metadata
license: apache-2.0
tags:
- Fake News Detection
- Text Classification
model-index:
- name: distilroberta-base-finetuned-fake-news-detection
results: []
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:
| Precision | Recall | F-1 Score | Support | |
|---|---|---|---|---|
| Not Hate Speech (0) | 0.99 | 0.99 | 0.99 | 4335 |
| Hate Speech (1) | 0.99 | 0.99 | 00.99 | 3782 |
| accuracy | 00.99 | 8117 | ||
| macro avg | 0.99 | 0.99 | 0.99 | 8117 |
| weighted avg | 0.99 | 0.99 | 0.99 | 8117 |
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
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.099100 | 0.042086 |
| 2 | 0.030200 | 0.028448 |
| 3 | 0.017500 | 0.024397 |
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)
Framework versions
- Transformers 4.12.5
- Pytorch 1.11.0
- Datasets 1.17.0
- Tokenizers 0.10.3