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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.

Using BERTweet in fairseq

Installation

  • Python version >= 3.6
  • PyTorch version >= 1.4.0
  • fairseq
  • fastBPE: pip3 install fastBPE

Pre-trained model

Model #params size Download
BERTweet-base 135M 1.2GB BERTweet_base_fairseq.tar.gz (md5sum 692cd647e630c9f5de5d3a6ccfea6eb2)
  • wget https://public.vinai.io/BERTweet_base_fairseq.tar.gz
  • tar -xzvf BERTweet_base_fairseq.tar.gz

Example usage

import torch

# Load BERTweet-base in fairseq
from fairseq.models.roberta import RobertaModel
bpe_codes_file = '/Absolute-path-to/BERTweet_base_fairseq/bpe.codes'
BERTweet = RobertaModel.from_pretrained('/Absolute-path-to/BERTweet_base_fairseq', checkpoint_file='model.pt', bpe='fastbpe', bpe_codes=bpe_codes_file).eval()

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

# Extract the last layer's features  
subwords = BERTweet.encode(line)  
last_layer_features = BERTweet.extract_features(subwords)  
assert last_layer_features.size() == torch.Size([1, 21, 768])  
  
# Extract all layer's features (layer 0 is the embedding layer)  
all_layers = BERTweet.extract_features(subwords, return_all_hiddens=True)  
assert len(all_layers) == 13  
assert torch.all(all_layers[-1] == last_layer_features)  

# Filling marks  
masked_line = 'SC has first two presumptive cases of  <mask> , DHEC confirms HTTPURL via @USER :cry:'  
topk_filled_outputs = BERTweet.fill_mask(masked_line, topk=5)  
for candidate in topk_filled_outputs:  
    print(candidate)
    # ('SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:', 0.8643638491630554, 'coronavirus')
    # ('SC has first two presumptive cases of Coronavirus , DHEC confirms HTTPURL via @USER :cry:', 0.04520644247531891, 'Coronavirus')
    # ('SC has first two presumptive cases of #coronavirus , DHEC confirms HTTPURL via @USER :cry:', 0.035870883613824844, '#coronavirus')
    # ('SC has first two presumptive cases of #COVID19 , DHEC confirms HTTPURL via @USER :cry:', 0.029708299785852432, '#COVID19')
    # ('SC has first two presumptive cases of #Coronavirus , DHEC confirms HTTPURL via @USER :cry:', 0.005226477049291134, '#Coronavirus')

License

MIT License

Copyright (c) 2020 VinAI Research

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.