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* first commit * change phobert to phoBERT as per author in overview * v3 and v4 both runs on same code hence there is no need to differentiate them Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
60 lines
2.4 KiB
ReStructuredText
60 lines
2.4 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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PhoBERT
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The PhoBERT model was proposed in `PhoBERT: Pre-trained language models for Vietnamese
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<https://www.aclweb.org/anthology/2020.findings-emnlp.92.pdf>`__ by Dat Quoc Nguyen, Anh Tuan Nguyen.
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The abstract from the paper is the following:
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*We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual
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language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent
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best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple
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Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and
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Natural language inference.*
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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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phobert = AutoModel.from_pretrained("vinai/phobert-base")
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tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
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# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
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line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
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input_ids = torch.tensor([tokenizer.encode(line)])
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with torch.no_grad():
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features = phobert(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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# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
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The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
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PhobertTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.PhobertTokenizer
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:members:
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