readme: ---
  language: en
  license: apache-2.0
  datasets:
  - tweets
  widget:
  - text: "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
  ---
  
  # 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
  You can classify if the input tweet (or any others statement) about COVID-19/vaccine is `true`, `false` or `misleading`.
  Note that since this model was trained with data up to May 2020, the most recent information may not be reflected.
  
  #### How to use
  You can use this model directly on this page or using `transformers` in python.
  
  - Load pipeline and implement with input sequence
  ```python
  from transformers import pipeline
  pipe = pipeline("sentiment-analysis", model = "ans/vaccinating-covid-tweets")
  seq = "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
  pipe(seq)
  ```
  
  - Expected output
  ```python
    [
      {
        "label": "false",
        "score": 0.07972867041826248
      },
      {
        "label": "misleading",
        "score": 0.019911376759409904
      },
      {
        "label": "true",
        "score": 0.9003599882125854
      }
    ]
  ```
  
  - `true` examples
  ```python
  "By the end of 2020, several vaccines had become available for use in different parts of the world."
  "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
  "RNA vaccines were the first vaccines for SARS-CoV-2 to be produced and represent an entirely new vaccine approach."
  ```
  
  - `false` examples
  ```python
  "COVID-19 vaccine caused new strain in UK."
  ```
  
  #### Limitations and bias
  To conservatively classify whether an input sequence is true or not, the model may have predictions biased toward `false` or `misleading`.
  
  ## 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 from pre-trained language model (1) for fact-classification task on COVID-19/vaccine.
  - COVID-19/vaccine-related statements were collected from [Poynter](https://www.poynter.org/ifcn-covid-19-misinformation/) and [Snopes](https://www.snopes.com/) using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021.
  - Original labels were divided within following three categories:
    - `False`: includes false, no evidence, manipulated, fake, not true, unproven and unverified
    - `Misleading`: includes misleading, exaggerated, out of context and needs context
    - `True`: includes true and correct
  
  ## Evaluation results
  | Training loss | Validation loss | Training accuracy | Validation accuracy |
  | --- | --- | --- | --- |
  | 0.1062 | 0.1006 | 96.3% | 94.5% |
  
  # Contributors
  - This model 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
  
  <a href="https://gsds.snu.ac.kr/"><img src="https://gsds.snu.ac.kr/wp-content/uploads/sites/50/2021/04/GSDS_logo2-e1619068952717.png" width="200" height="80"></a>