language:
- en
license: mit
library_name: transformers
tags:
- fake news
metrics:
- accuracy
pipeline_tag: text-classification
Model Card for Model ID
Predicts whether the news article's title is fake or real. This is my first work, if you find the model interesting or useful, please like it, it will encourage me to do more research <3
Model Details
Model Description
This model's purpose is to classify, whether the information, given in the news article, is true or false. It was trained on 2 datasets, combined and preprocessed. 0 (LABEL_0) stands for false and 1 stands for true.
- Developed by: Ostap Mykhailiv
- Model type: Classification
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: google-bert/bert-base-uncased
Bias, Risks, and Limitations
Since it's a Bert model, it also exhibits bias. Be careful about checking some specific data by this model, since it was trained on pre-2023 data. Additionally, the lack of preprocessing for people's names in the training data might cause a bias towards certain persons.
Recommendations
To get better overall results, I decided to make a title truncation in training. Though it increased the overall result for both longer and shorter text, one should not give less than 6 and more than 12 words for predictions, excluding stopwords. For the preprocess operations look below. One can translate news from the language into English, though it may not give the expected results.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import pipeline
pipe = pipeline("text-classification", model="omykhailiv/bert-fake-news-recognition")
pipe.predict('Some text')
It will return something like this: [{'label': 'LABEL_0', 'score': 0.7248537290096283}] Where 'LABEL_0' means false and 'score' stands for the probability of it.
Training Data
https://huggingface.co/datasets/GonzaloA/fake_news https://github.com/GeorgeMcIntire/fake_real_news_dataset
Preprocessing
Preprocessing was made by using this function. Note that the data, tested below, was not truncated to 12 >= len(new_filtered_words) >= 6, but it has still been pre-processed.
import re
import string
import spacy
from nltk.corpus import stopwords
lem = spacy.load('en_core_web_sm')
def testing_data_prep(text):
"""
Args:
text (str): The input text string.
Returns:
str: The preprocessed text string, or an empty string if the length
does not meet the specified criteria (6 to 20 words).
"""
# Convert text to lowercase for case-insensitive processing
text = str(text).lower()
# Remove HTML tags and their contents (e.g., "<tag>text</tag>")
text = re.sub('<.*?>+\w+<.*?>', '', text)
# Remove punctuation using regular expressions and string escaping
text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
# Remove words containing alphanumeric characters followed by digits
# (e.g., "model2023", "data10")
text = re.sub('\w*\d\w*', '', text)
# Remove newline characters
text = re.sub('\n', '', text)
# Replace multiple whitespace characters with a single space
text = re.sub('\\s+', ' ', text)
# Lemmatize words (convert them to their base form)
text = lem(text)
words = [word.lemma_ for word in text]
# Removing stopwords, such as do, not, as, etc. (https://gist.github.com/sebleier/554280)
new_filtered_words = [
word for word in words if word not in stopwords.words('english')]
if 20 >= len(new_filtered_words) >= 6:
return ' '.join(new_filtered_words)
return ' '
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 32
- eval_batch_size: 32
- num_epochs: 5
- warmup_steps: 500
- weight_decay: 0.03
- random seed: 42
Testing Data, Metrics
Testing Data
https://huggingface.co/datasets/GonzaloA/fake_news https://github.com/GeorgeMcIntire/fake_real_news_dataset https://onlineacademiccommunity.uvic.ca/isot/2022/11/27/fake-news-detection-datasets/ https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification/data
Metrics
Accuracy
Results
For testing on GonzaloA/fake_news test split dataset
precision recall f1-score support
0 0.93 0.94 0.94 3782
1 0.95 0.94 0.95 4335
accuracy 0.94 8117
macro avg 0.94 0.94 0.94 8117
weighted avg 0.94 0.94 0.94 8117
For testing on https://github.com/GeorgeMcIntire/fake_real_news_dataset
precision recall f1-score support
0 0.93 0.88 0.90 2297
1 0.89 0.93 0.91 2297
accuracy 0.91 4594
macro avg 0.91 0.91 0.91 4594
weighted avg 0.91 0.91 0.91 4594
For testing on https://onlineacademiccommunity.uvic.ca/isot/2022/11/27/fake-news-detection-datasets/
precision recall f1-score support
0 0.9736 0.9750 0.9743 10455
1 0.9726 0.9711 0.9718 9541
accuracy 0.9731 19996
macro avg 0.9731 0.9731 0.9731 19996
weighted avg 0.9731 0.9731 0.9731 19996
For testing on random 1k rows of https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification/data
precision recall f1-score support
0 0.87 0.80 0.84 492
1 0.82 0.89 0.85 508
accuracy 0.85 1000
macro avg 0.85 0.85 0.85 1000
weighted avg 0.85 0.85 0.85 1000
Hardware
Tesla T4 GPU, available for free in Google Collab