---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased_fakenews_identification
  results: []
---

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# distilbert-base-uncased_fakenews_identification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the below dataset.
https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
It achieves the following results on the evaluation set:
- Loss: 0.0059
- Accuracy: 0.999
- F1: 0.9990

## Label Description

LABEL_0  - Fake News
LABEL_1  - Real News
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0014        | 1.0   | 1000 | 0.0208          | 0.9965   | 0.9965 |
| 0.0006        | 2.0   | 2000 | 0.0041          | 0.9994   | 0.9994 |
| 0.0006        | 3.0   | 3000 | 0.0044          | 0.9992   | 0.9993 |
| 0.0           | 4.0   | 4000 | 0.0059          | 0.999    | 0.9990 |


### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6