Update README.md

README.md

@@ -22,10 +22,9 @@ Alternatively, to run locally
 ```
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")
-
-model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-pretraining-covid-vaccine-tweets-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned")
 
+model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned")
 ```
 
 ## Model description
@@ -43,8 +42,19 @@ The tokenizer requires the emoji library to be installed.
 
 The intended use of this model is to detect if the contents of a covid tweet is potentially false or misleading. This model is not an end all be all. It has many limitations. For example, if someone makes a post containing an image, but has attached a satirical image, this model would not be able to distinguish this. If a user links a website, the tokenizer allocates a special token for links, meaning the contents of the linked website is completely lost. If someone tweets a reply, this model can't look at the parent tweets, and will lack context.
 
-This model's dataset relies on the crowd-sourcing annotations being accurate.
+This model's dataset relies on the crowd-sourcing annotations being accurate. This data is only accurate of up until early December 2021. For example, it probably wouldn't do very ell with tweets regarded the new omicron variant.
+
+Example true inputs:
+```
+Covid vaccines are safe and effective. -> 97% true
+Vaccinations are safe and help prevent covid. -> 97% true
+```
 
+Example false inputs:
+```
+Covid vaccines will kill you. -> 97% false
+covid vaccines make you infertile. -> 97% false
+```