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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5k3Qn8DImEGv",
"outputId": "d9946915-5fcd-43b3-edc2-e119b15c77c8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
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"\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, tokenizers, transformers, peft, datasets\n",
" Attempting uninstall: tokenizers\n",
" Found existing installation: tokenizers 0.19.1\n",
" Uninstalling tokenizers-0.19.1:\n",
" Successfully uninstalled tokenizers-0.19.1\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.44.2\n",
" Uninstalling transformers-4.44.2:\n",
" Successfully uninstalled transformers-4.44.2\n",
"Successfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 peft-0.13.2 tokenizers-0.20.1 transformers-4.45.2 xxhash-3.5.0\n"
]
}
],
"source": [
"!pip install -U transformers datasets peft"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "F0rYC0S3lhUJ"
},
"outputs": [],
"source": [
"import torch\n",
"from torch.utils.data import DataLoader, Dataset\n",
"from transformers import AutoModel, AdamW, get_linear_schedule_with_warmup,DebertaV2Tokenizer\n",
"from sklearn.model_selection import train_test_split\n",
"from datasets import load_dataset\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from transformers import Trainer, TrainingArguments\n",
"from datasets import Dataset as HFDataset\n",
"from peft import PeftConfig, PeftModel,LoraConfig,get_peft_model\n",
"\n",
"# Define constants\n",
"MODEL_NAME = 'google/electra-small-discriminator'\n",
"BATCH_SIZE = 4\n",
"EPOCHS = 3\n",
"LEARNING_RATE = 2e-4\n",
"MAX_LENGTH = 512"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wVfJhfyqnur3",
"outputId": "c4f695e0-0281-43f5-b508-6c58c3971222"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Columns in /content/ScamDataNew.csv: ['Scammer', 'Label']\n",
"Columns in /content/cleaned-data.csv: ['input', 'output']\n",
" text label\n",
"0 unknown: Hello this is HUGIE Finance calling. ... 1.0\n",
"1 unknown: Pepperfry item (Yukashi 3 Door Wardro... 0.0\n",
"2 unknown: Act now to benefit from our unique of... 1.0\n",
"3 unknown: It's Shoppers Stop BirthYAY & we love... 0.0\n",
"4 unknown: Hello I'm calling from MUTHOOT Financ... 1.0\n",
"... ... ...\n",
"4433 unknown: did you check the email i sent yester... 0.0\n",
"4434 unknown: Cant wait to see you this weekend, so... 0.0\n",
"4435 unknown: I think we should leave earlier, traf... 0.0\n",
"4436 unknown: forgot to bring the umbrella, it's ra... 0.0\n",
"4437 unknown: is there anything else you need from the 0.0\n",
"\n",
"[4438 rows x 2 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# List of file paths to the CSV files\n",
"csv_files = [\n",
" '/content/ScamDataNew.csv',\n",
" '/content/cleaned-data.csv',\n",
"]\n",
"\n",
"# Function to load a CSV file and extract two columns\n",
"def load_and_select_columns(file_path, text_col, label_col):\n",
" if (file_path=='/content/Data_including_normal.csv'):\n",
" df = pd.read_csv(file_path, encoding='ISO-8859-1')\n",
" else:\n",
" df = pd.read_csv(file_path)\n",
" print(f\"Columns in {file_path}: {df.columns.tolist()}\")\n",
" selected_df = df[[text_col, label_col]].copy() # Select the two columns\n",
" selected_df.columns = ['text', 'label'] # Standardize column names\n",
" return selected_df\n",
"\n",
"# Load each CSV and extract relevant columns\n",
"# Update 'text_col' and 'label_col' with actual column names from each CSV\n",
"df1 = load_and_select_columns(csv_files[0], 'Scammer', 'Label')\n",
"df1['text']='unknown: '+df1['text']\n",
"df2 = load_and_select_columns(csv_files[1], 'input', 'output')\n",
"# df3 = load_and_select_columns(csv_files[2], 'dialogue', 'labels')\n",
"df4=pd.read_excel(\"/content/Old+Improved data.xlsx\")\n",
"df4 = df4[['content', 'is scam']].copy() # Select the two columns\n",
"df4.columns = ['text', 'label']\n",
"df4['text']='unknown: '+df4['text']\n",
"# Concatenate the selected columns from all files\n",
"combined_df = pd.concat([df1, df2,df4], ignore_index=True)\n",
"\n",
"# Display the combined DataFrame\n",
"print(combined_df)"
]
},
{
"cell_type": "code",
"source": [
"combined_df.dropna(inplace=True)\n",
"combined_df['label'] = combined_df['label'].astype(int)\n",
"\n",
"# # Reset the index of the combined DataFrame\n",
"# combined_df.reset_index(drop=True, inplace=True)\n",
"combined_df['text']=combined_df['text'].str.lower()\n",
"# Display the combined DataFrame\n",
"print(combined_df)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZXcCFIgM08Bp",
"outputId": "898a7392-0b33-40f6-b274-d01989facd41"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" text label\n",
"0 unknown: hello this is hugie finance calling. ... 1\n",
"1 unknown: pepperfry item (yukashi 3 door wardro... 0\n",
"2 unknown: act now to benefit from our unique of... 1\n",
"3 unknown: it's shoppers stop birthyay & we love... 0\n",
"4 unknown: hello i'm calling from muthoot financ... 1\n",
"... ... ...\n",
"4433 unknown: did you check the email i sent yester... 0\n",
"4434 unknown: cant wait to see you this weekend, so... 0\n",
"4435 unknown: i think we should leave earlier, traf... 0\n",
"4436 unknown: forgot to bring the umbrella, it's ra... 0\n",
"4437 unknown: is there anything else you need from the 0\n",
"\n",
"[4437 rows x 2 columns]\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "0M-Psc9XlwCx"
},
"outputs": [],
"source": [
"combined_df.to_csv('cleaned-data-version2-with-user-unknown.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "9HEql8ZemQ8V"
},
"outputs": [],
"source": [
"def load_data_from_csv():\n",
" df = combined_df\n",
" texts = df['text'].tolist() # Replace with your text column name\n",
" label = df['label'].tolist() # Replace with your label column name\n",
" le = LabelEncoder()\n",
" label = le.fit_transform(label)\n",
" return texts, label\n",
"\n",
"import pandas as pd\n",
"from datasets import Dataset as HFDataset\n",
"import torch\n",
"\n",
"def preprocess_data(texts, label, tokenizer, max_length):\n",
" # Tokenize the input texts\n",
" encodings = tokenizer(texts, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')\n",
"\n",
" # Convert PyTorch tensors to lists\n",
" input_ids = encodings['input_ids'].tolist()\n",
" attention_mask = encodings['attention_mask'].tolist()\n",
" token_type_ids = encodings['token_type_ids'].tolist() if 'token_type_ids' in encodings else None\n",
"\n",
" # Ensure labels are also in list format\n",
" if isinstance(label, torch.Tensor):\n",
" label = label.tolist()\n",
"\n",
" # Create a dictionary for the dataset\n",
" dataset_dict = {\n",
" 'input_ids': input_ids,\n",
" 'attention_mask': attention_mask,\n",
" 'token_type_ids': token_type_ids,\n",
" 'labels': label\n",
" }\n",
"\n",
" # Convert the dictionary to a Pandas DataFrame\n",
" df = pd.DataFrame(dataset_dict)\n",
"\n",
" # Convert the DataFrame to a Hugging Face Dataset\n",
" dataset = HFDataset.from_pandas(df)\n",
"\n",
" print(dataset)\n",
" return dataset"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6VVDZ_WAo9o5",
"outputId": "897c1876-8636-4079-98ca-d002eeb997c7"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at google/electra-small-discriminator and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"13549314\n",
"Dataset({\n",
" features: ['input_ids', 'attention_mask', 'token_type_ids', 'labels'],\n",
" num_rows: 3940\n",
"})\n",
"Dataset({\n",
" features: ['input_ids', 'attention_mask', 'token_type_ids', 'labels'],\n",
" num_rows: 986\n",
"})\n"
]
}
],
"source": [
"from transformers import AutoModelForSequenceClassification,AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
"# Load model directly\n",
"model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)\n",
"\n",
"\n",
"def count_trainable_parameters(model):\n",
" return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"print(count_trainable_parameters(model))\n",
"\n",
"\n",
"\n",
"lora_config = LoraConfig(\n",
" r=8, # Rank of the low-rank matrices\n",
" lora_alpha=16, # Alpha for the LoRA scaling\n",
" lora_dropout=0.1 # Dropout for LoRA layers\n",
")\n",
"# peft_config = PeftConfig(\n",
"# base_model_name_or_path=MODEL_NAME,\n",
"# adapter_config=lora_config\n",
"# )\n",
"# model = get_peft_model(model, peft_config=lora_config)\n",
"# Load and preprocess data\n",
"texts, label = load_data_from_csv() # Replace with your file path\n",
"train_texts, val_texts, train_label, val_label = train_test_split(texts, label, test_size=0.2, random_state=42)\n",
"\n",
"train_dataset = preprocess_data(train_texts, train_label, tokenizer, MAX_LENGTH)\n",
"val_dataset = preprocess_data(val_texts, val_label, tokenizer, MAX_LENGTH)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "l7FiPtRFr9ma",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "de83f7ea-0c56-4695-fe73-5ffffc8ca0cc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"13549314\n"
]
}
],
"source": [
"print(count_trainable_parameters(model))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 422
},
"id": "8qZSOElhsDsG",
"outputId": "46e7b2c1-6ba8-4fb4-c083-7282feab6194"
},
"outputs": [
{
"output_type": "error",
"ename": "RuntimeError",
"evalue": "CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-3e927ad3458e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m training_args = TrainingArguments(\n\u001b[1;32m 3\u001b[0m \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'./results'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0meval_strategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLEARNING_RATE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m 168\u001b[0m \"\"\"\n\u001b[1;32m 169\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
]
}
],
"source": [
"torch.cuda.empty_cache()\n",
"training_args = TrainingArguments(\n",
" output_dir='./results',\n",
" eval_strategy='epoch',\n",
" learning_rate=LEARNING_RATE,\n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=16,\n",
" num_train_epochs=6,\n",
" weight_decay=0.001,\n",
" logging_dir='./logs',\n",
" logging_steps=1,\n",
" remove_unused_columns=False)\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=val_dataset,\n",
")\n",
"\n",
"trainer.train()\n",
"trainer.evaluate()\n",
"#trainer.save_model('./final_model')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SHjymNkXuwEY"
},
"outputs": [],
"source": [
"# from huggingface_hub import notebook_login\n",
"# notebook_login()\n",
"# repo_name = \"AiisNothing/electra-discriminator-trained-merged-dataset-version1\"\n",
"# model = model.merge_and_unload()\n",
"\n",
"# model.push_to_hub(repo_name)\n",
"# tokenizer.push_to_hub(repo_name)"
]
},
{
"cell_type": "code",
"source": [
"model.save_pretrained('/content/final_model')\n",
"tokenizer.save_pretrained('/content/final_model')"
],
"metadata": {
"id": "RfSas7HWwNfG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "POq_UyFZw-mS"
},
"outputs": [],
"source": [
"# After inference\n",
"del tokenized_inputs, outputs, logits\n",
"torch.cuda.empty_cache() # Clear unused memory\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s4p4Lv0Ry_J5"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"import torch\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Load the dataset from Hugging Face Hub (test split)\n",
"dataset = load_dataset(\"AiisNothing/test_data\", split=\"test\")\n",
"\n",
"# Load the tokenizer and the model from your Hugging Face model repository\n",
"repo_name = \"AiisNothing/electra-discriminator-trained-merged-dataset-version1\" # Replace with your repo name\n",
"tokenizer = AutoTokenizer.from_pretrained(repo_name)\n",
"model = AutoModelForSequenceClassification.from_pretrained(repo_name)\n",
"\n",
"# Move model to GPU if available and set to eval mode\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"model.to(device)\n",
"model.eval() # Set the model to evaluation mode\n",
"\n",
"# Prepare inputs from the dataset (assuming the 'dialogue' column contains the text and 'label' contains the labels)\n",
"inputs = dataset['dialogue']\n",
"true_labels = dataset['label']\n",
"\n",
"predicted_labels = []\n",
"\n",
"# Process each input one by one\n",
"for i in range(len(inputs)):\n",
" # Get the current input\n",
" current_input = inputs[i]\n",
"\n",
" # Tokenize the input\n",
" tokenized_input = tokenizer(current_input, padding=True, truncation=True, return_tensors=\"pt\", max_length=256)\n",
"\n",
" # Move the tokenized input to GPU\n",
" tokenized_input = {k: v.to(device) for k, v in tokenized_input.items()}\n",
"\n",
" # Perform inference (disable gradients for faster evaluation)\n",
" with torch.no_grad():\n",
" outputs = model(**tokenized_input)\n",
"\n",
" # Get the logits (raw predictions)\n",
" logits = outputs.logits\n",
"\n",
" # Convert logits to predicted class (using argmax)\n",
" predicted_labels.append(torch.argmax(logits, dim=-1).cpu().item()) # Use .item() to get a Python number\n",
"\n",
" # Clear GPU memory\n",
" del tokenized_input, outputs, logits\n",
" torch.cuda.empty_cache() # Clear unused memory\n",
"\n",
"# Calculate accuracy\n",
"accuracy = accuracy_score(true_labels, predicted_labels)\n",
"\n",
"# Report accuracy\n",
"print(f\"Model Accuracy on Test Split: {accuracy * 100:.2f}%\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9qSC8A70vGJ8"
},
"outputs": [],
"source": [
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I3XtYBPa0UVE"
},
"outputs": [],
"source": [
"pip install optimum[exporters]"
]
},
{
"cell_type": "code",
"source": [
"from optimum.onnxruntime import ORTModelForSequenceClassification\n",
"from transformers import AutoTokenizer\n",
"from onnxruntime.quantization import quantize_dynamic, QuantType\n",
"\n",
"model_checkpoint = \"\"\n",
"save_directory = \"\"\n",
"\n",
"# Load a model from transformers and export it to ONNX\n",
"ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
"\n",
"# Save the ONNX model and tokenizer\n",
"ort_model.save_pretrained(save_directory)\n",
"tokenizer.save_pretrained(save_directory)\n",
"\n",
"# Quantize the exported ONNX model to 8-bit\n",
"onnx_model_path = f\"{save_directory}/model.onnx\"\n",
"quantized_model_path = f\"{save_directory}/model-quantized.onnx\"\n",
"\n",
"# Apply dynamic quantization\n",
"quantize_dynamic(\n",
" model_input=onnx_model_path,\n",
" model_output=quantized_model_path,\n",
" weight_type=QuantType.QUInt8 # Quantize weights to 8-bit\n",
")\n",
"\n",
"print(f\"Quantized model saved to: {quantized_model_path}\")"
],
"metadata": {
"id": "PFWPfabCwCZe"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |