Table of contents
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 installtransformers
from source.
Note that we merged a slow tokenizer for BERTweet into the maintransformers
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 installtransformers
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
andvinai/bertweet-covid19-base-uncased
are resulted by further pre-training the pre-trained modelvinai/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. Neweremoji
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 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
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The above copyright notice and this permission notice shall be included in all
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SOFTWARE.