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* bertweet docs coverage * style doc max len 119 * maxlen style rst * run main() from style_doc * changed according to comments
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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Bertweet
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The BERTweet model was proposed in `BERTweet: A pre-trained language model for English Tweets
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<https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf>`__ by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.
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The abstract from the paper is the following:
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*We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having
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the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et
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al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al.,
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2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks:
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Part-of-speech tagging, Named-entity recognition and text classification.*
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Example of use:
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.. code-block::
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import torch
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from transformers import AutoModel, AutoTokenizer
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bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
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# For transformers v4.x+:
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tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
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# For transformers v3.x:
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# tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
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# INPUT TWEET IS ALREADY NORMALIZED!
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line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
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input_ids = torch.tensor([tokenizer.encode(line)])
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with torch.no_grad():
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features = bertweet(input_ids) # Models outputs are now tuples
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## With TensorFlow 2.0+:
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# from transformers import TFAutoModel
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# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
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The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.
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BertweetTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertweetTokenizer
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:members:
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