---
language: en
license: apache-2.0
datasets:
- tweets
widget:
- text: "COVID-19 vaccines are safe and effective."
---

# Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.

# Vaccinating COVID tweets
A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine.

## Intended uses & limitations

#### How to use
Load pipeline
```python
from transformers import pipeline
pipe = pipeline("sentiment-analysis", model = "ans/vaccinating-covid-tweets")
seq = 'The Pfizer-BioNTech COVID-19 vaccine is authorized for use under an Emergency Use Authorization (EUA) for active immunization to prevent COVID-19 caused by SARS-CoV-2 in individuals 16 years of age and older'

pipe(seq)
```

Expected output
```
seq = "There is no FDA-approved vaccine to prevent COVID-19."
pipe(seq)
```
[{'label': 'true', 'score': 0.9996959567070007}]

#### Limitations and bias

Provide examples of latent issues and potential remediations.

## Training data & Procedure

#### Pre-trained baseline model
- Pre-trained model: [BERTweet](https://github.com/VinAIResearch/BERTweet)
  - trained based on the RoBERTa pre-training procedure
  - 850M General English Tweets (Jan 2012 to Aug 2019)
  - 23M COVID-19 English Tweets
  - Size of the model: >134M parameters
- Further training
  - Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification

#### 1) Pre-training language model
- The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet
- Following datasets on English tweets were used:
  - Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 ([kaggle](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets))
  - Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 ([kaggle](https://www.kaggle.com/gpreda/all-covid19-vaccines-tweets))
  - COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 ([github](https://github.com/thepanacealab/covid19_twitter))
  
#### 2) Fine-tuning for fact classification
- A fine-tuned model on English tweets using a masked language modeling (MLM) objective from [BERTweet](https://github.com/VinAIResearch/BERTweet) for fact-classification task on COVID-19/vaccine.

- Statements from Poynter and Snopes with Selenium 14,000 fact-checked statements from Jan 2020 to May 2021 
- Divide original labels within 3 categories
  - False: false, no evidence, manipulated, fake, not true, unproven, unverified
  - Misleading: misleading, exaggerated, out of context, needs context
  - True: true, correct

## Eval results

# Contributors
- This page is a part of final team project from MLDL for DS class at SNU
  - Team BIBI - Vaccinating COVID-NineTweets
  - Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom
  - Advisor: Prof. Wen-Syan Li

<img src="https://gsds.snu.ac.kr/sites/gsds.snu.ac.kr/files/GSDS_logo.png" width="300" height="100">