---
tags:

model-index:
- name: bertweet-covid--vaccine-tweets-finetuned
  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. -->

# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets

This model is a fine-tuned version of [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) which was finetuned by using [this google fact check](https://huggingface.co./datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co./datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine.
It achieves the following results on the evaluation set (20% from the dataset randomly shuffled and selected to serve as a test set):
- Validation Loss: 0.246620
- Accuracy: 0.902417%

To use the model, use the inference API.

Alternatively, to run locally
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")

model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")

```

## Model description

This model is a fine-tuned version of pretrained version [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets). Click on [this](https://huggingface.co./justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) to see how the pre-training was done.

This model was fine-tuned with a dataset of ~5500. A web scraper was used to scrape polifact and a script was used to pull from the google fact check API. Because ~80% of both these datasets were either false or misleading, I pulled about ~1200 tweets from the CDC related to covid and labelled them as true. ~30% of this dataset is considered true and the rest false or misleading. Please see the published datasets above for more detailed information.

The tokenizer requires the emoji library to be installed.
```
!pip install nltk emoji
```

## Intended uses & limitations

The intended use of this model is to detect if the contents of a covid tweet is potentially false or misleading. This model is not an end all be all. It has many limitations. For example, if someone makes a post containing an image, but has attached a satirical image, this model would not be able to distinguish this. If a user links a website, the tokenizer allocates a special token for links, meaning the contents of the linked website is completely lost. If someone tweets a reply, this model can't look at the parent tweets, and will lack context.

This model's dataset relies on the crowd-sourcing annotations being accurate.




## Training and evaluation data

This model was finetuned by using [this google fact check](https://huggingface.co./datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co./datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine.


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- 

### Training results

| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.435500      | 1.0   | 0.401900        | 0.906893 |
| 0.309700      | 2.0   | 0.265500        | 0.907789 |
| 0.266200      | 3.0   | 0.216500        | 0.911370 |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3