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    Copyright 2020 The HuggingFace Team. All rights reserved.

    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
    the License. You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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Bertweet
-----------------------------------------------------------------------------------------------------------------------

Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The BERTweet model was proposed in `BERTweet: A pre-trained language model for English Tweets
<https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf>`__ 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:

.. code-block::

    >>> 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 <https://github.com/VinAIResearch/BERTweet>`__.

BertweetTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.BertweetTokenizer
    :members: 
