GH-351: Release v1.3.1a0 (#377)

undertheseanlp · Dec 25, 2020 · a82be5a · a82be5a
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diff --git a/HISTORY.rst b/HISTORY.rst
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 History
 ================================================================================
 
+Current
+--------------------------------------------------------------------------------
+
+* [Funny Update] Change underthesea's avatar (GH-371)
+
 1.3.0 (2020-12-11)
 --------------------------------------------------------------------------------
 

diff --git a/README.md b/README.md
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+<p align="center">
+  <br>
+  <img src="logo.png"/>
+  <br/>
+</p>
+
+<p align="center">
+  <a href="https://pypi.python.org/pypi/underthesea">
+    <img src="https://img.shields.io/pypi/v/underthesea.svg">
+  </a>
+  <a href="https://pypi.python.org/pypi/underthesea">
+    <img src="https://img.shields.io/pypi/pyversions/underthesea.svg">
+  </a>
+  <a href="https://travis-ci.org/undertheseanlp/underthesea">
+    <img src="https://img.shields.io/travis/undertheseanlp/underthesea.svg">
+  </a>
+  <a href="http://underthesea.readthedocs.io/en/latest/">
+    <img src="https://readthedocs.org/projects/underthesea/badge/?version=latest">
+  </a>
+  <a href="http://undertheseanlp.com/">
+    <img src="https://img.shields.io/badge/website-online-brightgreen">
+  </a>
+  <a href="https://colab.research.google.com/drive/1gD8dSMSE_uNacW4qJ-NSnvRT85xo9ZY2">
+    <img src="https://img.shields.io/badge/colab-latest-ff9f01">
+  </a>
+  <a href="https://colab.research.google.com/drive/1U6EWY7ewNUtCXGsa5uZtDEz4I5exO_fo">
+    <img src="https://img.shields.io/badge/colab-stable-ff9f01">
+  </a>
+  <a href="https://www.facebook.com/undertheseanlp/">
+    <img src="https://img.shields.io/badge/Facebook-1877F2?logo=facebook&logoColor=white">
+  </a>
+  <a href="https://www.youtube.com/channel/UC9Jv1Qg49uprg6SjkyAqs9A">
+    <img src="https://img.shields.io/badge/YouTube-FF0000?logo=youtube&logoColor=white">
+  </a>
+</p>
+
+<h3 align="center">
+Open-source Vietnamese Natural Language Process Toolkit
+</h3>
+
+`Underthesea` is:
+
+🌊 **A Vietnamese NLP toolkit.** Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in [Vietnamese Natural Language Processing](https://github.com/undertheseanlp/underthesea). We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
+
+🌊 **A Pytorch library.** Underthesea is backed by one of most popular deep learning libraries, [Pytorch](https://pytorch.org/), make it easy to train your deep learning models and experiment with new approaches using Underthesea modules and classes.
+
+🌊 **An open-source software.** Underthesea is published under the [GNU General Public License v3.0](https://github.com/undertheseanlp/underthesea/blob/master/LICENSE) license. Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license.
+
+💫 **Version 1.3.0 out now!** [Underthesea meet deep learning!](https://github.com/undertheseanlp/underthesea/issues/359)
+
+## Installation
+
+
+To install underthesea, simply:
+
+```bash
+$ pip install underthesea
+✨🍰✨
+```
+
+Satisfaction, guaranteed.
+
+## Tutorials
+
+* [1. Sentence Segmentation](#1-sentence-segmentation)
+* [2. Word Segmentation](#2-word-segmentation)
+* [3. POS Tagging](#3-pos-tagging)
+* [4. Chunking](#4-chunking)
+* [5. Dependency Parsing](#5-dependency-parsing)
+* [6. Named Entity Recognition](#6-named-entity-recognition)
+* [7. Text Classification](#7-text-classification)
+* [8. Sentiment Analysis](#8-sentiment-analysis)
+* [9. Vietnamese NLP Resources](#9-vietnamese-nlp-resources)
+
+### 1. Sentence Segmentation
+
+Usage
+
+```python
+>>> from underthesea import sent_tokenize
+>>> text = 'Taylor cho biết lúc đầu cô cảm thấy ngại với cô bạn thân Amanda nhưng rồi mọi thứ trôi qua nhanh chóng. Amanda cũng thoải mái với mối quan hệ này.'
+
+>>> sent_tokenize(text)
+[
+  "Taylor cho biết lúc đầu cô cảm thấy ngại với cô bạn thân Amanda nhưng rồi mọi thứ trôi qua nhanh chóng.",
+  "Amanda cũng thoải mái với mối quan hệ này."
+]
+```
+
+### 2. Word Segmentation
+
+Usage
+
+```python
+>>> from underthesea import word_tokenize
+>>> sentence = 'Chàng trai 9X Quảng Trị khởi nghiệp từ nấm sò'
+
+>>> word_tokenize(sentence)
+['Chàng trai', '9X', 'Quảng Trị', 'khởi nghiệp', 'từ', 'nấm', 'sò']
+
+>>> word_tokenize(sentence, format="text")
+'Chàng_trai 9X Quảng_Trị khởi_nghiệp từ nấm sò'
+```
+
+### 3. POS Tagging
+
+
+Usage
+
+```python
+>>> from underthesea import pos_tag
+>>> pos_tag('Chợ thịt chó nổi tiếng ở Sài Gòn bị truy quét')
+[('Chợ', 'N'),
+ ('thịt', 'N'),
+ ('chó', 'N'),
+ ('nổi tiếng', 'A'),
+ ('ở', 'E'),
+ ('Sài Gòn', 'Np'),
+ ('bị', 'V'),
+ ('truy quét', 'V')]
+```
+
+
+### 4. Chunking
+
+
+Usage
+
+```python
+>>> from underthesea import chunk
+>>> text = 'Bác sĩ bây giờ có thể thản nhiên báo tin bệnh nhân bị ung thư?'
+>>> chunk(text)
+[('Bác sĩ', 'N', 'B-NP'),
+ ('bây giờ', 'P', 'I-NP'),
+ ('có thể', 'R', 'B-VP'),
+ ('thản nhiên', 'V', 'I-VP'),
+ ('báo tin', 'N', 'B-NP'),
+ ('bệnh nhân', 'N', 'I-NP'),
+ ('bị', 'V', 'B-VP'),
+ ('ung thư', 'N', 'I-VP'),
+ ('?', 'CH', 'O')]
+```
+
+
+### 5. Dependency Parsing
+
+
+Usage
+
+```python
+>>> from underthesea import dependency_parse
+>>> text = 'Tối 29/11, Việt Nam thêm 2 ca mắc Covid-19'
+>>> dependency_parse(text)
+[('Tối', 5, 'obl:tmod'),
+ ('29/11', 1, 'flat:date'),
+ (',', 1, 'punct'),
+ ('Việt Nam', 5, 'nsubj'),
+ ('thêm', 0, 'root'),
+ ('2', 7, 'nummod'),
+ ('ca', 5, 'obj'),
+ ('mắc', 7, 'nmod'),
+ ('Covid-19', 8, 'nummod')]
+```
+
+
+### 6. Named Entity Recognition
+
+
+Usage
+
+```python
+>>> from underthesea import ner
+>>> text = 'Chưa tiết lộ lịch trình tới Việt Nam của Tổng thống Mỹ Donald Trump'
+>>> ner(text)
+[('Chưa', 'R', 'O', 'O'),
+ ('tiết lộ', 'V', 'B-VP', 'O'),
+ ('lịch trình', 'V', 'B-VP', 'O'),
+ ('tới', 'E', 'B-PP', 'O'),
+ ('Việt Nam', 'Np', 'B-NP', 'B-LOC'),
+ ('của', 'E', 'B-PP', 'O'),
+ ('Tổng thống', 'N', 'B-NP', 'O'),
+ ('Mỹ', 'Np', 'B-NP', 'B-LOC'),
+ ('Donald', 'Np', 'B-NP', 'B-PER'),
+ ('Trump', 'Np', 'B-NP', 'I-PER')]
+```
+
+### 7. Text Classification
+
+
+Download models
+
+```bash
+$ underthesea download-model TC_GENERAL
+$ underthesea download-model TC_BANK
+```
+
+Usage
+
+```python
+>>> from underthesea import classify
+
+>>> classify('HLV đầu tiên ở Premier League bị sa thải sau 4 vòng đấu')
+['The thao']
+
+>>> classify('Hội đồng tư vấn kinh doanh Asean vinh danh giải thưởng quốc tế')
+['Kinh doanh']
+
+>> classify('Lãi suất từ BIDV rất ưu đãi', domain='bank')
+['INTEREST_RATE']
+```
+
+
+### 8. Sentiment Analysis
+
+Download models
+
+```bash
+$ underthesea download-model SA_GENERAL
+$ underthesea download-model SA_BANK
+```
+
+Usage
+
+
+```python
+>>> from underthesea import sentiment
+
+>>> sentiment('hàng kém chất lg,chăn đắp lên dính lông lá khắp người. thất vọng')
+negative
+>>> sentiment('Sản phẩm hơi nhỏ so với tưởng tượng nhưng chất lượng tốt, đóng gói cẩn thận.')
+positive
+
+>>> sentiment('Đky qua đường link ở bài viết này từ thứ 6 mà giờ chưa thấy ai lhe hết', domain='bank')
+['CUSTOMER_SUPPORT#negative']
+>>> sentiment('Xem lại vẫn thấy xúc động và tự hào về BIDV của mình', domain='bank')
+['TRADEMARK#positive']
+```
+
+### 9. Vietnamese NLP Resources
+
+List resources
+
+```bash
+$ underthesea list-data
+| Name         | Type        | License   |   Year | Directory             |
+|--------------+-------------+-----------+--------+-----------------------|
+| UTS2017-BANK | Categorized | Open      |   2017 | datasets/UTS2017-BANK |
+| VNESES       | Plaintext   | Open      |   2012 | datasets/LTA          |
+| VNTQ_BIG     | Plaintext   | Open      |   2012 | datasets/LTA          |
+| VNTQ_SMALL   | Plaintext   | Open      |   2012 | datasets/LTA          |
+| VNTC         | Categorized | Open      |   2007 | datasets/VNTC         |
+
+$ underthesea list-data --all
+```
+
+Download resources
+
+```bash
+$ underthesea download-data VNTC
+100%|██████████| 74846806/74846806 [00:09<00:00, 8243779.16B/s]
+Resource VNTC is downloaded in ~/.underthesea/datasets/VNTC folder
+```
+
+### Up Coming Features
+
+* Machine Translation
+* Text to Speech
+* Automatic Speech Recognition
+
+### Contributing
+
+Do you want to contribute with underthesea development? Great! Please read more details at [CONTRIBUTING.rst](https://github.com/undertheseanlp/underthesea/blob/master/CONTRIBUTING.rst)