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FakeNewsDetection_Cross-Sean

Text Classification
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bert
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FakeNewsDetection_Cross-Sean / README.md
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metadata
license: mit
base_model: digitalepidemiologylab/covid-twitter-bert-v2
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: FakeNewsDetection_Cross-Sean
    results: []

FakeNewsDetection_Cross-Sean

This model is a fine-tuned version of digitalepidemiologylab/covid-twitter-bert-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0824
  • F1: 0.9882

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
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss F1
0.0786 1.0 1100 0.0654 0.9845
0.0386 2.0 2200 0.0574 0.9852
0.0222 3.0 3300 0.0689 0.9864
0.0098 4.0 4400 0.0924 0.9848
0.0059 5.0 5500 0.0824 0.9882

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.1