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vaccinating-covid-tweets

@@ -13,7 +13,7 @@ widget:
 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
@@ -45,14 +45,14 @@ pipe(seq)
   ]
 ```
-- 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."
 ```
@@ -82,9 +82,9 @@ To conservatively classify whether an input sequence is true or not, the model m
 - 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 |

 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
   ]
 ```
+- `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."
 ```
 - 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 |