Update README.md

README.md

@@ -10,14 +10,16 @@ widget:
 # Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
 
 # Vaccinating COVID tweets
-Fine-tuned model on English language using a masked language modeling (MLM) objective from BERTweet in [this repository](https://github.com/VinAIResearch/BERTweet) for the classification task for factual information about COVID-19/vaccine.
+A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine.
 
 ## Intended uses & limitations
 
 #### How to use
 
 ```python
-# You can include sample code which will be formatted
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("ans/vaccinating-covid-tweets")
+model = AutoModelForSequenceClassification.from_pretrained("ans/vaccinating-covid-tweets")
 ```
 
 #### Limitations and bias
@@ -36,11 +38,15 @@ Provide examples of latent issues and potential remediations.
   - Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification
 
 #### 1) Pre-training language model
-- 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)
-
+- 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
@@ -56,4 +62,4 @@ Provide examples of latent issues and potential remediations.
   - Advisor: Prof. Wen-Syan Li
 
 # ![GSDS](https://gsds.snu.ac.kr/sites/gsds.snu.ac.kr/files/GSDS_logo.png)
-<img src="https://gsds.snu.ac.kr/sites/gsds.snu.ac.kr/files/GSDS_logo.png" width="100" height="100">
+<img src="https://gsds.snu.ac.kr/sites/gsds.snu.ac.kr/files/GSDS_logo.png" width="300" height="100">