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")

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.


class transformers.BertweetTokenizer(vocab_file, merges_file, normalization=False, bos_token='<s>', eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', **kwargs)[source]¶

Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

  • vocab_file (str) – Path to the vocabulary file.

  • merges_file (str) – Path to the merges file.

  • normalization (bool, optional, defaults to False) – Whether or not to apply a normalization preprocess.

  • bos_token (str, optional, defaults to "<s>") –

    The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.


    When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.

  • eos_token (str, optional, defaults to "</s>") –

    The end of sequence token.


    When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.

  • sep_token (str, optional, defaults to "</s>") – The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

  • cls_token (str, optional, defaults to "<s>") – The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

  • unk_token (str, optional, defaults to "<unk>") – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

  • pad_token (str, optional, defaults to "<pad>") – The token used for padding, for example when batching sequences of different lengths.

  • mask_token (str, optional, defaults to "<mask>") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.


Loads a pre-existing dictionary from a text file and adds its symbols to this instance.

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERTweet sequence has the following format:

  • single sequence: <s> X </s>

  • pair of sequences: <s> A </s></s> B </s>

  • token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.


List of input IDs with the appropriate special tokens.

Return type



Converts a sequence of tokens (string) in a single string.

create_token_type_ids_from_sequences(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

Create a mask from the two sequences passed to be used in a sequence-pair classification task. BERTweet does not make use of token type ids, therefore a list of zeros is returned.

  • token_ids_0 (List[int]) – List of IDs.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.


List of zeros.

Return type


get_special_tokens_mask(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int][source]¶

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

  • token_ids_0 (List[int]) – List of IDs.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

  • already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.


A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type



Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token is in the vocab.


The vocabulary.

Return type

Dict[str, int]


Normalize tokens in a Tweet


Normalize a raw Tweet

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]¶

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.


Paths to the files saved.

Return type


property vocab_size¶

Size of the base vocabulary (without the added tokens).