66 lines
2.6 KiB
Markdown
66 lines
2.6 KiB
Markdown
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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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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
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*This model was released on 2020-03-02 and added to Hugging Face Transformers on 2020-11-16.*
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# PhoBERT
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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The PhoBERT model was proposed in [PhoBERT: Pre-trained language models for Vietnamese](https://huggingface.co/papers/2003.00744) 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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This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The original code can be found [here](https://github.com/VinAIResearch/PhoBERT).
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## Usage example
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```python
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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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```
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<Tip>
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PhoBERT implementation is the same as BERT, except for tokenization. Refer to [BERT documentation](bert) for information on
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configuration classes and their parameters. PhoBERT-specific tokenizer is documented below.
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</Tip>
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## PhobertTokenizer
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[[autodoc]] PhobertTokenizer
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