Create README.md

README.md

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+#### Table of contents
+1. [Introduction](#introduction)
+2. [Using PhoBERT with `transformers`](#transformers)
+	- [Installation](#install2)
+	- [Pre-trained models](#models2)
+	- [Example usage](#usage2)
+3. [Using PhoBERT with `fairseq`](#fairseq)
+4. [Notes](#vncorenlp)
+
+# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese 
+
+Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): 
+
+ - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md)  which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
+ - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
+
+The general architecture and experimental results of PhoBERT can be found in our [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.92/):
+
+    @inproceedings{phobert,
+    title     = {{PhoBERT: Pre-trained language models for Vietnamese}},
+    author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},
+    booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
+    year      = {2020},
+    pages     = {1037--1042}
+    }
+
+**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
+
+## <a name="transformers"></a> Using PhoBERT with `transformers` 
+
+### Installation <a name="install2"></a>
+- Install `transformers` with pip: `pip install transformers`, or [install `transformers` from source](https://huggingface.co/docs/transformers/installation#installing-from-source).  <br /> 
+Note that we merged a slow tokenizer for PhoBERT into the main `transformers` branch. The process of merging a fast tokenizer for PhoBERT is in the discussion, as mentioned in [this pull request](https://github.com/huggingface/transformers/pull/17254#issuecomment-1133932067). If users would like to utilize the fast tokenizer, the users might install `transformers` as follows:
+
+
+```
+git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git
+cd transformers
+pip3 install -e .
+```
+
+- Install `tokenizers` with pip: `pip3 install tokenizers`
+
+### Pre-trained models <a name="models2"></a>
+
+
+Model | #params | Arch.	 | Max length | Pre-training data
+---|---|---|---|---
+`vinai/phobert-base` | 135M | base | 256 | 20GB  of Wikipedia and News texts
+`vinai/phobert-large` | 370M | large | 256 | 20GB  of Wikipedia and News texts
+`vinai/phobert-base-v2` | 135M | base | 256 | 20GB  of Wikipedia and News texts + 120GB of texts from OSCAR-2301
+
+### Example usage <a name="usage2"></a>
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+phobert = AutoModel.from_pretrained("vinai/phobert-base-v2")
+tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2")
+
+# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
+sentence = 'Chúng_tôi là những nghiên_cứu_viên .'  
+
+input_ids = torch.tensor([tokenizer.encode(sentence)])
+
+with torch.no_grad():
+    features = phobert(input_ids)  # Models outputs are now tuples
+
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
+```
+
+
+## <a name="fairseq"></a> Using PhoBERT with `fairseq`
+
+Please see details at [HERE](https://github.com/VinAIResearch/PhoBERT/blob/master/README_fairseq.md)!
+
+## <a name="vncorenlp"></a> Notes 
+
+In case the input texts are `raw`, i.e. without word segmentation, a word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT. As PhoBERT employed the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data (including [Vietnamese tone normalization](https://github.com/VinAIResearch/BARTpho/blob/main/VietnameseToneNormalization.md) and word and sentence segmentation), it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts.
+
+#### Installation
+
+    pip install py_vncorenlp
+
+#### Example usage <a name="example"></a>
+
+```python
+import py_vncorenlp
+
+# Automatically download VnCoreNLP components from the original repository
+# and save them in some local machine folder
+py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp')
+
+# Load the word and sentence segmentation component
+rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp')
+
+text = "Ông Nguyễn Khắc Chúc  đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."
+
+output = rdrsegmenter.word_segment(text)
+
+print(output)
+# ['Ông Nguyễn_Khắc_Chúc đang làm_việc tại Đại_học Quốc_gia Hà_Nội .', 'Bà Lan , vợ ông Chúc , cũng làm_việc tại đây .']
+```
+
+## License
+    
+	MIT License
+
+	Copyright (c) 2020 VinAI Research
+
+	Permission is hereby granted, free of charge, to any person obtaining a copy
+	of this software and associated documentation files (the "Software"), to deal
+	in the Software without restriction, including without limitation the rights
+	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+	copies of the Software, and to permit persons to whom the Software is
+	furnished to do so, subject to the following conditions:
+
+	The above copyright notice and this permission notice shall be included in all
+	copies or substantial portions of the Software.
+
+	THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+	IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+	FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+	AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+	LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+	OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+	SOFTWARE.