---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
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/nn_model'  
filename='nn_model_v3.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 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 all references to US as u.s.
    df[column_name] = df[column_name].str.replace(r'US', 'u.s.', regex=True)
    df[column_name] = df[column_name].str.replace(r'UN', 'u.n.', regex=True)

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

    # get rid of these fox news patterns we see
    df[column_name] = df[column_name].str.replace(r'fox news poll:', '', regex=True)

    df[column_name] = df[column_name].str.replace(r'| fox news', '', regex=True)

    df[column_name] = df[column_name].str.replace(r'Fox News', '', regex=True)
    df[column_name] = df[column_name].str.replace(r'fox news', '', regex=True)

    df[column_name] = df[column_name].str.replace(r'news poll:', '', regex=True)

    df[column_name] = df[column_name].str.replace(r'opinion:', '', regex=True)

    df[column_name] = df[column_name].str.replace(r"reporter's notebook", '', 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...")

y_test = X_test['labels']
X_test = X_test['title']
  
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': 0, 'FoxNews': 1}

def compute_category_accuracy(y_true, y_pred, label):
  y_true = np.array(y_true)
  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 ######

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]