---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FakevsRealNews
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# FakevsRealNews

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on "Fake and real news dataset" dataset.

Link to Dataset : https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset

It achieves the following results on the evaluation set:
- Loss: 0.0006
- Accuracy: 0.6309
- F1: 0.7677
- Precision: 0.6233
- Recall: 0.9992

## Model description

Finetuned Distilbert

## Intended uses & limitations

More information needed

## Training and evaluation data

The data was split into train-dev-test sets on a ratio of 80:10:10

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0176        | 1.0   | 1956 | 0.0009          | 0.9616   | 0.9695 | 0.9409    | 1.0    |
| 0.0014        | 2.0   | 3912 | 0.0015          | 0.9864   | 0.9890 | 0.9783    | 1.0    |
| 0.0011        | 3.0   | 5868 | 0.0008          | 0.7611   | 0.8363 | 0.7188    | 0.9996 |
| 0.0008        | 4.0   | 7824 | 0.0008          | 0.7872   | 0.8514 | 0.7418    | 0.9992 |
| 0.0006        | 5.0   | 9780 | 0.0006          | 0.6309   | 0.7677 | 0.6233    | 0.9992 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1