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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: TwHIN-BERT-Misinformation-Classifier
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. -->
# TwHIN-BERT-Misinformation-Classifier
This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0632
- F1: 0.9829
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3092 | 0.04 | 250 | 0.0873 | 0.9683 |
| 0.095 | 0.09 | 500 | 0.0973 | 0.9752 |
| 0.0937 | 0.13 | 750 | 0.0969 | 0.9669 |
| 0.0859 | 0.17 | 1000 | 0.0762 | 0.9772 |
| 0.0828 | 0.22 | 1250 | 0.1208 | 0.9669 |
| 0.0685 | 0.26 | 1500 | 0.0745 | 0.9803 |
| 0.0801 | 0.3 | 1750 | 0.0678 | 0.9798 |
| 0.0717 | 0.35 | 2000 | 0.1027 | 0.9783 |
| 0.1132 | 0.39 | 2250 | 0.0968 | 0.9771 |
| 0.1088 | 0.43 | 2500 | 0.0838 | 0.9794 |
| 0.0976 | 0.48 | 2750 | 0.0915 | 0.9787 |
| 0.0863 | 0.52 | 3000 | 0.0828 | 0.9797 |
| 0.0967 | 0.56 | 3250 | 0.1350 | 0.9722 |
| 0.1225 | 0.61 | 3500 | 0.1098 | 0.9753 |
| 0.1017 | 0.65 | 3750 | 0.0807 | 0.9754 |
| 0.091 | 0.69 | 4000 | 0.1083 | 0.9757 |
| 0.0882 | 0.74 | 4250 | 0.0768 | 0.9811 |
| 0.0688 | 0.78 | 4500 | 0.0819 | 0.9788 |
| 0.0728 | 0.82 | 4750 | 0.0758 | 0.9802 |
| 0.0967 | 0.87 | 5000 | 0.1157 | 0.9743 |
| 0.0862 | 0.91 | 5250 | 0.0677 | 0.9816 |
| 0.0815 | 0.95 | 5500 | 0.0709 | 0.9768 |
| 0.0776 | 1.0 | 5750 | 0.0737 | 0.9799 |
| 0.0635 | 1.04 | 6000 | 0.0941 | 0.9761 |
| 0.0805 | 1.08 | 6250 | 0.0993 | 0.9778 |
| 0.0887 | 1.13 | 6500 | 0.0916 | 0.9770 |
| 0.0824 | 1.17 | 6750 | 0.0859 | 0.9790 |
| 0.0885 | 1.21 | 7000 | 0.0893 | 0.9795 |
| 0.0868 | 1.26 | 7250 | 0.0686 | 0.9801 |
| 0.09 | 1.3 | 7500 | 0.0940 | 0.9767 |
| 0.0907 | 1.34 | 7750 | 0.0878 | 0.9796 |
| 0.0835 | 1.39 | 8000 | 0.0736 | 0.9790 |
| 0.0747 | 1.43 | 8250 | 0.0878 | 0.9817 |
| 0.0745 | 1.47 | 8500 | 0.0675 | 0.9809 |
| 0.0754 | 1.52 | 8750 | 0.0708 | 0.9813 |
| 0.0781 | 1.56 | 9000 | 0.0816 | 0.9822 |
| 0.069 | 1.6 | 9250 | 0.0761 | 0.9820 |
| 0.0708 | 1.65 | 9500 | 0.0887 | 0.9814 |
| 0.1062 | 1.69 | 9750 | 0.0887 | 0.9803 |
| 0.0643 | 1.73 | 10000 | 0.0771 | 0.9821 |
| 0.0675 | 1.77 | 10250 | 0.0870 | 0.9809 |
| 0.0705 | 1.82 | 10500 | 0.0791 | 0.9817 |
| 0.0737 | 1.86 | 10750 | 0.0780 | 0.9815 |
| 0.0665 | 1.9 | 11000 | 0.0779 | 0.9828 |
| 0.0919 | 1.95 | 11250 | 0.0905 | 0.9768 |
| 0.0687 | 1.99 | 11500 | 0.0647 | 0.9806 |
| 0.0675 | 2.03 | 11750 | 0.0634 | 0.9814 |
| 0.0549 | 2.08 | 12000 | 0.0670 | 0.9812 |
| 0.0593 | 2.12 | 12250 | 0.0676 | 0.9815 |
| 0.0603 | 2.16 | 12500 | 0.0624 | 0.9828 |
| 0.0553 | 2.21 | 12750 | 0.0762 | 0.9806 |
| 0.0502 | 2.25 | 13000 | 0.0798 | 0.9835 |
| 0.0431 | 2.29 | 13250 | 0.1644 | 0.9227 |
| 0.0514 | 2.34 | 13500 | 0.0831 | 0.9803 |
| 0.0536 | 2.38 | 13750 | 0.0585 | 0.9833 |
| 0.0472 | 2.42 | 14000 | 0.0570 | 0.9839 |
| 0.0581 | 2.47 | 14250 | 0.0561 | 0.9828 |
| 0.0465 | 2.51 | 14500 | 0.0585 | 0.9842 |
| 0.0542 | 2.55 | 14750 | 0.1652 | 0.9827 |
| 0.0595 | 2.6 | 15000 | 0.1015 | 0.9821 |
| 0.051 | 2.64 | 15250 | 0.2099 | 0.8379 |
| 0.0447 | 2.68 | 15500 | 0.0633 | 0.9826 |
| 0.0389 | 2.73 | 15750 | 0.0647 | 0.9830 |
| 0.0533 | 2.77 | 16000 | 0.0623 | 0.9829 |
| 0.0524 | 2.81 | 16250 | 0.0586 | 0.9831 |
| 0.0576 | 2.86 | 16500 | 0.0597 | 0.9830 |
| 0.0483 | 2.9 | 16750 | 0.0689 | 0.9836 |
| 0.0469 | 2.94 | 17000 | 0.0569 | 0.9837 |
| 0.0447 | 2.99 | 17250 | 0.0632 | 0.9829 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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