baseline_model

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This is the baseline model for the news source classification project.

Please run the following evaluation pipeline code:

START

Imports

<pre>from huggingface_hub import hf_hub_download import joblib !huggingface-cli login import pandas as pd import torch from transformers import AutoTokenizer, AutoModel import torchvision from torchvision import transforms, utils import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms from PIL import Image from skimage import io, transform from torchvision.io import read_image from torch.utils.data import Dataset, DataLoader from sklearn.metrics import accuracy_score import numpy as np import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import nltk from nltk.corpus import stopwords nltk.download('stopwords') nltk.download('wordnet') import re from transformers import DistilBertTokenizer, DistilBertModel</pre>

Load model from Huggingface (Please load test data into test_df below)

<pre>repo_id='awngsz/baseline_model' filename='CIS5190_Proj2_AWNGSZ.joblib' model_file_path=hf_hub_download(repo_id=repo_id, filename=filename) <br> model=joblib.load(model_file_path) print(model) #Load test dataset (assuming the name is the same as the one in the Ed post) <br> test_df = pd.read_csv(file_path) #Copying the naming convention from the sample dataset in the edpost <br> X_test = test_df['title'] y_test = test_df['labels'] </pre>

Clean the data

<pre> def clean_headlines(df, column_name): """ Cleans a specified column in a DataFrame by: - Removing HTML tags - Removing <script> elements - Removing extra spaces, trailing/leading whitespaces - Removing special characters - Removing repeating special characters - Removing tabs - Removing newline characters - Removing specific punctuation: periods, commas, and parentheses - Normalizing double quotes ("") to single quotes ('') Args: df (pd.DataFrame): The DataFrame containing the column to clean column_name (str): The name of the column to clean Returns: pd.DataFrame: A DataFrame with the cleaned column """ # Remove HTML tags df[column_name] = df[column_name].str.replace(r'<[^<]+?>', '', regex=True) # Remove scripts df[column_name] = df[column_name].str.replace(r'<script.*?</script>', '', regex=True)

# Remove extra spaces including leading/trailing whitespaces
df[column_name] = df[column_name].str.strip().str.replace(r'\s+', ' ', regex=True)

# Remove special characters
df[column_name] = df[column_name].str.strip().str.replace(r'[&*|~`^=_+{}[\]<>\\]', ' ', regex=True)

# Remove repeating special characters
df[column_name] = df[column_name].str.strip().str.replace(r'([?!])\1+', r'\1', regex=True)

# Remove tabs
df[column_name] = df[column_name].str.replace(r'\t', ' ', regex=True)

# Remove newline characters
df[column_name] = df[column_name].str.replace(r'\n', ' ', regex=True)

# Normalize double quotes to single quotes
# df[column_name] = df[column_name].str.replace(r'"', "'", regex=True)

# Punctuation
# df[column_name] = df[column_name].str.replace(r'[.,()]', '', regex=True)

return df </pre>

<pre> def normalize_headlines(df, column_name): """ Normalizes a given headline by: - converting it to lowercase - removing stopwords - applying stemming or lemmatization to reduce words to their base forms Args: df (pd.DataFrame): The DataFrame containing the column to clean column_name (str): The name of the column to clean Returns: pd.DataFrame: A DataFrame with the cleaned column """ # Convert headlines to lowercase df[column_name] = df[column_name].str.lower() # Remove stopwords from headline stop_words = set(stopwords.words('english')) df[column_name] = df[column_name].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)])) # Lemmatize words to base form lemmatizer = nltk.stem.WordNetLemmatizer() df[column_name] = df[column_name].apply(lambda x: ' '.join([lemmatizer.lemmatize(word) for word in x.split()])) return df </pre> <pre> def handle_missing_data(df, column_name): """ Handles missing or incomplete data in a given column of a DataFrame, including: - Replacing NULL values with "Unknown Headline" - Augmenting the data by creating headlines with synonyms of words in other headlines Args: df (pd.DataFrame): The DataFrame containing the column to clean column_name (str): The name of the column to clean Returns: pd.DataFrame: A DataFrame with the cleaned column """ # Remove NULL headlines df = df.dropna(subset=[column_name]) # Set a minimum word count threshold min_word_count = 3 # Filter out titles with fewer words df = df[df[column_name].str.split().apply(len) >= min_word_count].reset_index(drop=True) return df </pre> <pre> def consistency_checks(df, column_name): """ Ensures all headlines follow a consistent format by: - Removing duplicate headlines Args: df (pd.DataFrame): The DataFrame containing the column to clean column_name (str): The name of the column to clean Returns: pd.DataFrame: A DataFrame with the cleaned column """ # Remove duplicate headlines df = df.drop_duplicates(subset=[column_name]) # Filter headlines with too few or too many words #df = df[df['title'].str.split().apply(len).between(3, 20)] return df </pre> <pre> X_test = clean_headlines(X_test, 'title') X_test = normalize_headlines(X_test, 'title') X_test = X_test.dropna(subset = ['title']) X_test = handle_missing_data(X_test, 'title') X_test = consistency_checks(X_test, 'title') </pre>

Load the embedding model from Huggingface. Transformer: DistilBERT

<pre> def get_embeddings(text_all, tokenizer, model, device, max_len=128): ''' Generate embeddings using a transformer model on GPU if available. Args: - text_all: List of input texts - tokenizer: Tokenizer for the model - model: Transformer model - device: torch.device to run the computations - max_len: Maximum token length for the input Returns: - embeddings: List of embeddings for each input text ''' embeddings = [] count = 0 print('Start embeddings:') for text in text_all: count += 1 if count % (len(text_all) // 10) == 0: print(f'{count / len(text_all) * 100:.1f}% done ...') # Tokenize the input text model_input_token = tokenizer( text, add_special_tokens=True, max_length=max_len, padding='max_length', truncation=True, return_tensors='pt' ).to(device) # Move input tensors to GPU # Generate embeddings without gradient computation with torch.no_grad(): model_output = model(**model_input_token) cls_embedding = model_output.last_hidden_state[:, 0, :] # Use CLS token embedding cls_embedding = cls_embedding.squeeze().cpu().numpy() # Move back to CPU for numpy embeddings.append(cls_embedding) return embeddings </pre>

Check for GPU availability

<pre> device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f'Using device: {device}') # Load the tokenizer and model for 'all-mpnet-base-v2' print("Loading model and tokenizer...") # Load model and tokenizer tokenizer_news = AutoTokenizer.from_pretrained('distilbert-base-uncased') model_news = AutoModel.from_pretrained('distilbert-base-uncased').to(device) # Set the model to evaluation mode model_news.eval() ############################################# DBERT UNCASED Embedding ############################################# ############################################# Embedding ############################################# print("Computing DBERT embeddings for training data...") X_test_embeddings_DBERT = get_embeddings(X_test, tokenizer_news, model_news, device, max_len=128) print("DBERT embeddings for training data computed!") prediction = model.predict(X_test_embeddings_DBERT) </pre>

Accuracy

<pre>label_map = {'NBC': 1, 'FoxNews': 0} def compute_category_accuracy(y_true, y_pred, label): n_correct = np.sum((y_true == label) & (y_pred == label)) n_total = np.sum(y_true == label) cat_accuracy = n_correct / n_total return cat_accuracy #Print accuracy print(f'Test accuracy: {accuracy_score(y_test, prediction) * 100:.2f}%') print(f'Test accuracy for NBC: {compute_category_accuracy(y_test, prediction, label_map["NBC"]) * 100:.2f}%') print(f'Test accuracy for FoxNews: {compute_category_accuracy(y_test, prediction, label_map["FoxNews"]) * 100:.2f}%') </pre> <!-- from huggingface_hub import hf_hub_download import joblib #Load model from Huggingface repo_id='awngsz/baseline_model' filename='CIS5190_Proj2_AWNGSZ.joblib' file_path=hf_hub_download(repo_id=repo_id, filename=filename) model=joblib.load(file_path) print(model) #Load test dataset (assuming the name is the same as the one in the Ed post) test_df = pd.read_csv(file_path) #Copying the naming convention from the sample dataset in the edpost X_test = test_df['title'] y_test = test_df['labels'] #Load the embedding model from Huggingface ############################################# Transformer: DistilBERT ############################################# from transformers import DistilBertTokenizer, DistilBertModel # pytorch related packages import torch import torchvision from torchvision import transforms, utils import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms from PIL import Image from skimage import io, transform from torchvision.io import read_image from torch.utils.data import Dataset, DataLoader def get_embeddings(text_all, tokenizer, model, max_len = 128): ''' return: embeddings list ''' embeddings = [] count = 0 print('Start embeddings:') for text in text_all: count += 1 if count % (len(text_all) // 10) == 0: print(f'{count / len(text_all) * 100:.1f}% done ...') model_input_token = tokenizer( text, add_special_tokens = True, max_length = max_len, padding = 'max_length', truncation = True, return_tensors = 'pt' ) with torch.no_grad(): model_output = model(**model_input_token) cls_embedding = model_output.last_hidden_state[:, 0, :] cls_embedding = cls_embedding.squeeze().numpy() embeddings.append(cls_embedding) return embeddings #Load the tokenizer and model from Hugging Face tokenizer_DBERT = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') transformer_model_DBERT = DistilBertModel.from_pretrained('distilbert-base-uncased') #Set the model to evaluation mode transformer_model_DBERT.eval() #Get the embeddings for the test data max_len = max(len(text) for text in X_test) #this may take awhile to run X_test_embeddings_DBERT = get_embeddings(X_test, tokenizer_DBERT, transformer_model_DBERT, max_len = max_len) prediction = model.predict(X_test_embeddings_DBERT) #Accuracy from sklearn.metrics import accuracy_score label_map = {'NBC': 1, 'FoxNews': 0} def compute_category_accuracy(y_true, y_pred, label): n_correct = np.sum((y_true == label) & (y_pred == label)) n_total = np.sum(y_true == label) cat_accuracy = n_correct / n_total return cat_accuracy #Print accuracy print(f'Test accuracy: {accuracy_score(y_test, prediction) * 100:.2f}%') print(f'Test accuracy for NBC: {compute_category_accuracy(y_test, prediction, label_map["NBC"]) * 100:.2f}%') print(f'Test accuracy for FoxNews: {compute_category_accuracy(y_test, prediction, label_map["FoxNews"]) * 100:.2f}%') -->

END

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