---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fake-news-detector
  results: []
widget:
- text: >-
    In a shocking turn of events, reports have surfaced suggesting that a
    clandestine meeting of world leaders took place on Mars to discuss plans for
    the colonization of the Red Planet. According to anonymous sources within
    the highest echelons of government, the summit was organized by a coalition
    of space agencies and private corporations aiming to expedite humanity's
    expansion beyond Earth. The meeting purportedly took place in a hidden
    underground facility on Mars, accessible only to a select few individuals
    privy to the ambitious project.
  example_title: Mars Meeting
- text: >-
    In a groundbreaking revelation that has sent shockwaves through the
    scientific community, Dr. Rachel Bennett, a renowned researcher at the
    prestigious Cambridge Institute of Biotechnology, claims to have unlocked
    the elusive secret to eternal youth. According to Dr. Bennett, years of
    tireless research have culminated in the discovery of a revolutionary
    anti-aging compound derived from a rare Amazonian plant known only to
    indigenous tribes. Initial trials on laboratory mice have yielded
    astonishing results, with subjects exhibiting signs of reversed aging and
    enhanced vitality.
  example_title: Dr. Bennett
- text: Apples are orange
  example_title: Oranges are Apples
- text: Donald Trump is the 45th president of the United States.
  example_title: True News
datasets:
- AlexanderHolmes0/true-fake-news
language:
- en
pipeline_tag: text-classification
---

<!-- 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. -->

# fake-news-detector

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0027
- Accuracy: 0.9994

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.201         | 0.09  | 100  | 0.0444          | 0.9901   |
| 0.0319        | 0.19  | 200  | 0.0241          | 0.9938   |
| 0.0222        | 0.28  | 300  | 0.0249          | 0.9932   |
| 0.0094        | 0.38  | 400  | 0.0076          | 0.9984   |
| 0.0042        | 0.47  | 500  | 0.0062          | 0.9988   |
| 0.0076        | 0.57  | 600  | 0.0040          | 0.9988   |
| 0.0095        | 0.66  | 700  | 0.0040          | 0.9990   |
| 0.008         | 0.76  | 800  | 0.0040          | 0.9988   |
| 0.0086        | 0.85  | 900  | 0.0030          | 0.9993   |
| 0.0042        | 0.95  | 1000 | 0.0027          | 0.9994   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1