
# covid-twitter-bert-v2-struth

This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the [COVID-19 Fake News Dataset NLP by Elvin Aghammadzada](https://www.kaggle.com/datasets/elvinagammed/covid19-fake-news-dataset-nlp?select=Constraint_Val.csv).
It achieves the following results on the evaluation set:
- Loss: 0.1171
- Accuracy: 0.9662
- Precision: 0.9813
- Recall: 0.9493
- F1: 0.9650

## Model description

This model is built on the work on Digital Epidemiology Lab and their COVID Twitter BERT model. We have extended their model by training it for Sequence Classification tasks. This is part of a wider project for True/Fake news by the [Struth Social Team](https://github.com/Struth-Social-UNSW/ITProject2).

## Intended uses & limitations

This model is intended to be used for the classification of Tweets as either true or fake (0 or 1). The model can also be used for relatively complex statements regarding COVID-19.

A known limitation of this model is basic statements (e.g. COVID is a hoax) as the Tweets used to train the model are not simplistic in nature. 

## Training and evaluation data
Training and Testing data was split 80:20 for the results listed above.

Training/Testing Set:
- Samples Total: 8437
- Samples Train: 6749
- Samples Test:  1687

Evaluation Set:
- Samples Total: 100

## Training procedure
1. Data is preprocessed through custom scripts
2. Data is passed to the model training script
3. Training is conducted
4. Best model is retrieved at end of training and uploaded to the Hub

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1719        | 1.0   | 422  | 0.1171          | 0.9662   | 0.9813    | 0.9493 | 0.9650 |
| 0.0565        | 2.0   | 844  | 0.1595          | 0.9621   | 0.9421    | 0.9831 | 0.9622 |
| 0.0221        | 3.0   | 1266 | 0.2059          | 0.9585   | 0.9859    | 0.9287 | 0.9565 |
| 0.009         | 4.0   | 1688 | 0.1378          | 0.9722   | 0.9600    | 0.9843 | 0.9720 |
| 0.0021        | 5.0   | 2110 | 0.2013          | 0.9722   | 0.9863    | 0.9565 | 0.9712 |
| 0.0069        | 6.0   | 2532 | 0.2894          | 0.9615   | 0.9948    | 0.9263 | 0.9593 |
| 0.0054        | 7.0   | 2954 | 0.2692          | 0.9650   | 0.9949    | 0.9336 | 0.9632 |
| 0.0058        | 8.0   | 3376 | 0.2406          | 0.9639   | 0.9776    | 0.9481 | 0.9626 |
| 0.0017        | 9.0   | 3798 | 0.1877          | 0.9722   | 0.9654    | 0.9783 | 0.9718 |
| 0.0019        | 10.0  | 4220 | 0.2761          | 0.9686   | 0.9850    | 0.9505 | 0.9674 |
| 0.007         | 11.0  | 4642 | 0.1889          | 0.9722   | 0.9875    | 0.9553 | 0.9711 |
| 0.0007        | 12.0  | 5064 | 0.2774          | 0.9662   | 0.9837    | 0.9469 | 0.9649 |
| 0.0008        | 13.0  | 5486 | 0.2344          | 0.9722   | 0.9791    | 0.9638 | 0.9714 |
| 0.0           | 14.0  | 5908 | 0.2768          | 0.9662   | 0.9789    | 0.9517 | 0.9651 |
| 0.0           | 15.0  | 6330 | 0.2798          | 0.9662   | 0.9789    | 0.9517 | 0.9651 |
| 0.0           | 16.0  | 6752 | 0.2790          | 0.9668   | 0.9789    | 0.9529 | 0.9657 |
| 0.0           | 17.0  | 7174 | 0.2850          | 0.9668   | 0.9789    | 0.9529 | 0.9657 |
| 0.0           | 18.0  | 7596 | 0.2837          | 0.9668   | 0.9789    | 0.9529 | 0.9657 |
| 0.0           | 19.0  | 8018 | 0.2835          | 0.9674   | 0.9789    | 0.9541 | 0.9664 |
| 0.0           | 20.0  | 8440 | 0.2842          | 0.9674   | 0.9789    | 0.9541 | 0.9664 |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1

{"language": "en", "license": "afl-3.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "widget": [{"text": "COVID vaccines can prevent serious illness and death from COVID-19", "example_title": "Real Tweet"}, {"text": "COVID vaccines are not effective at protecting you from serious illness and death from COVID-19", "example_title": "Fake Tweet"}], "model-index": [{"name": "covid-twitter-bert-v2-struth", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

covid-twitter-bert-v2-struth
============================


This model is a fine-tuned version of digitalepidemiologylab/covid-twitter-bert-v2 on the COVID-19 Fake News Dataset NLP by Elvin Aghammadzada.
It achieves the following results on the evaluation set:


* Loss: 0.1171
* Accuracy: 0.9662
* Precision: 0.9813
* Recall: 0.9493
* F1: 0.9650


Model description
-----------------


This model is built on the work on Digital Epidemiology Lab and their COVID Twitter BERT model. We have extended their model by training it for Sequence Classification tasks. This is part of a wider project for True/Fake news by the Struth Social Team.


Intended uses & limitations
---------------------------


This model is intended to be used for the classification of Tweets as either true or fake (0 or 1). The model can also be used for relatively complex statements regarding COVID-19.


A known limitation of this model is basic statements (e.g. COVID is a hoax) as the Tweets used to train the model are not simplistic in nature.


Training and evaluation data
----------------------------


Training and Testing data was split 80:20 for the results listed above.


Training/Testing Set:


* Samples Total: 8437
* Samples Train: 6749
* Samples Test: 1687


Evaluation Set:


* Samples Total: 100


Training procedure
------------------


1. Data is preprocessed through custom scripts
2. Data is passed to the model training script
3. Training is conducted
4. Best model is retrieved at end of training and uploaded to the Hub


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 20


### Training results



### Framework versions


* Transformers 4.22.2
* Pytorch 1.12.1+cu113
* Datasets 2.5.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 20 ### Training results ### Framework versions


* Transformers 4.22.2
* Pytorch 1.12.1+cu113
* Datasets 2.5.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #license-afl-3.0 #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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.22.2
* Pytorch 1.12.1+cu113
* Datasets 2.5.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #license-afl-3.0 #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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.22.2
* Pytorch 1.12.1+cu113
* Datasets 2.5.1
* Tokenizers 0.12.1