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.
For further information or requests, please go to BERTweet's homepage!
Main results
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")
# 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")
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.
For "vinai/bertweet-large
", given the raw input Tweets, to obtain the same pre-processing output, users could employ our TweetNormalizer module.
- Installation:
pip3 install nltk emoji
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)])