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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "Twitter/twhin-bert-large", "model-index": [{"name": "TwHIN-BERT-Misinformation-Classifier", "results": []}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer base_model:Twitter/twhin-bert-large license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-Twitter/twhin-bert-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TwHIN-BERT-Misinformation-Classifier
====================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-Twitter/twhin-bert-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-Twitter/twhin-bert-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3