Update README.md

README.md

@@ -65,4 +65,18 @@ with torch.no_grad():
 
 Before applying BPE to the pre-training corpus of English Tweets, we tokenized these  Tweets using `TweetTokenizer` from the NLTK toolkit and used the `emoji` package to translate emotion icons into text strings (here, each icon is referred to as a word token).   We also normalized the Tweets by converting user mentions and web/url links into special tokens `@USER` and `HTTPURL`, respectively. Thus it is recommended to also apply the same pre-processing step for BERTweet-based downstream applications w.r.t. the raw input Tweets. 
 
-Please find examples of normalizing raw input Tweets at [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet#preprocess)!
+For "`vinai/bertweet-large`", given the raw input Tweets, to obtain the same pre-processing output, users could employ our  [TweetNormalizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) module.
+
+- Installation: `pip3 install nltk emoji`
+
+```python
+import torch
+from transformers import AutoTokenizer
+from TweetNormalizer import normalizeTweet
+
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large")
+
+line = normalizeTweet("DHEC confirms https://postandcourier.com/health/covid19/sc-has-first-two-presumptive-cases-of-coronavirus-dhec-confirms/article_bddfe4ae-5fd3-11ea-9ce4-5f495366cee6.html?utm_medium=social&utm_source=twitter&utm_campaign=user-share… via @postandcourier 😢")
+
+input_ids = torch.tensor([tokenizer.encode(line)])
+```