distilroberta-base-finetuned-fake-news-detection

distilroberta-base-finetuned-fake-news-detection

This model is a fine-tuned version of distilroberta-base on this Fake News Detection Dataset, which has been constructed by combining multiple Fake News datasets from Kaggle.

This is the classification report after training for 3 full epochs:

Training and evaluation data

All of the process to train this model is available in this repository. The dataset has been split into 24,353 examples for training & 8,117 examples for validation & testing each.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• optimizer: default AdamW Optimizer
• num_epochs: 3
• warmup_steps: 500
• weight_decay: 0.01
• random seed: 42

I also trained for 3 full epochs on Colab's Tesla P100-PCIE-16GB GPU.

Training results

Model in Action 🚀

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn as nn
tokenizer = AutoTokenizer.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
model = AutoModelForSequenceClassification.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
#Following the same truncation & padding strategy used while training
encoded_input = tokenizer("Enter any news article to be classified. Can be a list of articles too.", truncation = True, padding = "max_length", max_length = 512, return_tensors='pt')
output = model(**encoded_input)["logits"]
#detaching the output from the computation graph
detached_output = output.detach()
#Applying softmax here for single label classification
softmax = nn.Softmax(dim = 1)
prediction_probabilities = list(softmax(detached_output).detach().numpy())
predictions = []
for x,y in prediction_probabilities:
   predictions.append("not_fake_news") if x > y else predictions.append("fake_news")
print(predictions)

Please note that if you're performing inference on a lengthy dataset, split it up into multiple batches, otherwise your RAM will overflow, unless you're using a really high end GPU/TPU setup. I'd recommend a batch length of 50, if you're working with a vanilla GPU setup.

Framework versions

• Transformers 4.12.5
• Pytorch 1.11.0
• Datasets 1.17.0
• Tokenizers 0.10.3