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# 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 ## 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. 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") 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 ## 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). [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).