Bertweet¶

Overview¶

The BERTweet model was proposed in BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.

The abstract from the paper is the following:

We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification.

Example of use:

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 :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-base")

The original code can be found here.

Bertweet¶

Overview¶

BertweetTokenizer¶