Table of contents

1. Introduction
2. Main results
3. Using BERTweet with transformers
   • Pre-trained models
   • Example usage
   • Normalize raw input Tweets
4. Using BERTweet with fairseq

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 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:

@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.

Main results

      

      

Using BERTweet with transformers

Installation

• Install transformers with pip: pip install transformers, or install transformers from source.
  Note that we merged a slow tokenizer for BERTweet into the main transformers branch. The process of merging a fast tokenizer for BERTweet is in the discussion, as mentioned in this pull request. If users would like to utilize the fast tokenizer, the users might install transformers as follows:

git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git
cd transformers
pip3 install -e .

• Install tokenizers with pip: pip3 install tokenizers

Pre-trained models

Model	#params	Arch.	Max length	Pre-training data
vinai/bertweet-base	135M	base	128	850M English Tweets (cased)
vinai/bertweet-covid19-base-cased	135M	base	128	23M COVID-19 English Tweets (cased)
vinai/bertweet-covid19-base-uncased	135M	base	128	23M COVID-19 English Tweets (uncased)
vinai/bertweet-large	355M	large	512	873M English Tweets (cased)

• 09/2020: 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.
• 08/2021: Released vinai/bertweet-large.

Example usage

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 = "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-large")

Normalize raw input Tweets

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.

Given the raw input Tweets, to obtain the same pre-processing output, users could employ our TweetNormalizer module.

• Installation: pip3 install nltk emoji==0.6.0
• The emoji version must be either 0.5.4 or 0.6.0. Newer emoji versions have been updated to newer versions of the Emoji Charts, thus not consistent with the one used for pre-processing our pre-training Tweet corpus.

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)])

Using BERTweet with fairseq

Please see details at HERE!

License

MIT License

Copyright (c) 2020-2021 VinAI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.