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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Load the required libraries\n",
"import torch\n",
"from torch.utils.data import Dataset\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report\n",
"from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments\n",
"from transformers import TrainerCallback\n",
"import os\n",
"from transformers import TrainingArguments, Trainer\n",
"#Create directory to save model\n",
"os.makedirs(\"./best_model\", exist_ok=True)\n",
"\n",
"#Create a callback class to save the best model\n",
"class SaveBestModelCallback(TrainerCallback):\n",
" #Initialize the class variables and values\n",
" def __init__(self):\n",
" self.best_f1_score = 0\n",
" #Get the evaluation metrics\n",
" def on_evaluate(self, args, state, control, metrics, **kwargs):\n",
" metrics = trainer.evaluate()\n",
" f1_score = metrics[\"eval_f1\"]\n",
" #Save the model if the current f1 score is higher that the best f1 score so far\n",
" if f1_score > self.best_f1_score:\n",
" self.best_f1_score = f1_score\n",
" model.save_pretrained(\"./best_model\")\n",
" tokenizer.save_pretrained(\"./best_model\")\n",
" #Print the f1 score\n",
" print(f\"New best model saved with F1 score: {f1_score}\")\n",
"\n",
"# Load and preprocess the data\n",
"train_data = pd.read_csv(\"train_links.csv\", encoding='utf-8', encoding_errors='ignore')\n",
"test_data = pd.read_csv(\"test_links.csv\", encoding='utf-8', encoding_errors='ignore')\n",
"\n",
"test_data=test_data[:16171]\n",
"\n",
"train_data=train_data[['email', 'label']]\n",
"test_data=test_data[['email', 'label']]\n",
"\n",
"\n",
"#print(len(train_data))\n",
"#print(train_data[train_data['label'].isnull()])\n",
"\n",
"\n",
"train_data['label'] = train_data['label'].astype(int)\n",
"test_data['label'] = test_data['label'].astype(int)\n",
"\n",
"#Convert all column data to strings\n",
"train_email_list=train_data[\"email\"].tolist()\n",
"for i in range(len(train_email_list)):\n",
" if type(train_email_list[i]) != type('a'):\n",
" temp=str(train_email_list[i])\n",
" train_email_list[i]=temp\n",
"\n",
"#Get the label lists\n",
"train_label_list=train_data[\"label\"].tolist()\n",
"\n",
"#print(len(train_email_list))\n",
"#print(len(train_label_list))\n",
"\n",
"\n",
"for i in range(len(train_label_list)):\n",
" if type(train_label_list[i]) != type(1):\n",
" temp=int(train_label_list[i])\n",
" train_label_list[i]=temp\n",
"\n",
"#Convert null values in labels to 0\n",
"count=0\n",
"#print(count)\n",
"for i in (train_data[\"label\"].tolist()):\n",
" if type(i) != type(1):\n",
" count+=1\n",
"\n",
"#print(count)\n",
"\n",
"#print(len(train_data))\n",
"#print(train_data[train_data['label'].isnull()])\n",
"\n",
"\n",
"#Get test email and label lists\n",
"test_email_list=test_data[\"email\"].tolist()\n",
"for i in range(len(test_email_list)):\n",
" if type(test_email_list[i]) != type('a'):\n",
" temp=str(test_email_list[i])\n",
" test_email_list[i]=temp\n",
"\n",
"\n",
"test_label_list=test_data[\"label\"].tolist()\n",
"\n",
"#print(len(train_email_list))\n",
"#print(len(train_label_list))\n",
"\n",
"\n",
"for i in range(len(test_label_list)):\n",
" if type(test_label_list[i]) != type(1):\n",
" temp=int(test_label_list[i])\n",
" test_label_list[i]=temp\n",
"\n",
"count=0\n",
"#print(count)\n",
"for i in (test_data[\"label\"].tolist()):\n",
" if type(i) != type(1):\n",
" count+=1\n",
"\n",
"#print(count)\n",
"\n",
"train_data=train_data[['email', 'label']]\n",
"test_data=test_data[['email', 'label']]\n",
"\n",
"train_data['label'] = train_data['label'].astype(int)\n",
"test_data['label'] = test_data['label'].astype(int)\n",
"\n",
"#Load the RoBERTa tokenizer\n",
"tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
"\n",
"#Preprocess the data\n",
"def preprocess(df):\n",
" inputs = tokenizer(df[\"email\"].tolist(), return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
" labels = torch.tensor(df[\"label\"].tolist())\n",
" return inputs, labels\n",
"\n",
"train_inputs, train_labels = preprocess(train_data)\n",
"test_inputs, test_labels = preprocess(test_data)\n",
"\n",
"# Custom dataset class\n",
"class CustomDataset(Dataset):\n",
" def __init__(self, inputs, labels):\n",
" self.inputs = inputs\n",
" self.labels = labels\n",
"\n",
" def __len__(self):\n",
" return len(self.labels)\n",
"\n",
" def __getitem__(self, idx):\n",
" item = {key: val[idx] for key, val in self.inputs.items()}\n",
" item[\"labels\"] = self.labels[idx]\n",
" return item\n",
"\n",
"# Prepare the RoBERTa model for training\n",
"model = RobertaForSequenceClassification.from_pretrained(\"roberta-base\", num_labels=2)\n",
"\n",
"# Define the Trainer and TrainingArguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" num_train_epochs=1,\n",
" per_device_train_batch_size=8,\n",
" per_device_eval_batch_size=16,\n",
" logging_dir=\"./logs\",\n",
" logging_steps=100,\n",
" save_steps=1000,\n",
" evaluation_strategy=\"epoch\",\n",
" learning_rate=2e-5,\n",
" weight_decay=0.01,\n",
")\n",
"\n",
"#Define the compute metrics function\n",
"def compute_metrics(pred):\n",
" labels = pred.label_ids\n",
" preds = pred.predictions.argmax(-1)\n",
" metrics = classification_report(labels, preds, output_dict=True)[\"weighted avg\"]\n",
" return {\"f1\": metrics[\"f1-score\"]}\n",
"\n",
"\n",
"#Initialize the trainer\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=CustomDataset(train_inputs, train_labels),\n",
" eval_dataset=CustomDataset(test_inputs, test_labels),\n",
" compute_metrics=compute_metrics,\n",
")\n",
"\n",
"#trainer.add_callback(SaveBestModelCallback())\n",
"trainer.train()\n",
"\n",
"# Evaluate the model\n",
"eval_results = trainer.evaluate()\n",
"\n",
"#Printing the results\n",
"print(\"Evaluation results:\", eval_results)\n",
"\n",
"\n",
"#Save the best model\n",
"model.save_pretrained('./best_model')\n",
"model.save_pretrained('./best_model.h5')\n",
"tokenizer.save_pretrained(\"./best_model\")\n",
"\n",
"\"\"\"\n",
"best_model = RobertaForSequenceClassification.from_pretrained(\"./best_model\")\n",
"best_tokenizer = RobertaTokenizer.from_pretrained(\"./best_model\")\n",
"For using the saved model in a Google Chrome extension, you would need to use a server-side solution or a cloud-based API to connect your extension to the trained model.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = RobertaForSequenceClassification.from_pretrained(\"./best_model\")\n",
"tokenizer = RobertaTokenizer.from_pretrained(\"./best_model\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"inputs = tokenizer(\"www.tiem.utk.edu/~gross/bioed/bealsmodules/spider.html\", return_tensors=\"pt\")\n",
"outputs = model(**inputs)\n",
"predictions = torch.argmax(outputs.logits, dim=-1)\n",
"\n",
"print(predictions)"
]
}
],
"metadata": {
"language_info": {
"name": "python"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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