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:

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!

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)

Example usage

import torch
from transformers import AutoModel, AutoTokenizer 

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

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")
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