Sentiment Analysis/Twitter Sentiment Analysis/Twitter Sentiment Analysis_v2.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"48WpXM-ztDvj"},"source":["## US Airlines Sentiment Analysis from Tweets: Sentiment Analysis\n","\n","Dataset Source: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment"]},{"cell_type":"markdown","metadata":{"id":"h9vZDt6rtDvm"},"source":["#### Import Necessary Libraries"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14431,"status":"ok","timestamp":1680544420664,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"iNptfrRltDvm","outputId":"04d20497-05d5-43d2-8e79-d5705ef811e5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Git LFS initialized.\n"]}],"source":["import os, sys\n","os.environ['TOKENIZERS_PARALLELISM']='false'\n","\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","import datasets\n","from datasets import Dataset, DatasetDict\n","\n","import torch\n","from torch import nn\n","\n","import transformers\n","from transformers import AutoModelForSequenceClassification\n","from transformers import TrainingArguments, Trainer, set_seed\n","from transformers import DataCollatorWithPadding, AutoTokenizer\n","from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n","\n","import evaluate\n","\n","!git lfs install"]},{"cell_type":"markdown","metadata":{"id":"a2xIIlB9tDvo"},"source":["#### Display Library Versions"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1680544447162,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"zTFyF9eqtDvo","outputId":"2fd15d1c-4d28-4a24-db4f-74e1a63292f9"},"outputs":[{"name":"stdout","output_type":"stream","text":["        Python: 3.9.12\n","         NumPy: 1.24.2\n","        Pandas: 1.5.0\n","      Datasets: 2.9.0\n","  Transformers: 4.26.1\n","         Torch: 1.12.1\n","      Evaluate: 0.4.0\n"]}],"source":["print(\"Python:\".rjust(15), sys.version[0:6])\n","print(\"NumPy:\".rjust(15), np.__version__)\n","print(\"Pandas:\".rjust(15), pd.__version__)\n","print(\"Datasets:\".rjust(15), datasets.__version__)\n","print(\"Transformers:\".rjust(15), transformers.__version__)\n","print(\"Torch:\".rjust(15), torch.__version__)\n","print(\"Evaluate:\".rjust(15), evaluate.__version__)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"yCtyFLejtDvp"},"source":["#### Ingest & Start Preprocessing Dataset"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":625,"status":"ok","timestamp":1680544447784,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"qWHSbE2stDvp","outputId":"02348169-185d-4ea7-9d14-fe77432d72cf"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 14604 entries, 0 to 14639\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count  Dtype \n","---  ------  --------------  ----- \n"," 0   labels  14604 non-null  object\n"," 1   text    14604 non-null  object\n","dtypes: object(2)\n","memory usage: 342.3+ KB\n"]}],"source":["parent_dir = \"/Users/briandunn/Desktop/data\"\n","\n","data = pd.read_csv(os.path.join(parent_dir, \"Tweets.csv\"))\n","\n","data['airline_sentiment'] = data['airline_sentiment'].str.strip()\n","data['text'] = data['text'].str.strip()\n","\n","data.rename(columns={'airline_sentiment' : 'labels'}, inplace=True)\n","\n","data.drop_duplicates(inplace=True)\n","\n","data.drop(columns=['tweet_id', 'negativereason', \n","                   'negativereason_confidence', 'tweet_coord', \n","                   'tweet_created', 'tweet_location', \n","                   'user_timezone', 'airline_sentiment_gold', \n","                   'negativereason_gold', 'name', 'airline',\n","                   'airline_sentiment_confidence', 'retweet_count'],\n","          inplace=True)\n","\n","data.info()"]},{"cell_type":"markdown","metadata":{"id":"9IRyhkZhtDvp"},"source":["#### Define Dictionaries to Convert Between String & Integer Class Values"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1680544447784,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"xgfxmd0LtDvq"},"outputs":[],"source":["unique_classes = data['labels'].unique()\n","\n","NUM_OF_LABELS = len(unique_classes)\n","\n","id2label = {idx:label for idx, label in enumerate(unique_classes)}\n","label2id = {label:idx for idx, label in enumerate(unique_classes)}"]},{"cell_type":"markdown","metadata":{"id":"Y13czITytDvq"},"source":["#### Visual: Histogram of Text Lengths"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":464},"executionInfo":{"elapsed":945,"status":"ok","timestamp":1680544448726,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"iufeb7MptDvq","outputId":"d22686ae-f964-4f4d-e977-77011405bb98"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1800x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["The maximum length of Speeches is 36 words.\n"]}],"source":["data['text_length'] = data['text'].str.split().str.len()\n","\n","n_bin=60\n","data.hist('text_length', grid=False, figsize=(18,8), range=(0,n_bin), bins=n_bin)\n","plt.title('Histogram of Tweet Lengths')\n","plt.xlabel('# of Words in Tweets')\n","plt.ylabel('# of Samples')\n","plt.show()\n","\n","print(f\"The maximum length of Speeches is {max(data['text_length'])} words.\")"]},{"cell_type":"markdown","metadata":{"id":"3O3pdq3ttDvr"},"source":["#### Visual: Tweet Length by Class"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":402},"executionInfo":{"elapsed":148,"status":"ok","timestamp":1680544448868,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"J6CF02r7tDvr","outputId":"c2d06cd4-1b36-47cc-e178-dfb1496b0e19"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x400 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data.boxplot(\"text_length\", by=\"labels\", grid=False, showfliers=False, color=\"Blue\", figsize=(10,4))\n","\n","plt.suptitle('')\n","plt.title('Tweet Length by Type')\n","plt.xlabel('Tweet Sentiments')\n","plt.ylabel('Words per Tweet')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"D3XMdwoExc0q"},"source":["#### Find Largest Tokenized Input Value"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162,"referenced_widgets":["06630af0f3054dc7902f0fc3fb60af56","7bd3501a269b48c88c3ad8ce25b55b81","e60d2b3ef0fb4014b48120364c433b30","217849950a6645adbb7dae4e535b15b6","cf29015b12d543a68e348a09b5f0468d","fe083c9c898645f283042f8ea93681da","4875122b3dc94ec885fb5a51eb933272","57879b46108643a6a51688012c700873","b926f9e58ed14b348ea423dfef62a4a1","854eeb5d7d1447159d6a2ac99de948fd","abee5241934c4195bb8d8972fd3bfb0b","74daf1b982b24786a6d0b4e1cbe3035a","3285a509923947bdb726425d8f5da30c","21395cdb75774d799fa58ef6fafc466f","45f1b4a74bfd46f2a10e875299d28d06","da69ad3ccdfe450fa2a0dd3280d50581","ba41a19fc4dc4e85a85f4f258a43968d","697c606a499d4fba8f2fa390e262b86c","ac28ce68eb794eac851faee79e63daaf","a32fc49ed03d4687a52f28dd7b1e9379","ddcb04ce9b874f0495e6f6358e6b80e7","5b9ab3a184ce485a9370ea9e37eca424","f0a4f85ae6914c27b22254c2e752671a","dfe74d7b869a432f966d3e3ab4449405","037e575f0b804833ae5906211e743420","835df5ee421544f8be9c95f559568a16","c2f2b440bb9e4bfe9f85b6feb6a9027f","ec46cbb07efc4416b0bcad5d222c091f","e993a88416a54a6c87c55e13cc42f300","2ec8141ca00c47b9a8d347f3ef65c383","8308ff17df3843bbb8e0954f3438011c","8e04fb183785478ba20fdd95cc578da4","8c3e71b69a3f454bb552a2975d24a1fb","362c25ec20f04e5d9c184ea28b9ee4e7","ccad3c4d3aeb41ee8d1d91cbe2da1032","ced5d09d2ab246f79ae2642f05744f87","fc7054b1ec6149199d30fbf67c254a63","0eb23176a71e46ee88b6aba1c1d94c5b","f2840bff3395486eb6961927ea43d4f3","6fe7f00172364cc6a0702ca17998e507","35800adda77a48ca95f1c15bc9423ebc","5f102ea2da834262af5120b9f7a86faf","0bb73b9e69744cf3ab482ae6e4a5051a","eed266801b494693a7fe64160f83fd00"]},"executionInfo":{"elapsed":2739,"status":"ok","timestamp":1680544451600,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"E9dHtChgxb1X","outputId":"b37d61ed-e9a0-4f8a-809a-9b5f2c2d066b"},"outputs":[{"name":"stdout","output_type":"stream","text":["The longest article length is 67 tokens.\n"]}],"source":["MODEL_CKPT = \"bert-base-uncased\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT, \n","                                             truncation=True, \n","                                             padding=True,\n","                                             batched=True, \n","                                             batch_size=None)\n","\n","text_len = [len(tokenizer.encode(a)) for a in data[\"text\"]]\n","\n","MAX_LENGTH = max(text_len)\n","\n","print(f\"The longest article length is {MAX_LENGTH} tokens.\")"]},{"cell_type":"markdown","metadata":{"id":"z1S5BwzktDvs"},"source":["#### Number of Samples in Each Class"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":1244,"status":"ok","timestamp":1680544452823,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"yhC5FMfztDvs","outputId":"03fd16a5-fe00-4d09-8b4e-8080090099f0"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["class_counts = data.groupby(\"labels\").count()\n","\n","class_counts['text_length'].plot.bar(label='labels', \n","                                     color=plt.cm.Paired(np.arange(len(data))))\n","plt.suptitle('')\n","plt.title('# of Samples of Each Class')\n","plt.xlabel('Class')\n","plt.ylabel('# of Samples')\n","plt.xticks(rotation = 0)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"a4omdrRJtDvr"},"source":["#### Final Data Preprocessing"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1680544452823,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"xQEyb4xRtDvr","outputId":"c9c6df93-a820-4dcf-a811-0a95c90426f5"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 14604 entries, 0 to 14639\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count  Dtype \n","---  ------  --------------  ----- \n"," 0   labels  14604 non-null  object\n"," 1   text    14604 non-null  object\n","dtypes: object(2)\n","memory usage: 342.3+ KB\n"]}],"source":["data = data.drop(columns=[\"text_length\"])\n","\n","data.info()"]},{"cell_type":"markdown","metadata":{"id":"FJ6BeUUCtDvs"},"source":["#### Convert Class Values From String to Integer & Convert DataFrame to Dataset"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1680544452823,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"9jsZvrT2tDvs"},"outputs":[],"source":["data = data.replace(label2id)\n","\n","dataset = Dataset.from_pandas(data)"]},{"cell_type":"markdown","metadata":{"id":"cryNvTiptDvs"},"source":["#### Split Dataset into Training, Testing & Evaluation Datasets"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":269,"status":"ok","timestamp":1680544453087,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"kRzHD0SotDvs","outputId":"932310dd-b8dd-47a9-f9de-420d4a09c64e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training Dataset Shape: (11683, 3)\n","Testing Dataset Shape: (1460, 3)\n","Evaluation Dataset Shape: (1461, 3)\n"]}],"source":["train_testEval = dataset.train_test_split(train_size=0.80)\n","test_eval = train_testEval['test'].train_test_split(train_size=0.50)\n","\n","ds = DatasetDict({\n","    'train' : train_testEval['train'],\n","    'test' : test_eval['train'],\n","    'eval' : test_eval['test'],\n","})\n","\n","print(\"Training Dataset Shape:\", ds['train'].shape)\n","print(\"Testing Dataset Shape:\", ds['test'].shape)\n","print(\"Evaluation Dataset Shape:\", ds['eval'].shape)"]},{"cell_type":"markdown","metadata":{"id":"7ukExf2vtDvt"},"source":["#### Basic Values/Constants"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1680544453088,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"x2qdEUBHtDvt","outputId":"d977c7a1-546f-4bc0-caa3-af9e6079481b"},"outputs":[{"name":"stdout","output_type":"stream","text":["This project will utilize the CPU to train and evaluate.\n","This project will have a maximum length for the input text of 68 tokens.\n","The model checkpoint used for this project is: bert-base-uncased\n"]}],"source":["set_seed = 42\n","NUM_OF_EPOCHS = 12\n","\n","BATCH_SIZE = 64\n","LEARNING_RATE = 2e-5\n","\n","MAX_LENGTH = MAX_LENGTH + 1\n","MODEL_CKPT = \"bert-base-uncased\"\n","MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Twitter_Sentiment_Analysis_v2\"\n","\n","METRIC_NAME = \"f1\"\n","DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","print(f\"This project will utilize the {str(DEVICE).upper()} to train and evaluate.\")\n","print(f\"This project will have a maximum length for the input text of {MAX_LENGTH} tokens.\")\n","print(f\"The model checkpoint used for this project is: {MODEL_CKPT}\")"]},{"cell_type":"markdown","metadata":{"id":"XKzDOeTgtDvt"},"source":["#### Tokenize Entire Dataset"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":139,"referenced_widgets":["f7319b7a81524414946df804b12e9e15","f08ef4a291e54090a8dfa0aedf8fcde7","f86a1ed3bfa84c7bbbe69133f77116a6","fe8b07e7eb3844a7b368d8208dd2e73a","3a83e912299544a1902ab03b62c7fda9","dacf211e148a4da8beb70ebf9736fa1d","2d9977ae35cc4739acd5f8999a7b8973","c2f88e1329f243b3a98833d635d02f29","c9b1f4bdf0754ffc8e7f2e78daa47d41","cc4b83266b4f4d75bdb732990beb63ba","06e394600caa4ef9bb22c22e22ab4015","8517f64332944d1cadbfb11df130c193","c8c43aec3c154b17bc23e7cca29e7550","9f37123d6a7c4501aab7c1b755bba6d8","345c072781914af092760b300ecaad67","bf79e4c3fe194f0883bd9da4bf15fb96","f8896e62be444071a9bf0240870b8d17","231f473fab9b4faea36eaab46738e19f","2ea51145997b4b88b13351b6cf767534","e509be44835543c4b47084361c2b0046","acf41a68893a44ecb8a6bd77d1d48081","ba078fb20c73466e9b4ce53690d3812e","f2750020668e4e759c32f9ec91fefe0b","d43fa214ab1044858a49b91312e5d500","33cb44faa95545a18c396d903f7c199f","c6815f989ced487c93a86647156cb001","31d978a302ec454589290fd261c7a009","7473a935f1d54974a50081dab3636499","ca77c36e6fe64db8a712d4c64e813435","7930891dcda64c38abc0f8cdf04df0a7","576db1e96bde495280078ac69cd78ac7","2939c9e6f84a469cb9234f9269d0538c","456ffed08c454459921706217e22d669"]},"executionInfo":{"elapsed":3642,"status":"ok","timestamp":1680544456723,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"bYW2DsTptDvt","outputId":"beadf8a8-d41f-4368-ace0-cb2f5d695c7f"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f613fe981ef84fa3bd7cf58e83db107d","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4c862484fca1410e9a91edbb58e6f792","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5102e3f701414fadbe8a8c07e75a07ec","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Training data shape: (11683, 6)\n","Testing data shape: (1460, 6)\n","Validation data shape: (1461, 6)\n","Training Dataset Information: Dataset({\n","    features: ['labels', 'text', '__index_level_0__', 'input_ids', 'token_type_ids', 'attention_mask'],\n","    num_rows: 11683\n","})\n"]}],"source":["# Define tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT, \n","                                          truncation=True, \n","                                          padding=True)\n","\n","# Method to tokenize entire dataset\n","def tokenize(batch):\n","    return tokenizer(batch['text'], \n","                     padding=True, \n","                     truncation=True, \n","                     max_length=MAX_LENGTH,\n","                     return_tensors=\"pt\")\n","\n","# Map the 'Tokenize' Method to All Samples in the Entire Dataset\n","encoded_data = ds.map(tokenize, \n","                      batched=True, \n","                      batch_size=None)\n","\n","print('Training data shape:', encoded_data['train'].shape)\n","print('Testing data shape:', encoded_data['test'].shape)\n","print('Validation data shape:', encoded_data['eval'].shape)\n","\n","print(f\"Training Dataset Information: {encoded_data['train']}\")"]},{"cell_type":"markdown","metadata":{"id":"DFmjmZ-EtDvt"},"source":["#### Instantiate Model"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["d201ed2111994de4b62ece42df034650","7a11cc83927d4869a99af06c45613a63","938dfa48a085455f9b1408961143be55","2a3a30f88bff40c2b4cca8846c725c86","18bf55b5d9cd47b0b267995256152a19","9d88eadf84f840f783393b42cf1e95c2","a0ea404f43e54094aa6085a5a9cfbdfa","05201843fcd14ab5872d6b97832d4c5e","8e0f92b6b7894ea1b2286a8ce52716f2","680f0b984ef44b35b3593587382ba0b7","493193cd5a0146e18ba13c1d6f9ed88e"]},"executionInfo":{"elapsed":5125,"status":"ok","timestamp":1680544461845,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"hAj9ZZJ_tDvt","outputId":"79d9cff8-1618-4d37-c28c-4e8894f202fb"},"outputs":[{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["model = AutoModelForSequenceClassification.from_pretrained(\n","        MODEL_CKPT,\n","        num_labels=NUM_OF_LABELS,\n","        id2label=id2label,\n","        label2id=label2id\n","        )"]},{"cell_type":"markdown","metadata":{"id":"bnEZXBaPtDvt"},"source":["#### Define Function to Compute Metrics"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1680544461846,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"e7eIV6J4tDvu"},"outputs":[],"source":["def compute_metrics(p):\n","    accuracy_metric = evaluate.load(\"accuracy\")\n","    accuracy = accuracy_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)['accuracy']\n","    \n","    ### ------------------- F1 scores -------------------\n","    \n","    f1_score_metric = evaluate.load(\"f1\")\n","    weighted_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='weighted')[\"f1\"]\n","    micro_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='micro')['f1']\n","    macro_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='macro')[\"f1\"]\n","    \n","    ### ------------------- recall -------------------\n","    \n","    recall_metric = evaluate.load(\"recall\")\n","    weighted_recall = recall_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='weighted')[\"recall\"]\n","    micro_recall = recall_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='micro')[\"recall\"]\n","    macro_recall = recall_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='macro')[\"recall\"]\n","    \n","    ### ------------------- precision -------------------\n","    \n","    precision_metric = evaluate.load(\"precision\")\n","    weighted_precision = precision_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='weighted')[\"precision\"]\n","    micro_precision = precision_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='micro')[\"precision\"]\n","    macro_precision = precision_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average='macro')[\"precision\"]\n","    \n","    return {\"accuracy\" : accuracy, \n","            \"Weighted F1\" : weighted_f1_score,\n","            \"Micro F1\" : micro_f1_score,\n","            \"Macro F1\" : macro_f1_score,\n","            \"Weighted Recall\" : weighted_recall,\n","            \"Micro Recall\" : micro_recall,\n","            \"Macro Recall\" : macro_recall,\n","            \"Weighted Precision\" : weighted_precision,\n","            \"Micro Precision\" : micro_precision,\n","            \"Macro Precision\" : macro_precision\n","            }"]},{"cell_type":"markdown","metadata":{"id":"ldjtd0m-tDvu"},"source":["#### Define TrainingArguments"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1680544461846,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"SNmda3b9tDvu"},"outputs":[],"source":["training_args = Seq2SeqTrainingArguments(\n","    output_dir=MODEL_NAME,\n","    num_train_epochs=NUM_OF_EPOCHS,\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE, \n","    per_device_eval_batch_size=BATCH_SIZE, \n","    weight_decay=0.01,\n","    log_level=\"error\",\n","    logging_strategy='epoch', \n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    logging_first_step=True,\n","    load_best_model_at_end=True,\n","    hub_private_repo=True,\n","    disable_tqdm=False,\n","    push_to_hub=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"JlsZz5lztDvu"},"source":["#### Define Data Collator"]},{"cell_type":"code","execution_count":19,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1680544461846,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"Hva-yJl4tDvu"},"outputs":[],"source":["data_collator = DataCollatorWithPadding(tokenizer)"]},{"cell_type":"markdown","metadata":{"id":"nx9WK2zftDvu"},"source":["#### Subclass Trainer to Handle Class Imbalance"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":898,"status":"ok","timestamp":1680544462740,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"LIfoJmj_tDvu"},"outputs":[],"source":["class CustomTrainer(Seq2SeqTrainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.get(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs.get(\"logits\")\n","        loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0]))\n","        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n","        return (loss, outputs) if return_outputs else loss"]},{"cell_type":"markdown","metadata":{"id":"HPb8qGl8tDvu"},"source":["#### Instantiate Trainer"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14331,"status":"ok","timestamp":1680544477068,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"3xdweqe9tDvu","outputId":"8dde3681-5cda-4a65-d450-4e7c7f179fcd"},"outputs":[{"name":"stderr","output_type":"stream","text":["Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2 into local empty directory.\n"]}],"source":["trainer = CustomTrainer(model=model, \n","                  args=training_args, \n","                  compute_metrics=compute_metrics,\n","                  train_dataset=encoded_data['train'], \n","                  eval_dataset=encoded_data['eval'], \n","                  data_collator=data_collator,\n","                  tokenizer=tokenizer)"]},{"cell_type":"markdown","metadata":{"id":"bgD5mpbetDvv"},"source":["#### Train Model"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":418},"executionInfo":{"elapsed":5559,"status":"error","timestamp":1680544482623,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"mBh72OMStDvv","outputId":"25173105-acd0-4bc7-b2c2-53a2199aa286"},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/briandunn/Documents/nlpnn/nlp_ml/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0710cdd6ad774efbbbff7557cd82ea65","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'loss': 1.0267, 'learning_rate': 1.999089253187614e-05, 'epoch': 0.01}\n","{'loss': 0.3896, 'learning_rate': 1.8333333333333333e-05, 'epoch': 1.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"20dda014ff96422f88073c031528d117","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.3073105812072754, 'eval_accuracy': 0.8021902806297057, 'eval_Weighted F1': 0.7742896925057061, 'eval_Micro F1': 0.8021902806297057, 'eval_Macro F1': 0.6959786475095884, 'eval_Weighted Recall': 0.8021902806297057, 'eval_Micro Recall': 0.8021902806297057, 'eval_Macro Recall': 0.6914034606001951, 'eval_Weighted Precision': 0.805251462006953, 'eval_Micro Precision': 0.8021902806297057, 'eval_Macro Precision': 0.7879771030989001, 'eval_runtime': 395.2506, 'eval_samples_per_second': 3.696, 'eval_steps_per_second': 0.058, 'epoch': 1.0}\n","{'loss': 0.2488, 'learning_rate': 1.6666666666666667e-05, 'epoch': 2.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ddf50323216847cfb2120c6adab0f3bc","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.2936522364616394, 'eval_accuracy': 0.8473648186173853, 'eval_Weighted F1': 0.8408864153995298, 'eval_Micro F1': 0.8473648186173852, 'eval_Macro F1': 0.7879930148732887, 'eval_Weighted Recall': 0.8473648186173853, 'eval_Micro Recall': 0.8473648186173853, 'eval_Macro Recall': 0.7747410057084069, 'eval_Weighted Precision': 0.8429010198042194, 'eval_Micro Precision': 0.8473648186173853, 'eval_Macro Precision': 0.8131702574232724, 'eval_runtime': 394.0707, 'eval_samples_per_second': 3.707, 'eval_steps_per_second': 0.058, 'epoch': 2.0}\n","{'loss': 0.1766, 'learning_rate': 1.5000000000000002e-05, 'epoch': 3.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dc84ade617ee42df9c0a0a888605cbe3","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.311532199382782, 'eval_accuracy': 0.839835728952772, 'eval_Weighted F1': 0.829828127696968, 'eval_Micro F1': 0.839835728952772, 'eval_Macro F1': 0.7749760317692006, 'eval_Weighted Recall': 0.839835728952772, 'eval_Micro Recall': 0.839835728952772, 'eval_Macro Recall': 0.7613177393449716, 'eval_Weighted Precision': 0.8354959286227767, 'eval_Micro Precision': 0.839835728952772, 'eval_Macro Precision': 0.8111056030942244, 'eval_runtime': 394.109, 'eval_samples_per_second': 3.707, 'eval_steps_per_second': 0.058, 'epoch': 3.0}\n","{'loss': 0.1253, 'learning_rate': 1.3333333333333333e-05, 'epoch': 4.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c337c6642f894c71b79e728fdd4e8666","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.3354000747203827, 'eval_accuracy': 0.8487337440109514, 'eval_Weighted F1': 0.8405762458211518, 'eval_Micro F1': 0.8487337440109514, 'eval_Macro F1': 0.7843308635147238, 'eval_Weighted Recall': 0.8487337440109514, 'eval_Micro Recall': 0.8487337440109514, 'eval_Macro Recall': 0.7695029376563403, 'eval_Weighted Precision': 0.844821665066751, 'eval_Micro Precision': 0.8487337440109514, 'eval_Macro Precision': 0.8167286452515102, 'eval_runtime': 397.8128, 'eval_samples_per_second': 3.673, 'eval_steps_per_second': 0.058, 'epoch': 4.0}\n","{'loss': 0.09, 'learning_rate': 1.1666666666666668e-05, 'epoch': 5.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ddbd229658a940ce949bc9282017eb38","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.4137425720691681, 'eval_accuracy': 0.8473648186173853, 'eval_Weighted F1': 0.8447078466301169, 'eval_Micro F1': 0.8473648186173852, 'eval_Macro F1': 0.7929800369161076, 'eval_Weighted Recall': 0.8473648186173853, 'eval_Micro Recall': 0.8473648186173853, 'eval_Macro Recall': 0.7931567596659512, 'eval_Weighted Precision': 0.8454690797162288, 'eval_Micro Precision': 0.8473648186173853, 'eval_Macro Precision': 0.7985268420624786, 'eval_runtime': 394.2951, 'eval_samples_per_second': 3.705, 'eval_steps_per_second': 0.058, 'epoch': 5.0}\n","{'loss': 0.0677, 'learning_rate': 1e-05, 'epoch': 6.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7c976dbd535e435d892f21f32d4c9af3","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.487180233001709, 'eval_accuracy': 0.8494182067077344, 'eval_Weighted F1': 0.8491000789695532, 'eval_Micro F1': 0.8494182067077344, 'eval_Macro F1': 0.7976888354338474, 'eval_Weighted Recall': 0.8494182067077344, 'eval_Micro Recall': 0.8494182067077344, 'eval_Macro Recall': 0.8067597009635716, 'eval_Weighted Precision': 0.8510539183527065, 'eval_Micro Precision': 0.8494182067077344, 'eval_Macro Precision': 0.7929884204394009, 'eval_runtime': 395.1041, 'eval_samples_per_second': 3.698, 'eval_steps_per_second': 0.058, 'epoch': 6.0}\n","{'loss': 0.0501, 'learning_rate': 8.333333333333334e-06, 'epoch': 7.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d56c4a9f0d7944e8826e3999597ea463","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.4958624839782715, 'eval_accuracy': 0.8576317590691307, 'eval_Weighted F1': 0.8555987686818547, 'eval_Micro F1': 0.8576317590691307, 'eval_Macro F1': 0.8065940907541425, 'eval_Weighted Recall': 0.8576317590691307, 'eval_Micro Recall': 0.8576317590691307, 'eval_Macro Recall': 0.8063295129548841, 'eval_Weighted Precision': 0.8557633604737992, 'eval_Micro Precision': 0.8576317590691307, 'eval_Macro Precision': 0.810529420046731, 'eval_runtime': 396.3534, 'eval_samples_per_second': 3.686, 'eval_steps_per_second': 0.058, 'epoch': 7.0}\n","{'loss': 0.0415, 'learning_rate': 6.666666666666667e-06, 'epoch': 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12.0}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"01a0f09d128c4ebe8d60dfb735fedc24","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.5809382796287537, 'eval_accuracy': 0.8521560574948666, 'eval_Weighted F1': 0.8507220380280501, 'eval_Micro F1': 0.8521560574948664, 'eval_Macro F1': 0.8007248281853435, 'eval_Weighted Recall': 0.8521560574948666, 'eval_Micro Recall': 0.8521560574948666, 'eval_Macro Recall': 0.8006098402878489, 'eval_Weighted Precision': 0.8502744791439133, 'eval_Micro Precision': 0.8521560574948666, 'eval_Macro Precision': 0.8024916159475982, 'eval_runtime': 394.1004, 'eval_samples_per_second': 3.707, 'eval_steps_per_second': 0.058, 'epoch': 12.0}\n","{'train_runtime': 123346.2483, 'train_samples_per_second': 1.137, 'train_steps_per_second': 0.018, 'train_loss': 0.10751267507645168, 'epoch': 12.0}\n"]}],"source":["train_results = trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"0xlmbZHZtDvv"},"source":["#### Push Model to HuggingFace Hub"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":10,"status":"aborted","timestamp":1680544482625,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"dTMMPxmQtDvv"},"outputs":[{"name":"stderr","output_type":"stream","text":["Several commits (2) will be pushed upstream.\n","The progress bars may be unreliable.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b24d43a0981e417cb9ba835d79a2020c","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"896acd33e1f34c27bcba5730a2d97970","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["To https://huggingface.co/DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2\n","   732352a..9446f55  main -> main\n","\n","To https://huggingface.co/DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2\n","   9446f55..22febb7  main -> main\n","\n"]},{"data":{"text/plain":["'https://huggingface.co/DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2/commit/9446f558058bdf76f024c202e0fa4a247289b3d2'"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["trainer.push_to_hub(commit_message=\"All Dunn!!!\")"]},{"cell_type":"markdown","metadata":{"id":"xvh38l83tDvv"},"source":["#### Save Model & Metrics on Training Dataset"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1680544482625,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"fk0SSuO6tDvv"},"outputs":[{"name":"stdout","output_type":"stream","text":["***** train metrics *****\n","  epoch                    =               12.0\n","  train_loss               =             0.1075\n","  train_runtime            = 1 day, 10:15:46.24\n","  train_samples_per_second =              1.137\n","  train_steps_per_second   =              0.018\n"]}],"source":["trainer.save_model()\n","trainer.log_metrics(\"train\", train_results.metrics)\n","trainer.save_metrics(\"train\", train_results.metrics)\n","trainer.save_state()"]},{"cell_type":"markdown","metadata":{"id":"G62fsfcgtDvv"},"source":["#### Evaluate, Log, & Save (Evaluation) Metrics"]},{"cell_type":"code","execution_count":25,"metadata":{"executionInfo":{"elapsed":10,"status":"aborted","timestamp":1680544482626,"user":{"displayName":"Brian Dunn","userId":"18197000719962888348"},"user_tz":300},"id":"Nz3LQBIqtDvv"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0a2a3bd256ec49149437bc6200c69196","version_major":2,"version_minor":0},"text/plain":["A Jupyter Widget"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["***** eval metrics *****\n","  epoch                   =       12.0\n","  eval_Macro F1           =      0.788\n","  eval_Macro Precision    =     0.8132\n","  eval_Macro Recall       =     0.7747\n","  eval_Micro F1           =     0.8474\n","  eval_Micro Precision    =     0.8474\n","  eval_Micro Recall       =     0.8474\n","  eval_Weighted F1        =     0.8409\n","  eval_Weighted Precision =     0.8429\n","  eval_Weighted Recall    =     0.8474\n","  eval_accuracy           =     0.8474\n","  eval_loss               =     0.2937\n","  eval_runtime            = 0:06:34.14\n","  eval_samples_per_second =      3.707\n","  eval_steps_per_second   =      0.058\n"]}],"source":["metrics = trainer.evaluate(encoded_data['eval'])\n","trainer.log_metrics(\"eval\", metrics)\n","trainer.save_metrics(\"eval\", metrics)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"w9Huo4kMtDvv"},"source":["### Notes & Other Takeaways From This Project\n","****\n","- It seems as though the only way to improve this model from here is to use the large checkpoint (BERT-Large).\n","\n","****"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"jMi0SHjGtDvv"},"source":["### Citations\n","\n","- Model Checkpoint\n","    > @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},bibsource = {dblp computer science bibliography, https://dblp.org}}\n"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"nlp_ml","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"vscode":{"interpreter":{"hash":"0b7a754c5aaaa44a21e2f2148f50b931a5abdffe7ef71537f5954179554f39df"}},"widgets":{"application/vnd.jupyter.widget-state+json":{"037e575f0b804833ae5906211e743420":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"FloatProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_2ec8141ca00c47b9a8d347f3ef65c383","max":231508,"min":0,"orientation":"horizontal","style":"IPY_MODEL_8308ff17df3843bbb8e0954f3438011c","value":231508}},"05201843fcd14ab5872d6b97832d4c5e":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"06630af0f3054dc7902f0fc3fb60af56":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_7bd3501a269b48c88c3ad8ce25b55b81","IPY_MODEL_e60d2b3ef0fb4014b48120364c433b30","IPY_MODEL_217849950a6645adbb7dae4e535b15b6"],"layout":"IPY_MODEL_cf29015b12d543a68e348a09b5f0468d"}},"06e394600caa4ef9bb22c22e22ab4015":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"0bb73b9e69744cf3ab482ae6e4a5051a":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"0eb23176a71e46ee88b6aba1c1d94c5b":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justi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