metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
base_model: albert-base-v2
model-index:
  - name: albert-base-v2-fakenews-discriminator
    results: []

albert-base-v2-fakenews-discriminator

The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset

I use title and label to train the classifier

label_0 : Fake news label_1 : Real news

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0910
Accuracy: 0.9758

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: 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0452	1.0	1768	0.0910	0.9758

Framework versions

Transformers 4.12.3
Pytorch 1.10.0+cu111
Datasets 1.15.1
Tokenizers 0.10.3