", x['instructions']])\n","df['Input'] = df.apply(prepare_text, axis=1)\n","\n","# Split in train and test - 90% train and 10% train\n","df_train, df_val = train_test_split(df, train_size = 0.9, random_state = 77)\n","\n","#Train and validation dataset sizes\n","print(f'There are {len(df_train)} recipes for training and {len(df_val)} for validation')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YOYHdpUNaoj4"},"outputs":[],"source":["# Reset the index of dataframes are split\n","df_train.reset_index(inplace=True, drop=True)\n","df_val.reset_index(inplace=True, drop=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1683861390749,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"iTdSnaS9a1RY","outputId":"00984dad-33df-42fb-ecd2-3924926f1fbb"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-f4194290-31a8-48b7-9c2b-fb154c3c342c\">\n","    <div 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>ingredients</th>\n","      <th>instructions</th>\n","      <th>Input</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[fresh strawberries, strawberry Jell-O, boilin...</td>\n","      <td>Put strawberries in blender container; cover a...</td>\n","      <td>Ingredients: [fresh strawberries, strawberry J...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>['1 small butternut squash (about 1 1/2 pounds...</td>\n","      <td>1) Halve, seed and peel squash. Cut into bite-...</td>\n","      <td>Ingredients: ['1 small butternut squash (about...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[margarine, sugar, egg, milk, salt, baking pow...</td>\n","      <td>Cream the margarine. Add sugar and beat until ...</td>\n","      <td>Ingredients: [margarine, sugar, egg, milk, sal...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[dinner rolls, brown sugar, regular butterscot...</td>\n","      <td>Grease and flour Bundt cake pan. Place frozen ...</td>\n","      <td>Ingredients: [dinner rolls, brown sugar, regul...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[apple, lemon juice, trout]</td>\n","      <td>Combine apple slices and lemon juice in medium...</td>\n","      <td>Ingredients: [apple, lemon juice, trout] Recip...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4194290-31a8-48b7-9c2b-fb154c3c342c')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-f4194290-31a8-48b7-9c2b-fb154c3c342c button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-f4194290-31a8-48b7-9c2b-fb154c3c342c');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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Cut into bite-...   \n","2  Cream the margarine. Add sugar and beat until ...   \n","3  Grease and flour Bundt cake pan. Place frozen ...   \n","4  Combine apple slices and lemon juice in medium...   \n","\n","                                               Input  \n","0  Ingredients: [fresh strawberries, strawberry J...  \n","1  Ingredients: ['1 small butternut squash (about...  \n","2  Ingredients: [margarine, sugar, egg, milk, sal...  \n","3  Ingredients: [dinner rolls, brown sugar, regul...  \n","4  Ingredients: [apple, lemon juice, trout] Recip...  "]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["df_val.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MrM31GDuVRx6"},"outputs":[],"source":["#Tokenizing the input\n","#Truncation is done if the length exceeds maximum length \n","def preprocess_function(input, tokenizer = base_tokenizer):\n","    return base_tokenizer(input, add_special_tokens=True, truncation=True, max_length = 1024)['input_ids']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DANXpxEuVvWC"},"outputs":[],"source":["#Modifying the recipe into a prompt based input for the baseline model \n","def prompt_to_test(x):\n","  return ' '.join([\"Generate a recipe using following\", x])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pIWcezKHrK3q"},"outputs":[],"source":["#Tokenizer does not have an explicit pad token, therefore setting pad token to eos token \n","base_tokenizer.pad_token = base_tokenizer.eos_token"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i8wvpWcobBol"},"outputs":[],"source":["#Baseline is not finetuned on the preprocessed dataset\n","#Testing for baseline is done on the same validation dataset used for the finetuned model\n","baseline_val = df_val['Input']\n","baseline_val = baseline_val.apply(prompt_to_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z0Qs5aa1dqq0"},"outputs":[],"source":["#Applying the prompt preprocessing to the baseline validation dataset\n","baseline_val = baseline_val.apply(preprocess_function)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iym17AryVRx6"},"outputs":[],"source":["#Tokenizing the train and validation dataset for the model\n","df_train= df_train['Input'].apply(preprocess_function)\n","df_val = df_val['Input'].apply(preprocess_function)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3XyoktCRS5vx"},"outputs":[],"source":["# Can store the datasets and load later to avoid performing data split and tokenization redundantly \n","\n","# df_train.to_csv('/content/drive/MyDrive/685 Project/RG_train_dataset', index=False)\n","# df_val.to_csv('/content/drive/MyDrive/685 Project/RG_test_dataset', index = False)\n","# baseline_val.to_csv('/content/drive/MyDrive/685 Project/RG_baseline_test_dataset', index = False)"]},{"cell_type":"markdown","metadata":{"id":"eahqSqiVf0a4"},"source":["## Training the GPT2 Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i1p1I1_hiTbQ"},"outputs":[],"source":["# Checking the size of the model in bytes\n","\n","# !pip install objsize\n","# import objsize\n","# objsize.get_deep_size(base_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GL1S5Mh4TZvl"},"outputs":[],"source":["# Loading the tokenized dataset if stored earlier\n","\n","# df_train = pd.read_csv('/content/drive/MyDrive/685 Project/RG_train_dataset') \n","# df_val = pd.read_csv('/content/drive/MyDrive/685 Project/RG_test_dataset')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PjyjTLSkW33q"},"outputs":[],"source":["# df_train = df_train['Input']\n","# df_val = df_val['Input']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["96b17d101b224576b5f99c07a52a4ff1","636ee44a17954b4ba74c5d27a80d1e07","0c680d8637584aef8aec447484aea1ab","997d6ae9224d456585ecd8cfba155cb1","1c95ea02109542e2bf88dfb43d180b7f","0647af9adeb3499091395da6ab307266","5eb6cd5878004119ba018ff48529ed1f","e4cd7a1fa98548428864a4db9908c85c","c5e038b066ac418fad6c1bed80c59f93","ccf3e8e7034749df8b0d62e447d5fae5","e5c888dda0ce48058c3505244faf6f61","5430abab3a854dbaba100776b4dae817","5115410f5601455d93544139d2a10b29","e1526974592b425596e5bf4af35bfd33","4db0a065ff634c8dab12b3f44c65de2d","d9484bc9f7d243aabf66215119dd9360","605d31021ad24a22afa5a59426315755","4ced5f8a72294b9bb1eda4fd7c38f552","96f6c96360e045b1b0e438175b6836a5","5c464e00df6743048b235768546ed785","9ece67f168144e4eb2fc6fa1a0ff3e3b","39cd8e3815be46b9ab7aeb6f903b2db0"]},"executionInfo":{"elapsed":26646,"status":"ok","timestamp":1683674265848,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"tYeKypq6VRx7","outputId":"7a747a90-38d3-4ff5-def7-9aed2987ff81"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"96b17d101b224576b5f99c07a52a4ff1","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/353M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5430abab3a854dbaba100776b4dae817","version_major":2,"version_minor":0},"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["#Initializing baseline model\n","base_model = AutoModelForCausalLM.from_pretrained(\"distilgpt2\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":216,"status":"ok","timestamp":1683674266062,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"cPraI2W3GvLs","outputId":"cc713d7d-df08-4daf-932c-f4c8f8c032a1"},"outputs":[{"data":{"text/plain":["<bound method ModuleUtilsMixin.num_parameters of GPT2LMHeadModel(\n","  (transformer): GPT2Model(\n","    (wte): Embedding(50257, 768)\n","    (wpe): Embedding(1024, 768)\n","    (drop): Dropout(p=0.1, inplace=False)\n","    (h): ModuleList(\n","      (0-5): 6 x GPT2Block(\n","        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n","        (attn): GPT2Attention(\n","          (c_attn): Conv1D()\n","          (c_proj): Conv1D()\n","          (attn_dropout): Dropout(p=0.1, inplace=False)\n","          (resid_dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n","        (mlp): GPT2MLP(\n","          (c_fc): Conv1D()\n","          (c_proj): Conv1D()\n","          (act): NewGELUActivation()\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","      )\n","    )\n","    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n","  )\n","  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",")>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["#Checking different parameters in the model\n","base_model.num_parameters"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vOaqOACyf5vL"},"outputs":[],"source":["#Initializing the GPT2 Fast Tokenizer\n","base_tokenizer = AutoTokenizer.from_pretrained('distilgpt2')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5d8nTKEvJAWE"},"outputs":[],"source":["#Setting pad token same as eos token\n","base_tokenizer.pad_token = base_tokenizer.eos_token\n","\n","#Initializing Data Collator that forms batches and sends the input in a proper language modeling format to the model for training and evaluation\n","data_collator = DataCollatorForLanguageModeling(\n","        tokenizer=base_tokenizer,\n","        mlm=False,\n","        return_tensors=\"pt\"\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fB0ZA4sUEI5t"},"outputs":[],"source":["from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n","#Specifying path where running logs, checkpoints and final model parameters will be stored\n","model_path = '/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2_20'\n","\n","#Specifying the Training arguments for the model training \n","training_args = TrainingArguments(\n","    output_dir=model_path,\n","    overwrite_output_dir = True,  #If there is any content present in the output directory, overwrite it\n","    evaluation_strategy=\"no\",     # Do not perform evaluation during the training\n","    gradient_accumulation_steps=8, # Accumulate the gradients for 8 steps, before performing the weight updates\n","    num_train_epochs=20,        # Number of training epochs\n","    weight_decay=0.01,          # Weight update decay after each epoch\n","    warmup_steps=1_000,         # Number of training steps, before a weight update is performed\n","    lr_scheduler_type=\"cosine\", \n","    learning_rate=5e-4,         # Initial learning rate value\n","    save_steps=5_000,           # After how many training steps, should the model be checkpointed \n","    fp16=True                   # Limit the precision of the weights to 16 floating points only\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7-nFTR4CJCf9"},"outputs":[],"source":["trainer = Trainer(\n","    model=base_model,                         # the instantiated  Transformers model to be trained\n","    args=training_args,                  # training arguments, defined above\n","    data_collator=data_collator,        #data collator \n","    train_dataset=df_train,         # training dataset\n","    eval_dataset=df_val            # evaluation dataset\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g8_hgagGadze"},"outputs":[],"source":["#Model training\n","trainer.train()  "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uTwfi1WvJLZE"},"outputs":[],"source":["#Saving model and the tokenizer\n","trainer.save_model()\n","base_tokenizer.save_pretrained(model_path)"]},{"cell_type":"markdown","metadata":{"id":"9khMF9Xjfu-f"},"source":["## Inference (PERPLEXITY)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NX5uHak3JNiP"},"outputs":[],"source":["import math\n","\n","#Perplexity of the model is evaluated on the validation dataset \n","eval_results = trainer.evaluate()\n","print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"]},{"cell_type":"markdown","metadata":{"id":"J4ByUTfaVMks"},"source":["##Testing Baseline DistilGPT2 (PERPLEXITY)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wrU0We2wWHqL"},"outputs":[],"source":["#Initializing the baseline tokenizer\n","baseline_tokenizer = AutoTokenizer.from_pretrained('distilgpt2')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8VQ6yCKsWE05"},"outputs":[],"source":["#Setting the pad token to eos token\n","baseline_tokenizer.pad_token = baseline_tokenizer.eos_token"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["1b49c01f705b49d4baad6439de6250ec","72952eafb2714d608a337080fce1291a","35b7c12acff542c99e874a249153ae55","2373762a1b5b4a29b20e95928ac968dd","ecea2dfd100e4ab99bd9f2782e46a8db","48bf59ad29dc4d919f068fa8e8802d10","c71888a84a174a2d9b07b2941d36f6a0","ade015f2dfd2402ba29c02705c18ae9c","0e8f3b8dd3894704ba1f573ff47b192b","19e71e82dcf04474b4f3237b038dbf19","4675b3b256f04ab4b12f83ffa643a192","f1197f89e3504a769cda2a29a65a8500","9f9038c097494112b015e73c368f2b71","e07af4a3f1854b13a9f758b0d213ebf5","8bffef67f3a14821bdcf8aa447a6e672","a70b5eb3ffc84a87bf1d6d5f80ec51cb","54a43b0298334456a0e2a47ee6d44921","5ffc6e1e0149469783eda92fd8ca7132","45096c0f18244045bc22fee38fee1ce6","eb4aaf10d35142a8b3869f57ed42b79b","22600e3287774701afcde9c1071a4b17","217c3bb84b2a48569a28b67e966ca0f8"]},"executionInfo":{"elapsed":7736,"status":"ok","timestamp":1683733595614,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"y5ZonKXmVRWY","outputId":"9b8f7808-2cf4-42f0-d882-11e61f3a6ecc"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1b49c01f705b49d4baad6439de6250ec","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/353M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f1197f89e3504a769cda2a29a65a8500","version_major":2,"version_minor":0},"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["#Initializing the baseline model (Non fine tuned DistilGPT2)\n","baseline_model = AutoModelForCausalLM.from_pretrained(\"distilgpt2\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4848,"status":"ok","timestamp":1683652619338,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"SeDiuDKCWToH","outputId":"e7718ac4-30dc-47e9-c831-3fa88b801154"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n","  warnings.warn(\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread] Recipe: [Strawberry, cheese, graham crackers, Regardless (and all) of the ingredients listed above for \"Seeding.\"\\n\\n\\nA few ways to make it this way:\\n1) Place a bowl of water between the bottom of the bowl and stir thoroughly. Place the bowl of water so that the water between the bottom of the bowl and stir thoroughly. Bring to a boil, and leave some water in place of the mixture so that the mixture is dry: If you do not like the texture of the mixture, combine the milk before cooking.\\n2) Remove the bowl of water, and let it cool,'}]"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["#Creating a text generation pipeline to see how the baseline model performs on the task of recipe generation\n","from transformers import pipeline\n","baseline_generator = pipeline('text-generation', model=baseline_model, tokenizer = baseline_tokenizer)\n","input = \"Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread] Recipe:\"\n","#Generating the results for the baseline model \n","baseline_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L743NN-8esgE"},"outputs":[],"source":["#If saved the dataset earlier, then can load it \n","# baseline_val = pd.read_csv('/content/drive/MyDrive/685 Project/RG_baseline_test_dataset')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MO5bEO5Zmg74"},"outputs":[],"source":["# baseline_val = baseline_val['Input']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c9wDUzdAmX9O"},"outputs":[],"source":["baseline_val.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YrrNryVMYU_O"},"outputs":[],"source":["# Creating the DataCollator for the evaluation dataset of the baseline model\n","baseline_data_collator = DataCollatorForLanguageModeling(\n","        tokenizer=baseline_tokenizer,\n","        mlm=False,\n","        return_tensors=\"pt\"\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6fHnCWlxX-yx"},"outputs":[],"source":["from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n","model_path = './baseline_model_RG'\n","\n","#Specifying the training arguments - Although we are not performing training, but Trainer method requires this argument \n","training_args = TrainingArguments(\n","    output_dir=model_path,\n","    overwrite_output_dir = True,\n","    gradient_accumulation_steps=8,\n","    num_train_epochs=0,   #Set the training epochs to 0, as we will not be doing any sort of training \n","    weight_decay=0.01,\n","    warmup_steps=1_000,\n","    lr_scheduler_type=\"cosine\",\n","    learning_rate=5e-4,\n","    save_steps=5_000,\n","    fp16=True\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EB27Mf2CYUsg"},"outputs":[],"source":["trainer = Trainer(\n","    model=baseline_model,                         # the instantiated  Transformers model to be trained\n","    args=training_args,                  # training arguments, defined above\n","    data_collator=baseline_data_collator,\n","    eval_dataset=baseline_val          # evaluation dataset\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1374,"status":"ok","timestamp":1683734089955,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"s-icBL6cYYYw","outputId":"2621bbb0-3ae1-40b0-e12c-7c54828089f0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Perplexity: 28.58\n"]}],"source":["#Evaluating the baseline model using evaluation dataset on Perplexity  \n","eval_results = trainer.evaluate()\n","print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"]},{"cell_type":"markdown","metadata":{"id":"XUPGz6aPbgkI"},"source":["##BLEU Score Evaluation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7060,"status":"ok","timestamp":1683735941869,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"uQ8Y7bKxXvqQ","outputId":"61f0058d-4c2a-4e61-e5e9-e5b6db11200a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.0)\n","Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.12.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.22.4)\n","Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.6)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n","Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.27.1)\n","Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from evaluate) (4.65.0)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from evaluate) (3.2.0)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.70.14)\n","Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2023.4.0)\n","Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.14.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n","Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.18.0)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.8.4)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (6.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (3.12.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (4.5.0)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (1.26.15)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2022.12.7)\n","Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (3.4)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2022.7.1)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.1.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.4)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.2)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.3)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n"]}],"source":["#Installing the evaluate library for BLEU score evaluation \n","!pip install evaluate"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"so-YrmBcXVKo"},"outputs":[],"source":["from evaluate import load\n","#Loading the BLEU score metric\n","bleu = load(\"bleu\")\n","\n","# predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n","# references = [\"hello there general kenobi\",\"foo bar foobar\"]\n","# results = bleu.compute(predictions=predictions, references=references)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":161,"status":"ok","timestamp":1683683825558,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"pP3TfSyTYCSu","outputId":"e7144a9f-caf6-4d3f-849d-c742e72203a0"},"outputs":[{"name":"stdout","output_type":"stream","text":["{'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.1666666666666667, 'translation_length': 7, 'reference_length': 6}\n"]}],"source":["# print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0mlVIU3ld3dD"},"outputs":[],"source":["#Function to separate the ingredients from the recipe \n","def input_to_model(x):\n","  input = ' '.join(x.split()[:x.split().index('Recipe:')+1])\n","  return input"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":105,"status":"ok","timestamp":1683735970289,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"TifqlC5-cpAM","outputId":"da26a9e6-df13-4a91-f80c-a4b7dbcd1cf3"},"outputs":[{"data":{"text/plain":["0    Generate a recipe using following Ingredients:...\n","1    Generate a recipe using following Ingredients:...\n","2    Generate a recipe using following Ingredients:...\n","3    Generate a recipe using following Ingredients:...\n","4    Generate a recipe using following Ingredients:...\n","Name: Input, dtype: object"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["baseline_val.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":119,"status":"ok","timestamp":1683735968180,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"6c1BKESHYOVA","outputId":"639ff468-086b-4779-aa18-53e5b807eeb9"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-1559b175-1724-48a1-81b8-0050bcc8d815\">\n","    <div 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>ingredients</th>\n","      <th>instructions</th>\n","      <th>Input</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[fresh strawberries, strawberry Jell-O, boilin...</td>\n","      <td>Put strawberries in blender container; cover a...</td>\n","      <td>Ingredients: [fresh strawberries, strawberry J...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>['1 small butternut squash (about 1 1/2 pounds...</td>\n","      <td>1) Halve, seed and peel squash. Cut into bite-...</td>\n","      <td>Ingredients: ['1 small butternut squash (about...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[margarine, sugar, egg, milk, salt, baking pow...</td>\n","      <td>Cream the margarine. Add sugar and beat until ...</td>\n","      <td>Ingredients: [margarine, sugar, egg, milk, sal...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[dinner rolls, brown sugar, regular butterscot...</td>\n","      <td>Grease and flour Bundt cake pan. Place frozen ...</td>\n","      <td>Ingredients: [dinner rolls, brown sugar, regul...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[apple, lemon juice, trout]</td>\n","      <td>Combine apple slices and lemon juice in medium...</td>\n","      <td>Ingredients: [apple, lemon juice, trout] Recip...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1559b175-1724-48a1-81b8-0050bcc8d815')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-1559b175-1724-48a1-81b8-0050bcc8d815 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                                         ingredients  \\\n","0  [fresh strawberries, strawberry Jell-O, boilin...   \n","1  ['1 small butternut squash (about 1 1/2 pounds...   \n","2  [margarine, sugar, egg, milk, salt, baking pow...   \n","3  [dinner rolls, brown sugar, regular butterscot...   \n","4                        [apple, lemon juice, trout]   \n","\n","                                        instructions  \\\n","0  Put strawberries in blender container; cover a...   \n","1  1) Halve, seed and peel squash. Cut into bite-...   \n","2  Cream the margarine. Add sugar and beat until ...   \n","3  Grease and flour Bundt cake pan. Place frozen ...   \n","4  Combine apple slices and lemon juice in medium...   \n","\n","                                               Input  \n","0  Ingredients: [fresh strawberries, strawberry J...  \n","1  Ingredients: ['1 small butternut squash (about...  \n","2  Ingredients: [margarine, sugar, egg, milk, sal...  \n","3  Ingredients: [dinner rolls, brown sugar, regul...  \n","4  Ingredients: [apple, lemon juice, trout] Recip...  "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df_val.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HLIZ2SDWbjZw"},"outputs":[],"source":["#Selecting 5000 validation samples to be evaluated using BLEU score \n","num_of_samples = 5000\n","df_val = df_val[:num_of_samples]\n","baseline_val = baseline_val[:num_of_samples]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XAD52svxb4_W"},"outputs":[],"source":["#Creating dataframes for the FT model and baseline model to store the Reference and the Input to the models\n","test_baseline_val = pd.DataFrame(columns = ['Input','Reference'])\n","test_base_val = pd.DataFrame(columns = ['Input','Reference'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xpmxoaLDYTS7"},"outputs":[],"source":["test_base_val['Reference'] = df_val['Input']\n","test_baseline_val['Reference'] = baseline_val\n","test_base_val['Input'] = df_val['Input'].apply(input_to_model)\n","test_baseline_val['Input'] = baseline_val.apply(input_to_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5lDQxmJIei17"},"outputs":[],"source":["#Load the models and tokenizers\n","\n","baseline_model = AutoModelForCausalLM.from_pretrained(\"distilgpt2\")\n","baseline_tokenizer = AutoTokenizer.from_pretrained('distilgpt2') \n","\n","base_model = AutoModelForCausalLM.from_pretrained(\"/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2_20\")\n","baseline_tokenizer = AutoTokenizer.from_pretrained('/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2Tokenizer') "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CMeuI7gEesy0"},"outputs":[],"source":["from transformers import pipeline\n","#creating pipelines for FT model and baseline model\n","baseline_generator = pipeline('text-generation', model=baseline_model, tokenizer = baseline_tokenizer)\n","base_generator = pipeline('text-generation', model=base_model, tokenizer = base_tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":234,"status":"ok","timestamp":1683736143329,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"4p4tzOr0fl9Q","outputId":"fe44d989-e1a5-4c6f-8f02-b28637905f3f"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-3a41b42e-1786-480e-922d-a6aea6e3f2d7\">\n","    <div 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>Input</th>\n","      <th>Reference</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","      <td>Generate a recipe using following Ingredients:...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a41b42e-1786-480e-922d-a6aea6e3f2d7')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                                               Input  \\\n","0  Generate a recipe using following Ingredients:...   \n","1  Generate a recipe using following Ingredients:...   \n","2  Generate a recipe using following Ingredients:...   \n","3  Generate a recipe using following Ingredients:...   \n","4  Generate a recipe using following Ingredients:...   \n","\n","                                           Reference  \n","0  Generate a recipe using following Ingredients:...  \n","1  Generate a recipe using following Ingredients:...  \n","2  Generate a recipe using following Ingredients:...  \n","3  Generate a recipe using following Ingredients:...  \n","4  Generate a recipe using following Ingredients:...  "]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["test_baseline_val.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GWxxNoFWf2vF"},"outputs":[],"source":["num_beams = 1 \n","top_p = 1.0\n","do_sample = False\n","\n","#Change num_beams to more than 1, to get a beam search decoding\n","#If do_sample is true, then ancestral sampling is performed\n","#Include top_p in ancestral to get Nucleus sampling"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"refKgzXmmoDi"},"outputs":[],"source":["#Greedy, Ancestral and Nucleus\n","decoding_strats = [[1,1.0, False],[1,1.0, True],[1,0.9, True]]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jceowrLCeKtV"},"outputs":[],"source":["#For each input, model performs recipe generation, then the prediction and the reference are stored for later use in BLEU score evaluation\n","\n","#BLEU score eval of FT model\n","results = []\n","predictions = []\n","references = []\n","for x in decoding_strats:\n","  num_beams, top_p, do_sample = x\n","  for idx in test_base_val.iterrows():\n","    references.append(test_base_val['Reference'].iloc[idx])\n","    predictions.append(base_generator(str(test_base_val['Input'].iloc[idx]), num_beams = num_beams, top_p = top_p, do_sample = do_sample, max_length = 150)[0]['generated_text'])\n","  base_results = bleu.compute(predictions=predictions, references=references)\n","  results.append(base_results)\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":162363,"status":"ok","timestamp":1683738419065,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"h9lM87pvgS9O","outputId":"efb1db95-b5f4-4e5f-ebf7-77869fbb4a89"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["[{'bleu': 0.3061613521424476, 'precisions': [0.3739198743126473, 0.30324623911322246, 0.2841181165203512, 0.2727272727272727], 'brevity_penalty': 1.0, 'length_ratio': 1.041734860883797, 'translation_length': 1273, 'reference_length': 1222}, {'bleu': 0.30453436032452413, 'precisions': [0.3788476716653512, 0.29912490055688146, 0.2806736166800321, 0.2704122877930477], 'brevity_penalty': 1.0, 'length_ratio': 1.0368248772504092, 'translation_length': 2534, 'reference_length': 2444}, {'bleu': 0.3067048763117223, 'precisions': [0.38295064660860384, 0.300877893056664, 0.2823813354786806, 0.2719653960529873], 'brevity_penalty': 1.0, 'length_ratio': 1.0335515548281506, 'translation_length': 3789, 'reference_length': 3666}]\n"]}],"source":["#BLEU score eval of baseline model\n","results = []\n","predictions = []\n","references = []\n","for x in decoding_strats:\n","  num_beams, top_p, do_sample = x\n","  for idx, row in test_baseline_val.iterrows():\n","    references.append(test_baseline_val['Reference'].iloc[idx])\n","    predictions.append(baseline_generator(str(test_baseline_val['Input'].iloc[idx]), num_beams = num_beams, top_p = top_p, do_sample = do_sample, max_length = 150)[0]['generated_text'])\n","  baseline_results = bleu.compute(predictions=predictions, references=references)\n","  results.append(baseline_results)\n","print(results)"]},{"cell_type":"markdown","metadata":{"id":"kmHo69Z39qru"},"source":["##Generations by the Model \n","\n","Here you can test the model on different ingredients as input and see what recipe it generates. Can see affect by try different decoding strategies and changing the maximum length"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RxSbaORo9tu6"},"outputs":[],"source":["from transformers import AutoModelForCausalLM, AutoTokenizer\n","base_model = AutoModelForCausalLM.from_pretrained('/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2_20')\n","base_tokenizer = AutoTokenizer.from_pretrained(\"/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2Tokenizer/\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4797,"status":"ok","timestamp":1683906166488,"user":{"displayName":"Ashvath Balgovind","userId":"07649613257406864791"},"user_tz":240},"id":"d_lUh8pyEUQv","outputId":"6e02da69-7c2b-49d6-89af-d42bdea24e69"},"outputs":[{"name":"stderr","output_type":"stream","text":["Xformers is not installed correctly. If you want to use memorry_efficient_attention to accelerate training use the following command to install Xformers\n","pip install xformers.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Ingredients:  [rice, egg, chicken, shrimp, soy sauce, sesame oil, salt, pepper, green onion] Recipe: Cook rice and set aside.  In a large nonstick frying pan, add olive oil and heat until almost smoking. Add chicken/seitan and cook covered for 10 minutes. Add shrimp and cook for 2-3 minutes. Add egg, cook until egg is cooked (scrambled). Add rice and stir up; add soy sauce and seasonings.  Garnish with chopped scallions and/or crushed red pepper. Serve hot. Enjoy.  Makes 4-6 servings as a main dish. Cooking time is 15-20 minutes.  Enjoy.  Serves 2.5. Per'}]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["from transformers import pipeline\n","\n","input = \"Ingredients:  [rice, egg, chicken, shrimp, soy sauce, sesame oil, salt, pepper, green onion] Recipe:\"\n","generator = pipeline(\"text-generation\", model=base_model, tokenizer = base_tokenizer)\n","generator(input, max_length=150)"]},{"cell_type":"markdown","metadata":{"id":"1JWLbKTShxAW"},"source":["##Plotting of loss curve"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ULWKB4_qh0rN"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vH68esWXh8nR"},"outputs":[],"source":["#Loading the trainer_state json file, which consists of the losses at different steps/epochs\n","df = pd.read_json('/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2_20/trainer_state.json')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":316,"status":"ok","timestamp":1683757226627,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"xuJbD8OgwEpI","outputId":"7f23623c-f95d-4302-a827-da2b428c8656"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-e5b69ac3-bc0e-46a2-bc53-394d1f91d536\">\n","    <div 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>best_metric</th>\n","      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trial_params  \n","0                20  616695923127656448         NaN           NaN  \n","1                20  616695923127656448         NaN           NaN  \n","2                20  616695923127656448         NaN           NaN  \n","3                20  616695923127656448         NaN           NaN  \n","4                20  616695923127656448         NaN           NaN  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8rox5SYJw4CL"},"outputs":[],"source":["#Abstraction of the loss and epochs from the log history \n","def get_epoch(x):\n","  return x['epoch']\n","\n","def get_loss(x):\n","  return x['loss']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MzgsgU0JQApg"},"outputs":[],"source":["#Organize in a way to extract losses and steps\n","df['epoch_number'] = df['log_history'].apply(get_epoch)\n","df ['loss'] = df['log_history'].apply(get_loss)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"executionInfo":{"elapsed":670,"status":"ok","timestamp":1683757715231,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"S-XSGEbIh5Zb","outputId":"7298323c-44bf-424c-d62b-f9d44775a2f5"},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Training curve DistilGPT2')"]},"execution_count":23,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#Plotting of the training loss curve for the FT Model \n","plt.plot(df['epoch_number'].values, df['loss'].values) \n","plt.xlabel('#Epochs')\n","plt.ylabel('Loss value')\n","plt.title('Training curve DistilGPT2')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VJgkVinLU23w"},"outputs":[],"source":["training_args = torch.load('/content/drive/MyDrive/685 Project/training_args.bin')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":189,"status":"ok","timestamp":1683732810480,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"DrrWHJ3GS6u4","outputId":"86ca5c06-b450-40b9-b06d-84271ba290bf"},"outputs":[{"name":"stdout","output_type":"stream","text":["TrainingArguments(\n","_n_gpu=1,\n","adafactor=False,\n","adam_beta1=0.9,\n","adam_beta2=0.999,\n","adam_epsilon=1e-08,\n","auto_find_batch_size=False,\n","bf16=False,\n","bf16_full_eval=False,\n","data_seed=None,\n","dataloader_drop_last=False,\n","dataloader_num_workers=0,\n","dataloader_pin_memory=True,\n","ddp_bucket_cap_mb=None,\n","ddp_find_unused_parameters=None,\n","ddp_timeout=1800,\n","debug=[],\n","deepspeed=None,\n","disable_tqdm=False,\n","do_eval=False,\n","do_predict=False,\n","do_train=False,\n","eval_accumulation_steps=None,\n","eval_delay=0,\n","eval_steps=None,\n","evaluation_strategy=no,\n","fp16=True,\n","fp16_backend=auto,\n","fp16_full_eval=False,\n","fp16_opt_level=O1,\n","fsdp=[],\n","fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n","fsdp_min_num_params=0,\n","fsdp_transformer_layer_cls_to_wrap=None,\n","full_determinism=False,\n","gradient_accumulation_steps=8,\n","gradient_checkpointing=False,\n","greater_is_better=None,\n","group_by_length=False,\n","half_precision_backend=cuda_amp,\n","hub_model_id=None,\n","hub_private_repo=False,\n","hub_strategy=every_save,\n","hub_token=<HUB_TOKEN>,\n","ignore_data_skip=False,\n","include_inputs_for_metrics=False,\n","jit_mode_eval=False,\n","label_names=None,\n","label_smoothing_factor=0.0,\n","learning_rate=0.0005,\n","length_column_name=length,\n","load_best_model_at_end=False,\n","local_rank=-1,\n","log_level=passive,\n","log_level_replica=warning,\n","log_on_each_node=True,\n","logging_dir=./model_RG_distilGPT_20\\runs\\May09_20-08-23_Anshumaan,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=500,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=1.0,\n","max_steps=-1,\n","metric_for_best_model=None,\n","mp_parameters=,\n","no_cuda=False,\n","num_train_epochs=20,\n","optim=adamw_hf,\n","optim_args=None,\n","output_dir=./model_RG_distilGPT_20,\n","overwrite_output_dir=True,\n","past_index=-1,\n","per_device_eval_batch_size=8,\n","per_device_train_batch_size=8,\n","prediction_loss_only=False,\n","push_to_hub=False,\n","push_to_hub_model_id=None,\n","push_to_hub_organization=None,\n","push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n","ray_scope=last,\n","remove_unused_columns=True,\n","report_to=[],\n","resume_from_checkpoint=None,\n","run_name=./model_RG_distilGPT_20,\n","save_on_each_node=False,\n","save_safetensors=False,\n","save_steps=5000,\n","save_strategy=steps,\n","save_total_limit=None,\n","seed=42,\n","sharded_ddp=[],\n","skip_memory_metrics=True,\n","tf32=None,\n","torch_compile=False,\n","torch_compile_backend=None,\n","torch_compile_mode=None,\n","torchdynamo=None,\n","tpu_metrics_debug=False,\n","tpu_num_cores=None,\n","use_ipex=False,\n","use_legacy_prediction_loop=False,\n","use_mps_device=False,\n","warmup_ratio=0.0,\n","warmup_steps=1000,\n","weight_decay=0.01,\n","xpu_backend=None,\n",")\n"]}],"source":["#Training arguments used in the training process of the FT model \n","print(training_args)"]},{"cell_type":"markdown","metadata":{"id":"iIFxhP_fq1LB"},"source":["##Adversarial Input Testing (Error Analysis)\n","\n","There are several input cases, where the baseline model fails to follow the pattern it is supposed to, in many cases these are also called Adversarial Inputs to the model. In our experimentation we found four such failure cases of the model: \\\\\n","\n","1) Ingredients to the model are some non-edible products \\\\\n","2) The number of ingredients are too less to create a recipe \\\\\n","3) Too many ingredients sepcified that the model is not able to utilize them all \\\\\n","4) Removal of the phrase 'Recipe:' from the input - model adds new ingredients "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":98,"referenced_widgets":["09d8e2fdc26a4312974031a45a4cdaed","cbee4dd25aa349c8bbf0bfd859f42905","d05fb2a13b804db4b0f25de3822cc55a","007a3a6c6e534ede98a7905e5fac5944","feae9fb793294426bf85a466b2cf3070","3b370b1921724d9c9ff0dd21b0d698bf","a860396354fc4f4bba9bf37a12773319","0c3de9253f1c4d4b9dc21f06d1c99a08","693d70e7cc124232b1b76e7a00247799","2a1b80f3404546bda28e86757652992a","cdc86dacf0184eb394b7f7c8c9309808","bca3ba12f68d4cbeaa27c23976909b89","8df3625cbd89416dbf27e46f49db1629","a876d79abe74404698278fa6fc28ed67","ceba93b7804748ae9ffd092dea841d28","58c8f0e57d724ab2885b4ae42cf7c49b","5b4fcfe99cde41d58a9a287dabfac3ee","83d3802a29f946709d7110e762252684","65f2dfc614a54067a473c9ac2042bb9d","5fc4959b8edd4e758316d224d8fc1620","cd24fe301ac84ecea36e227e8aa2d089","4d2c84583615457481a7d521a48ed2ba","c7063af602904665b687a220709e4ab7"]},"executionInfo":{"elapsed":1066,"status":"ok","timestamp":1683740315034,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"OwptJpssrCyA","outputId":"477f1658-227c-4ca9-af88-0c5262de13ae"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"09d8e2fdc26a4312974031a45a4cdaed","version_major":2,"version_minor":0},"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cbee4dd25aa349c8bbf0bfd859f42905","version_major":2,"version_minor":0},"text/plain":["Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8df3625cbd89416dbf27e46f49db1629","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["baseline_tokenizer = AutoTokenizer.from_pretrained('distilgpt2')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pRhi0G2trxKm"},"outputs":[],"source":["baseline_tokenizer.pad_token = baseline_tokenizer.eos_token"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["9213c102a5db4d0fb4de7b9aed15cba8","96768e8ba463427799853b0065e81389","d93c5bd4b2374cd6b6c40605ca2baaae","493425db60fa4fd3bb4174811e18cacb","0d2d16328d344d24a6a2ffbd5e20b906","61a5e10838d54f718965e292c557f64d","cfe2dd8c349d4b1c830468583d1767ff","4b3956964410475c9c76c462abd996a6","365f91f90a7d441abce2755b08883f30","7e16f007377947ce837ee2aebad8b55a","f12ee430367a4e63acddd8bd56b1cf7f","9a1cd9cc6a0948609f555282624a1dab","9e0c11efdf7649eb8af18f28969a28f9","32dd28e45fbd41e3ba6abe58359099e2","12468db3b7a645db90f186018881dea0","65c718554a4748fb8bb5a9c371836cb0","2c063f6c0cd04a0787648cd6e4c846e1","db5680770f91452f860d9744e949702b","b3d38e49424a47029396ac64de76b982","db344e7dffbe4d9db687a104e38948bb","810471e12df94dbd974119f9089a3a8f","f5bb20bec9224c13b2002006a0aabc7e"]},"executionInfo":{"elapsed":11952,"status":"ok","timestamp":1683740330008,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"aiDl_EEVritY","outputId":"84cb4799-3094-4a51-fb8a-138d4aafdb8c"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9213c102a5db4d0fb4de7b9aed15cba8","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/353M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9a1cd9cc6a0948609f555282624a1dab","version_major":2,"version_minor":0},"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["baseline_model = AutoModelForCausalLM.from_pretrained(\"distilgpt2\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PKwOmpz4rx-q"},"outputs":[],"source":["from transformers import pipeline\n","baseline_generator = pipeline('text-generation', model=baseline_model, tokenizer = baseline_tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7188,"status":"ok","timestamp":1683740338141,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"apTKIXQXsZlq","outputId":"c4727415-72c3-4c22-ec46-ff9ffce62823"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n","  warnings.warn(\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [strawberry, car, plastic, tire] Recipe: [recipe, sour cream, pumpkin juice, ice cream, pumpkin jam, lime (not added), pumpkin pie\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTo order this recipe, you can either use a recipe from this cookbook or create a separate page to add the recipe to the page. For example, my recipe for dressing ice cream, frozen pumpkin pie and pineapple pie is the link for this recipe.\\nThis recipe can either be used by your own kitchen for your own baking and pie recipes, or by a kitchen for your own baked goods for your own kitchen, or by someone'}]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Non sensical inputs\n","input = \"Generate a recipe using following Ingredients: [strawberry, car, plastic, tire] Recipe:\"\n","baseline_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5335,"status":"ok","timestamp":1683740343465,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"MHHemLxesN_W","outputId":"bdf6df2f-67bf-4c48-99b9-cea707ca018c"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [strawberry] Recipe: [fresh green apples] Recipe: [chocolate] Recipe: [sauce] Recipe: Recipe: [sauce] Recipe: [sauce] Recipe: [sauce] Recipe: [sauce] Recipe: [sauce] Nutrition Facts Nutritional Fat: 10g / grams Calories from Fat - Total Fat 1g / grams Fiber - Fat 6g / grams Fat - Total Fat 1g / grams Fiber - Fat 9g / grams Fiber - Fat 6g / grams Fat - Total Fat 1g / grams Fiber - Fat 5g / grams Fiber - Total Fat 1g / grams Fiber - Fat 6g / g Fiber'}]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["#Very few inputs\n","input = \"Generate a recipe using following Ingredients: [strawberry] Recipe:\"\n","baseline_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4369,"status":"ok","timestamp":1683740347823,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"BY-7i9xXtLTx","outputId":"d70fb062-8eef-4ccd-ca4f-202593fb5eff"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [1 tbsp white vinegar, 6 Coles Australian Free Range Eggs, 500g fresh fettuccine or pappardelle,  6 pancetta slices, 2 Coles Australian Free Range Egg yolks, 1/2 cup (125ml) thickened cream, 1/2 cup (40g) finely grated parmesan, 120g pkt Coles Australian Baby Spinach] Recipe: [2] Prep Time 10 minutes Servings - 3 2 - 4 Calories from Fat - 30 kcal Author : Marcel Gersberg\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n- Foul of the recipe\\n\\n\\n\\n\\n\\nHow many times do I have to use'}]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["#Large number of inputs\n","input = \"Generate a recipe using following Ingredients: [1 tbsp white vinegar, 6 Coles Australian Free Range Eggs, 500g fresh fettuccine or pappardelle,  6 pancetta slices, 2 Coles Australian Free Range Egg yolks, 1/2 cup (125ml) thickened cream, 1/2 cup (40g) finely grated parmesan, 120g pkt Coles Australian Baby Spinach] Recipe:\"\n","baseline_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3531,"status":"ok","timestamp":1683740351341,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"CJiTdSLQtNle","outputId":"af1b9eb4-1a83-49e2-a570-09daa61df3c8"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread] 1 cup of vanilla extract\\n1 tablespoon of ciander / vanilla extract Instructions Preheat the oven to 300-200 F. In a 350F oven, bring the milk, milk, and vanilla extract into a bowl. Slowly whisk together the spinach, mozzarella, oregano, and bay leaves or chopped ginger. Add the green juice and the remaining coconut milk, then stir to combine the spinach with the milk and juice. Remove from the oven and let cool for 40-45 minutes.\\nPlace a tray on a baking sheet and place it in an oven until golden brown and completely golden brown. The topping will look nice on the surface'}]"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Exclusion of Recipe\n","input = \"Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread]\"\n","baseline_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FBRfSaBYzElU"},"outputs":[],"source":["base_tokenizer = AutoTokenizer.from_pretrained('/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2Tokenizer')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FHqabMRrzMGl"},"outputs":[],"source":["base_model = AutoModelForCausalLM.from_pretrained('/content/drive/MyDrive/685 Project/RG_Model/DistilGPT2_20')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pGt0SNnnzPZd"},"outputs":[],"source":["base_generator = pipeline('text-generation', model=base_model, tokenizer = base_tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10179,"status":"ok","timestamp":1683758218745,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"nR7fzXCvzWE0","outputId":"3221642d-a6e5-4bd6-fe53-c9c5fe845b13"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': \"Generate a recipe using following Ingredients: [strawberry, car, plastic, tire] Recipe: Combine all ingredients in a large punch bowl and punch bowl. If you have trouble getting punch right down and you need something to help you have to do with. If you don't have Aguher, you might want to add sugar or sugar. Pour punch over crushed ice into a punch bowl and enjoy punch. I add an extra splash of lime juice to the punch bowl. Serves 50. This can easily be halved and put in a punch bowl. Can be made in large quantities and frzn. Toss lightly but do not make too thick. Serves 60. Can be doubled. Enjoy. Serves 100. Food exchange.\"}]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# Non sensical inputs\n","input = \"Generate a recipe using following Ingredients: [strawberry, car, plastic, tire] Recipe:\"\n","base_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6917,"status":"ok","timestamp":1683758225660,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"6-a4GGu-zWkf","outputId":"eb46eaff-e954-4005-b279-67c67e1c7fc7"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [strawberry] Recipe: Combine ingredients in a bowl and let stand at room temperature for 24 hours.  Notes: Substitute orange juice for white grape juice with orange juice. 1 1/2 jasmine tea bag, loosely packed. Pour a tsp or so into each cup of a teacup and fill with tequila. Serve chilled with or without a garnish of fresh fruit and sugar. Yield: 2 1/2 cups or 32 servings (approx. 0 cup per serving).  Read More http://www.ethanks-basting.com/recipe/recipe/ugget-cups.html?oc=linkback.AVier-v favoring'}]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["#Very few inputs\n","input = \"Generate a recipe using following Ingredients: [strawberry] Recipe:\"\n","base_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4484,"status":"ok","timestamp":1683758230137,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"_Hfl1KEFzXu4","outputId":"8cce0d42-9e69-4904-ff49-09870911c15e"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': 'Generate a recipe using following Ingredients: [1 tbsp white vinegar, 6 Coles Australian Free Range Eggs, 500g fresh fettuccine or pappardelle,  6 pancetta slices, 2 Coles Australian Free Range Egg yolks, 1/2 cup (125ml) thickened cream, 1/2 cup (40g) finely grated parmesan, 120g pkt Coles Australian Baby Spinach] Recipe: Bring the vinegar to the boil in a small saucepan, remove from the heat and cool to room temperature. Cook the pasta in a large pan of salted boiling water according to the packet directions. Add 3 tbsp (45ml) of the cooked pasta into the pan, and cook for'}]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["#Large number of inputs\n","input = \"Generate a recipe using following Ingredients: [1 tbsp white vinegar, 6 Coles Australian Free Range Eggs, 500g fresh fettuccine or pappardelle,  6 pancetta slices, 2 Coles Australian Free Range Egg yolks, 1/2 cup (125ml) thickened cream, 1/2 cup (40g) finely grated parmesan, 120g pkt Coles Australian Baby Spinach] Recipe:\"\n","base_generator(input, max_length=150)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5716,"status":"ok","timestamp":1683758235846,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":240},"id":"z8fCNJt3zY3h","outputId":"e3aad286-3f6c-4f61-83d5-03aa8dd309d0"},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"data":{"text/plain":["[{'generated_text': \"Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread] Recipe: Whiz everything together in a blender until well blended and it becomes a thick spread.  Make sure you mix all the ingredients in a blender as this makes a smooth spread.  Spread that mixture on to the bread.  Place under the broiler about 4 minutes, or until the cheese browns and bubbles.  Serve.  Note: you may be able to slice these bread into about 10 pieces and spread them out a bit so they won't dry out. Also, the spread makes a nice finishing touch if you don't go big around with a spoon. Enjoy. Yum! :)!!!!! http://www.therealb\"}]"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["# Exclusion of Recipe\n","input = \"Generate a recipe using following Ingredients: [strawberry, cheese, milk, bread]\"\n","base_generator(input, max_length=150)"]},{"cell_type":"markdown","metadata":{"id":"s_q3PB6Zg4_5"},"source":["##Sampling generations from the model for TST\n","\n","In this section, we are taking 200 recipes that will be generated (by FT model) using inputs taken from the validation set. These recipes will be later used in the Text Style Transfer notebook. "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QAGnk12mF86x"},"outputs":[],"source":["# Extraction of the Ingredients from the entire input \n","def input_to_model(x):\n","  input = ' '.join(str(x).split()[:str(x).split().index('Recipe:')+1])\n","  return input"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NG8MrL4oD8jC"},"outputs":[],"source":["#Taking only 200 examples from the validation dataset \n","df_val = df_val[:200]\n","df_val['M_Input'] = df_val['Input'].apply(input_to_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":128,"status":"ok","timestamp":1683861467376,"user":{"displayName":"Anshumaan Chauhan","userId":"08247611702232216431"},"user_tz":360},"id":"3fJD_T_k84q1","outputId":"16985168-fb0e-4da2-9350-f7d113a95c89"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-61925f3e-dd46-429b-b8e3-a1cd6f5549c0\">\n","    <div 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>ingredients</th>\n","      <th>instructions</th>\n","      <th>Input</th>\n","      <th>M_Input</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[fresh strawberries, strawberry Jell-O, boilin...</td>\n","      <td>Put strawberries in blender container; cover a...</td>\n","      <td>Ingredients: [fresh strawberries, strawberry J...</td>\n","      <td>Ingredients: [fresh strawberries, strawberry J...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>['1 small butternut squash (about 1 1/2 pounds...</td>\n","      <td>1) Halve, seed and peel squash. Cut into bite-...</td>\n","      <td>Ingredients: ['1 small butternut squash (about...</td>\n","      <td>Ingredients: ['1 small butternut squash (about...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[margarine, sugar, egg, milk, salt, baking pow...</td>\n","      <td>Cream the margarine. Add sugar and beat until ...</td>\n","      <td>Ingredients: [margarine, sugar, egg, milk, sal...</td>\n","      <td>Ingredients: [margarine, sugar, egg, milk, sal...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[dinner rolls, brown sugar, regular butterscot...</td>\n","      <td>Grease and flour Bundt cake pan. 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Place frozen ...   \n","4  Combine apple slices and lemon juice in medium...   \n","\n","                                               Input  \\\n","0  Ingredients: [fresh strawberries, strawberry J...   \n","1  Ingredients: ['1 small butternut squash (about...   \n","2  Ingredients: [margarine, sugar, egg, milk, sal...   \n","3  Ingredients: [dinner rolls, brown sugar, regul...   \n","4  Ingredients: [apple, lemon juice, trout] Recip...   \n","\n","                                             M_Input  \n","0  Ingredients: [fresh strawberries, strawberry J...  \n","1  Ingredients: ['1 small butternut squash (about...  \n","2  Ingredients: [margarine, sugar, egg, milk, sal...  \n","3  Ingredients: [dinner rolls, brown sugar, regul...  \n","4   Ingredients: [apple, lemon juice, trout] Recipe:  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