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# <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)
Two pre-trained models `vinai/bertweet-covid19-base-cased` and `vinai/bertweet-covid19-base-uncased` are resulted by further pre-training the pre-trained model `vinai/bertweet-base` on a corpus of 23M COVID-19 English Tweets.
### <a name="usage2"></a> Example usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
bertweet = AutoModel.from_pretrained("vinai/bertweet-covid19-base-uncased")
# For transformers v4.x+:
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", use_fast=False)
# For transformers v3.x:
# tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased")
# INPUT TWEET IS ALREADY NORMALIZED!
line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :crying_face:"
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-covid19-base-uncased")
```
### <a name="preprocess"></a> Normalize raw input Tweets
Before applying `fastBPE` to the pre-training corpus of 850M 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. BERTweet provides this pre-processing step by enabling the `normalization` argument. This argument currently only supports models "`vinai/bertweet-base`", "`vinai/bertweet-covid19-base-cased`" and "`vinai/bertweet-covid19-base-uncased`".
- Install `emoji`: `pip3 install emoji`
```python
import torch
from transformers import AutoTokenizer
# Load the AutoTokenizer with a normalization mode if the input Tweet is raw
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", normalization=True)
# from transformers import BertweetTokenizer
# tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", normalization=True)
line = "SC has first two presumptive cases of coronavirus, 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)])
```
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