Update README.md (#7459)

model_cards/digitalepidemiologylab/covid-twitter-bert/README.md

-# COVID-Twitter-BERT (CT-BERT)
-BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19
+---
+language: "en"
+thumbnail: "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"
+tags:
+- Twitter
+- COVID-19
+license: "MIT"
+---
+
+# COVID-Twitter-BERT (CT-BERT) v1
+
+:warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) which was trained on more recent data and yields better performance_ :warning: 
+
+
+BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
 
 ## Overview
 This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.
@@ -14,5 +27,25 @@ tokenizer = AutoTokenizer.from_pretrained("digitalepidemiologylab/covid-twitter-
 model = AutoModel.from_pretrained("digitalepidemiologylab/covid-twitter-bert")
 ```
 
+You can also use the model with the `pipeline` interface:
+
+```python
+from transformers import pipeline
+import json
+
+pipe = pipeline(task='fill-mask', model='digitalepidemiologylab/covid-twitter-bert-v2')
+out = pipe(f"In places with a lot of people, it's a good idea to wear a {pipe.tokenizer.mask_token}")
+print(json.dumps(out, indent=4))
+[
+    {   
+        "sequence": "[CLS] in places with a lot of people, it's a good idea to wear a mask [SEP]",
+        "score": 0.9959408044815063,
+        "token": 7308,
+        "token_str": "mask"
+    },  
+    ... 
+]
+```
+
 ## References
 [1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).