Update README.md

README.md

@@ -72,20 +72,19 @@ To conservatively classify whether an input sequence is true or not, the model m
   - 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
+- 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
+- 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, unverified
+  - `Misleading`: includes misleading, exaggerated, out of context, needs context
+  - `True`: includes true, correct
 
 ## Evaluation results
 | Training loss | Validation loss | Training accuracy | Validation accuracy |
@@ -93,7 +92,7 @@ To conservatively classify whether an input sequence is true or not, the model m
 | 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
+- 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