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

• BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure, using the same model configuration as BERT-base.
• 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.
• BERTweet does better than its competitors RoBERTa-base and XLM-R-base and outperforms previous state-of-the-art models on three downstream Tweet NLP tasks of Part-of-speech tagging, Named entity recognition and text classification.

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},
year      = {2020},
pages     = {9--14}
}

Please CITE our paper when BERTweet is used to help produce published results or is incorporated into other software.

• Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
• Install transformers:
  • git clone https://github.com/huggingface/transformers.git
  • cd transformers
  • pip3 install --upgrade .
• Install emoji: pip3 install emoji

As of 09/2020, we have collected a corpus of about 23M "cased" COVID-19 English Tweets, and also generate an "uncased" version of this corpus. Then we continue pre-training from vinai/bertweet-base on each of the "cased" and "uncased" corpora of 23M Tweets for 40 additional epochs, resulting in two BERTweet variants vinai/bertweet-covid19-base-cased and vinai/bertweet-covid19-base-uncased, respectively.

import torch
from transformers import AutoModel, AutoTokenizer 

bertweet = AutoModel.from_pretrained("vinai/bertweet-base")

# For transformers v4.x+: 
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)

# For transformers v3.x: 
# tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")

# INPUT TWEET IS ALREADY NORMALIZED!
line = "SC has first two presumptive cases of coronavirus , 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-base")

Before applying fastBPE to the pre-training corpus of 850M 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. BERTweet provides this pre-processing step by enabling the normalization argument.

import torch
from transformers import AutoTokenizer

# Load the AutoTokenizer with a normalization mode if the input Tweet is raw
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)

# from transformers import BertweetTokenizer
# tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)

line = "SC has first two presumptive cases of coronavirus, 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)])

Please see details at HERE!

MIT License

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