Binary Classification/Malignant Comments/Malignant Comments - BERT-Base.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"YI17H3Melofy"},"source":["## Malignant Comments\n","\n","Dataset Source: https://www.kaggle.com/datasets/surekharamireddy/malignant-comment-classification"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"DswtRoySlof0"},"source":["#### Import Necessary Libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15618,"status":"ok","timestamp":1681825432664,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"Rae-2jwElof0","outputId":"f56ba99e-3cc8-4553-eb79-84d881718550"},"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","\n","import transformers\n","from transformers import AutoModelForSequenceClassification, AutoTokenizer\n","from transformers import TrainingArguments, Trainer, set_seed, DataCollatorWithPadding\n","\n","import evaluate\n","\n","!git lfs install"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"C1fukwgzlof1"},"source":["#### Display Library Versions"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1681825452264,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"-xu22V8llof1","outputId":"691d198f-e3d7-4189-c7f8-7abd73a7e125"},"outputs":[{"name":"stdout","output_type":"stream","text":["        Python: 3.9.7 \n","         NumPy: 1.23.3\n","        Pandas: 1.4.4\n","      Datasets: 2.8.0\n","  Transformers: 4.28.1\n","         Torch: 2.0.0\n","      Evaluate: 0.2.2\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":"Vob1xXeDlof2"},"source":["#### Load & Start Preprocessing Dataset"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":2995,"status":"ok","timestamp":1681825533014,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"znplYD_rlof2","outputId":"58de724a-0330-4164-a083-6ba7132d6543"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 159571 entries, 0 to 159570\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count   Dtype \n","---  ------  --------------   ----- \n"," 0   text    159571 non-null  object\n"," 1   label   159571 non-null  int64 \n","dtypes: int64(1), object(1)\n","memory usage: 2.4+ MB\n"]}],"source":["parent_dir = \"/Users/leedunn/Desktop/Projects_to_Train/Malignant Text/data\"\n","\n","data_file_location = os.path.join(parent_dir, \"train.csv\")\n","\n","data = pd.read_csv(data_file_location)\n","\n","data = data.rename(columns={\"comment_text\": \"text\"})\n","\n","data = data.drop(columns=['id', \"malignant\", \"highly_malignant\", \"rude\", \"loathe\"])\n","\n","data['text'] = data['text'].apply(lambda x: x.strip())\n","data['text'] = data['text'].replace(r'\\n', \" \", regex=True)\n","\n","data['label'] = data['abuse'] + data['threat']\n","\n","data['label'] = data['label'].apply(lambda x: 1 if x > 0 else 0)\n","\n","data = data.drop(columns=['threat', 'abuse'])\n","\n","data.info()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"zksx8xsklof2"},"source":["#### Define Dictionaries to Convert Between String & Integer Class Values"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"hd_T8NJllof2"},"outputs":[{"name":"stdout","output_type":"stream","text":["['clean', 'malignant']\n","2\n","{'clean': 0, 'malignant': 1}\n","{0: 'clean', 1: 'malignant'}\n"]}],"source":["labels = ['clean', 'malignant']\n","\n","id2label = {idx:label for idx, label in enumerate(labels)}\n","label2id = {label:idx for idx, label in enumerate(labels)}\n","\n","NUM_OF_LABELS = len(labels)\n","\n","print(labels)\n","print(NUM_OF_LABELS)\n","print(label2id)\n","print(id2label)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"xUshgDR8lof3"},"source":["#### Visual: Histogram of Text Lengths"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"8gKf23Wqlof3","outputId":"51f36894-6440-4bfd-ae17-a30aac9cb806"},"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 1411 words.\n"]}],"source":["data['text_length'] = data['text'].str.split().str.len()\n","\n","n_bin=170\n","data.hist('text_length', grid=False, figsize=(18,8), range=(0,n_bin + 1), bins=(n_bin + 1))\n","plt.title('Histogram: Length of Speeches')\n","plt.xlabel('# of Words in Speeches')\n","plt.ylabel('# of Samples')\n","plt.show()\n","\n","print(f\"The maximum length of Speeches is {max(data['text_length'])} words.\")"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"Z5LLbXellof3"},"source":["#### Visual: Tweet Length by Class"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"kZduaMjslof3","outputId":"3181f2e3-86f5-4979-e7e7-e9afb51b1433"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data.boxplot(\"text_length\", by=\"label\", grid=False, showfliers=False, color=\"Blue\", figsize=(10,8))\n","\n","plt.suptitle('')\n","plt.title('Tweet Length by Type')\n","plt.xlabel('')\n","plt.ylabel('Words per Tweet')\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"kqgMPq7elof3"},"source":["#### Final Data Preprocessing"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":236},"executionInfo":{"elapsed":9,"status":"error","timestamp":1681825560450,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"rG5s5ceDlof3","outputId":"1bd0b103-31b8-425d-f0aa-1647f7e32ceb"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 146214 entries, 0 to 159570\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count   Dtype \n","---  ------  --------------   ----- \n"," 0   text    146214 non-null  object\n"," 1   label   146214 non-null  int64 \n","dtypes: int64(1), object(1)\n","memory usage: 3.3+ MB\n"]}],"source":["data = data[data['text_length'] < 171]\n","\n","data = data.drop(columns=[\"text_length\"])\n","\n","data.info()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"XWYCTizQlof3"},"source":["#### Number of Samples in Each Class"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":6,"status":"aborted","timestamp":1681825560451,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"ih7P8VEmlof4"},"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['label'].value_counts()\n","\n","class_counts.plot.bar()\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.legend('', frameon=False)\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"r8AvzHcLlof4"},"source":["#### Convert DataFrame to Dataset"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":7,"status":"aborted","timestamp":1681825560452,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"TKpuexuklof4"},"outputs":[],"source":["dataset = Dataset.from_pandas(data)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"m7BbSvYhlof4"},"source":["#### Split Dataset into Training Evaluation Datasets"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1337,"status":"aborted","timestamp":1681825560452,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"WLG2lp4olof4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training Dataset Shape: (97963, 3)\n","Evaluation Dataset Shape: (48251, 3)\n"]}],"source":["train_eval = dataset.train_test_split(train_size=0.67)\n","\n","ds = DatasetDict({\n","    'train' : train_eval['train'],\n","    'eval' : train_eval['test'],\n","})\n","\n","print(\"Training Dataset Shape:\", ds['train'].shape)\n","print(\"Evaluation Dataset Shape:\", ds['eval'].shape)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"9nJka3nflof4"},"source":["#### Basic Values/Constants"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":1334,"status":"aborted","timestamp":1681825560453,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"4nBu-l_Nlof4"},"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 220 tokens.\n","The model checkpoint used for this project is: bert-base-uncased\n"]}],"source":["set_seed = 42\n","NUM_OF_EPOCHS = 2\n","\n","BATCH_SIZE = 64\n","LEARNING_RATE = 2e-5\n","\n","MAX_LENGTH = 220\n","MODEL_CKPT = \"bert-base-uncased\"\n","MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Abusive_Or_Threatening_Speech\"\n","\n","METRIC_NAME = \"f1\"\n","DEVICE = torch.device(\"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}\")"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"7liPWrBolof4"},"source":["#### Tokenize Entire Dataset"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":4,"status":"aborted","timestamp":1681825561343,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"VSAjrx9Tlof4"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"86627cb75efb442b860e264fa1987882","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b6ab3335f042447695d8e6533c14468f","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Training data shape: (97963, 6)\n","Validation data shape: (48251, 6)\n","{'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), '__index_level_0__': Value(dtype='int64', id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}\n","Dataset({\n","    features: ['text', 'label', '__index_level_0__', 'input_ids', 'token_type_ids', 'attention_mask'],\n","    num_rows: 97963\n","})\n"]}],"source":["# Define Tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)\n","\n","# Tokenize Method\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","# Tokenize 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('Validation data shape:', encoded_data['eval'].shape)\n","\n","print(encoded_data['train'].features)\n","print(encoded_data['train'])"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"jZ0t2_Ohlof4"},"source":["#### Instantiate Model"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":4,"status":"aborted","timestamp":1681825561344,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"vauFY6fOlof4"},"outputs":[{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["model = AutoModelForSequenceClassification.from_pretrained(\n","    MODEL_CKPT,\n","    num_labels=NUM_OF_LABELS,\n","    id2label=id2label,\n","    label2id=label2id\n","    ).to(DEVICE)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"AkIZ7Q8Llof5"},"source":["#### Define Function to Compute Metrics"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":5,"status":"aborted","timestamp":1681825561345,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"ZS-cTQ9plof5"},"outputs":[],"source":["def compute_metrics(p):\n","    accuracy_metric = evaluate.load(\"accuracy\")\n","    accuracy = accuracy_metric.compute(predictions=np.argmax(p.predictions, \n","                                                             axis=1), \n","                                       references=p.label_ids)['accuracy']\n","    \n","    ### ------------------- F1 scores -------------------\n","    \n","    f1_score_metric = evaluate.load(\"f1\")\n","    f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions, \n","                                                             axis=1), \n","                                       references=p.label_ids)[\"f1\"]\n","    \n","    ### ------------------- recall -------------------\n","    \n","    recall_metric = evaluate.load(\"recall\")\n","    recall = recall_metric.compute(predictions=np.argmax(p.predictions, \n","                                                         axis=1), \n","                                   references=p.label_ids)[\"recall\"]\n","    \n","    ### ------------------- precision -------------------\n","    \n","    precision_metric = evaluate.load(\"precision\")\n","    precision = precision_metric.compute(predictions=np.argmax(p.predictions, \n","                                                               axis=1), \n","                                         references=p.label_ids)[\"precision\"]\n","    \n","    return {\"accuracy\" : accuracy, \n","            \"F1\" : f1_score,\n","            \"Recall\" : recall,\n","            \"Precision\" : precision,\n","            }"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"1EYt1FZslof5"},"source":["#### Define TrainingArguments"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":5,"status":"aborted","timestamp":1681825561345,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"WP3ndiTRlof5"},"outputs":[],"source":["training_args = TrainingArguments(\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='steps', \n","    logging_steps=50,\n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    logging_first_step=True,\n","    load_best_model_at_end=True,\n","    disable_tqdm=False,\n","    hub_private_repo=True,\n","    push_to_hub=True\n",")"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"3sPPrpwIlof5"},"source":["#### Define Data Collator"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":5,"status":"aborted","timestamp":1681825561345,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"kJ05TD8rlof5"},"outputs":[],"source":["data_collator = DataCollatorWithPadding(tokenizer, padding=True)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"uzEpgs7Slof5"},"source":["#### Subclass Trainer to Handle Class Imbalance"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"P93xfT6Vlof5"},"outputs":[],"source":["class CustomTrainer(Trainer):\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]))\n","        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n","        return (loss, outputs) if return_outputs else loss"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"0JlxDWivlof5"},"source":["#### Instantiate Trainer"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"TUOElPI1lof5","outputId":"2d90b472-5ff9-4bc8-c550-5f778b329504"},"outputs":[{"name":"stderr","output_type":"stream","text":["Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech 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)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"umV50LBJlof5"},"source":["#### Train Model"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":166},"executionInfo":{"elapsed":6,"status":"error","timestamp":1681825562575,"user":{"displayName":"hill billy","userId":"11912902376012624422"},"user_tz":300},"id":"8Nd7rywHlof5","outputId":"148377c5-7c4e-458f-e492-46da70a5c165"},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/leedunn/Documents/nlpnn/nlp/lib/python3.9/site-packages/transformers/optimization.py:391: 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":"1d42fea2171849e0b7d8532343fe5e09","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/3062 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'loss': 0.7907, 'learning_rate': 1.999346832135859e-05, 'epoch': 0.0}\n","{'loss': 0.2498, 'learning_rate': 1.967341606792946e-05, 'epoch': 0.03}\n","{'loss': 0.1179, 'learning_rate': 1.9346832135858918e-05, 'epoch': 0.07}\n","{'loss': 0.1278, 'learning_rate': 1.9020248203788378e-05, 'epoch': 0.1}\n","{'loss': 0.1106, 'learning_rate': 1.8693664271717834e-05, 'epoch': 0.13}\n","{'loss': 0.1209, 'learning_rate': 1.836708033964729e-05, 'epoch': 0.16}\n","{'loss': 0.109, 'learning_rate': 1.8040496407576747e-05, 'epoch': 0.2}\n","{'loss': 0.1062, 'learning_rate': 1.7713912475506207e-05, 'epoch': 0.23}\n","{'loss': 0.096, 'learning_rate': 1.7387328543435663e-05, 'epoch': 0.26}\n","{'loss': 0.1032, 'learning_rate': 1.7060744611365123e-05, 'epoch': 0.29}\n","{'loss': 0.0895, 'learning_rate': 1.673416067929458e-05, 'epoch': 0.33}\n","{'loss': 0.087, 'learning_rate': 1.640757674722404e-05, 'epoch': 0.36}\n","{'loss': 0.1026, 'learning_rate': 1.6080992815153496e-05, 'epoch': 0.39}\n","{'loss': 0.0871, 'learning_rate': 1.5754408883082955e-05, 'epoch': 0.42}\n","{'loss': 0.0948, 'learning_rate': 1.5427824951012412e-05, 'epoch': 0.46}\n","{'loss': 0.1008, 'learning_rate': 1.510124101894187e-05, 'epoch': 0.49}\n","{'loss': 0.0696, 'learning_rate': 1.4774657086871326e-05, 'epoch': 0.52}\n","{'loss': 0.1023, 'learning_rate': 1.4448073154800786e-05, 'epoch': 0.56}\n","{'loss': 0.1002, 'learning_rate': 1.4121489222730243e-05, 'epoch': 0.59}\n","{'loss': 0.0789, 'learning_rate': 1.37949052906597e-05, 'epoch': 0.62}\n","{'loss': 0.0844, 'learning_rate': 1.3468321358589159e-05, 'epoch': 0.65}\n","{'loss': 0.099, 'learning_rate': 1.3141737426518617e-05, 'epoch': 0.69}\n","{'loss': 0.0859, 'learning_rate': 1.2815153494448073e-05, 'epoch': 0.72}\n","{'loss': 0.0806, 'learning_rate': 1.2488569562377533e-05, 'epoch': 0.75}\n","{'loss': 0.0762, 'learning_rate': 1.216198563030699e-05, 'epoch': 0.78}\n","{'loss': 0.1023, 'learning_rate': 1.1835401698236448e-05, 'epoch': 0.82}\n","{'loss': 0.0875, 'learning_rate': 1.1508817766165906e-05, 'epoch': 0.85}\n","{'loss': 0.0867, 'learning_rate': 1.1182233834095364e-05, 'epoch': 0.88}\n","{'loss': 0.0875, 'learning_rate': 1.085564990202482e-05, 'epoch': 0.91}\n","{'loss': 0.1097, 'learning_rate': 1.052906596995428e-05, 'epoch': 0.95}\n","{'loss': 0.1008, 'learning_rate': 1.0202482037883737e-05, 'epoch': 0.98}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8633845510464e9c8d0f8599f8f52584","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/754 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.08737248182296753, 'eval_accuracy': 0.9720213052579221, 'eval_F1': 0.7590146376294181, 'eval_Recall': 0.8406484776591538, 'eval_Precision': 0.6918320859095347, 'eval_runtime': 21370.7121, 'eval_samples_per_second': 2.258, 'eval_steps_per_second': 0.035, 'epoch': 1.0}\n","{'loss': 0.0676, 'learning_rate': 9.875898105813195e-06, 'epoch': 1.01}\n","{'loss': 0.0564, 'learning_rate': 9.549314173742653e-06, 'epoch': 1.05}\n","{'loss': 0.0683, 'learning_rate': 9.222730241672111e-06, 'epoch': 1.08}\n","{'loss': 0.0593, 'learning_rate': 8.896146309601569e-06, 'epoch': 1.11}\n","{'loss': 0.0628, 'learning_rate': 8.569562377531027e-06, 'epoch': 1.14}\n","{'loss': 0.0528, 'learning_rate': 8.242978445460484e-06, 'epoch': 1.18}\n","{'loss': 0.0589, 'learning_rate': 7.916394513389942e-06, 'epoch': 1.21}\n","{'loss': 0.065, 'learning_rate': 7.5898105813194e-06, 'epoch': 1.24}\n","{'loss': 0.0582, 'learning_rate': 7.263226649248858e-06, 'epoch': 1.27}\n","{'loss': 0.0542, 'learning_rate': 6.936642717178315e-06, 'epoch': 1.31}\n","{'loss': 0.0625, 'learning_rate': 6.6100587851077734e-06, 'epoch': 1.34}\n","{'loss': 0.0701, 'learning_rate': 6.2834748530372315e-06, 'epoch': 1.37}\n","{'loss': 0.0698, 'learning_rate': 5.956890920966689e-06, 'epoch': 1.4}\n","{'loss': 0.0526, 'learning_rate': 5.630306988896147e-06, 'epoch': 1.44}\n","{'loss': 0.0724, 'learning_rate': 5.303723056825605e-06, 'epoch': 1.47}\n","{'loss': 0.0616, 'learning_rate': 4.977139124755062e-06, 'epoch': 1.5}\n","{'loss': 0.0714, 'learning_rate': 4.6505551926845204e-06, 'epoch': 1.53}\n","{'loss': 0.0648, 'learning_rate': 4.3239712606139786e-06, 'epoch': 1.57}\n","{'loss': 0.0629, 'learning_rate': 3.997387328543436e-06, 'epoch': 1.6}\n","{'loss': 0.0666, 'learning_rate': 3.670803396472894e-06, 'epoch': 1.63}\n","{'loss': 0.0666, 'learning_rate': 3.3442194644023516e-06, 'epoch': 1.67}\n","{'loss': 0.0562, 'learning_rate': 3.0176355323318098e-06, 'epoch': 1.7}\n","{'loss': 0.056, 'learning_rate': 2.6910516002612675e-06, 'epoch': 1.73}\n","{'loss': 0.0625, 'learning_rate': 2.364467668190725e-06, 'epoch': 1.76}\n","{'loss': 0.064, 'learning_rate': 2.0378837361201833e-06, 'epoch': 1.8}\n","{'loss': 0.0568, 'learning_rate': 1.711299804049641e-06, 'epoch': 1.83}\n","{'loss': 0.0605, 'learning_rate': 1.3847158719790987e-06, 'epoch': 1.86}\n","{'loss': 0.057, 'learning_rate': 1.0581319399085566e-06, 'epoch': 1.89}\n","{'loss': 0.0587, 'learning_rate': 7.315480078380144e-07, 'epoch': 1.93}\n","{'loss': 0.0654, 'learning_rate': 4.0496407576747226e-07, 'epoch': 1.96}\n","{'loss': 0.0673, 'learning_rate': 7.838014369693013e-08, 'epoch': 1.99}\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"45570a7b8f844d5fb0fd7b7dce8bfc38","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/754 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'eval_loss': 0.09812068194150925, 'eval_accuracy': 0.9718969555035128, 'eval_F1': 0.7591474245115454, 'eval_Recall': 0.844998022933966, 'eval_Precision': 0.6891325378910029, 'eval_runtime': 21573.5728, 'eval_samples_per_second': 2.237, 'eval_steps_per_second': 0.035, 'epoch': 2.0}\n","{'train_runtime': 498162.2121, 'train_samples_per_second': 0.393, 'train_steps_per_second': 0.006, 'train_loss': 0.08183616427017301, 'epoch': 2.0}\n"]}],"source":["train_results = trainer.train()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"-KmSywCHlof5"},"source":["#### Save Model & Metrics on Training Dataset"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"referenced_widgets":["2bd1bb076e20406eb5a5cf5bbc492cae","4e622a4c359a46569dc449769c71d646"]},"id":"gJ3iXuR-lof5","outputId":"68a43ad8-999a-4724-9d97-d5490d22f341"},"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":"88533de0608b421c885cb7aa8e73ba01","version_major":2,"version_minor":0},"text/plain":["Upload file pytorch_model.bin:   0%|          | 1.00/418M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"072bfee7c0ae48c8a5d1ab07c5f7743e","version_major":2,"version_minor":0},"text/plain":["Upload file runs/May13_01-09-40_Lees-Air/events.out.tfevents.1683958188.Lees-Air.30886.0:   0%|          | 1.0…"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["To https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech\n","   6f68933..700aec6  main -> main\n","\n","To https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech\n","   700aec6..a0b4177  main -> main\n","\n"]},{"name":"stdout","output_type":"stream","text":["***** train metrics *****\n","  epoch                    =                 2.0\n","  train_loss               =              0.0818\n","  train_runtime            = 5 days, 18:22:42.21\n","  train_samples_per_second =               0.393\n","  train_steps_per_second   =               0.006\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()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"GpNxw--Alof5"},"source":["#### Evaluate Model"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"referenced_widgets":["27c83af28dab49b89cb08e72925344c5"]},"id":"QcHxdny1lof6","outputId":"47bb1af0-1553-466b-86c8-673d8a1e99dd"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"df834a5377ef4795b84f667dcc514ca0","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/754 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["***** eval metrics *****\n","  epoch                   =        2.0\n","  eval_F1                 =      0.759\n","  eval_Precision          =     0.6918\n","  eval_Recall             =     0.8406\n","  eval_accuracy           =      0.972\n","  eval_loss               =     0.0874\n","  eval_runtime            = 6:01:43.47\n","  eval_samples_per_second =      2.223\n","  eval_steps_per_second   =      0.035\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":"fCkk__fqlof6"},"source":["#### Push Model to HuggingFace Hub"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"BpR4tiIFlof6"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"aa1c7416504f4713af283fe4da2a2654","version_major":2,"version_minor":0},"text/plain":["Upload file runs/May13_01-09-40_Lees-Air/events.out.tfevents.1684478724.Lees-Air.30886.2: 100%|##########| 560…"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["To https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech\n","   a0b4177..40924bd  main -> main\n","\n","To https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech\n","   40924bd..a9885d4  main -> main\n","\n"]},{"data":{"text/plain":["'https://huggingface.co/DunnBC22/bert-base-uncased-Abusive_Or_Threatening_Speech/commit/40924bdcddf645cd407a5ed673f96fec4e1cfad0'"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["trainer.push_to_hub(commit_message=\"All Dunn!!!\")"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"RnF_C77Wlof6"},"source":["### Notes & Other Takeaways From This Project\n","****\n","- These text inputs are slightly different than the inputs from other projects that I have trained. The inputs are not spam and the intent is not to measure sentiment, but rather this project seeks to find any reviews that need to be flagged, reviewed and potentially removed. \n","****"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"SAgsejO9lof6"},"source":["### Citations\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":"3.9.7"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"0b7a754c5aaaa44a21e2f2148f50b931a5abdffe7ef71537f5954179554f39df"}}},"nbformat":4,"nbformat_minor":0}