# <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets 

BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md)  pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the **COVID-19** pandemic. The general architecture and experimental results of BERTweet can be found in our [paper](https://aclanthology.org/2020.emnlp-demos.2/):

    @inproceedings{bertweet,
    title     = {{BERTweet: A pre-trained language model for English Tweets}},
    author    = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen},
    booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
    pages     = {9--14},
    year      = {2020}
    }

**Please CITE** our paper when BERTweet is used to help produce published results or is incorporated into other software.

For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)!

### <a name="models2"></a> Pre-trained models 


Model | #params | Arch. | Pre-training data
---|---|---|---
`vinai/bertweet-base` | 135M | base | 850M English Tweets (cased)
`vinai/bertweet-covid19-base-cased` | 135M | base | 23M COVID-19 English Tweets (cased)
`vinai/bertweet-covid19-base-uncased` | 135M | base | 23M COVID-19 English Tweets (uncased)
`vinai/bertweet-large` | 355M | large | 873M English Tweets (cased)


### <a name="usage2"></a> Example usage 


```python
import torch
from transformers import AutoModel, AutoTokenizer 

bertweet = AutoModel.from_pretrained("vinai/bertweet-large")
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large")

# INPUT TWEET IS ALREADY NORMALIZED!
line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"

input_ids = torch.tensor([tokenizer.encode(line)])

with torch.no_grad():
    features = bertweet(input_ids)  # Models outputs are now tuples
    
## With TensorFlow 2.0+:
# from transformers import TFAutoModel
# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-large")
```