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* Add the pre-trained BARTpho model * Add the pre-trained BARTpho model * Add the pre-trained BARTpho model * Fix incorrectly sorted and/or formatted imports * Fix incorrectly sorted and/or formatted style * Fix check_dummies * Fix check_dummies * Fix check_dummies * Update docs/source/model_doc/bartpho.rst Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/bartpho/__init__.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/bartpho/tokenization_bartpho.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update tests/test_tokenization_bartpho.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update src/transformers/models/bartpho/tokenization_bartpho.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update tests/test_tokenization_bartpho.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update docs/source/model_doc/bartpho.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/model_doc/bartpho.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/bartpho/__init__.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Add the pre-trained BARTpho model * Add Tips section in doc and details of monolingual_vocab_file * Fix conflicts * Add another tip related to monolingual_vocab_file * Readd dependency_versions_table.py * Handle failing checks * Remove test_list.txt * Remove md5sum.saved * Revise Readme.md Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
65 lines
2.9 KiB
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65 lines
2.9 KiB
ReStructuredText
..
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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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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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This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
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<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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