DeepNLP AI APP Store

DeepNLP AI Store (http://www.deepnlp.org/store) is a newly released website to let users write genuine reviews, ratings, human evaluation, prompts and share use cases about detailed aspects of AI services from users' perspective (different from researchers' perspective like the LLM/MultiModal benchmarks). We want to build the "Yelp" for AI and Robotics community and ease the burden of customers choosing various AI services.

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Detailed AI Service Use Case

Users can write detailed reviews about some functions about an AI service (ChatGPT/Gemini/Perplexity/Midjourney/) like how AI is doing on "Correct Grammarly Mistakes in Essays", "Acting like doctors about Illness", or text-to-image ability like "Generate Cartoon Characters", "Draw Picture of Fantasy Humanoid", by uploading the screenshots of a conversation or the generated images of AI image generator.

Multi-Aspect Rating

Users can write review and give overall rating from 1 to 5 to each function of AI service, as well as give detailed rating of different aspects of each function, including "Correctness", "Helpfulness" and "Interesting", and customized aspects of each function, such as "Clarity of image", "Image Resolution", "Artistic", "Grammar", "Succintness" and more.

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All Categories and People Groups

We cover 30+ different categories of use cases, such as AI Image Generators, AI Assistant & Chatbot, AI Translator, AI Search Engine, AI for kids, AI for adults, AI for elderly, AI in TRAVEL, AI in TRANSPORTATION AI in HEALTHCARE, AI in BUSINESS, AI in FINANCE, AI in EDUCATION, AI in PRODUCTIVITY TOOL, AI in POLITICS, AI in ENTERTAINMENT, AI in NEWS, AI in ART AND SPORTS, AI in LIFESTYLE AI in PAYMENT, AI in SOCIAL, AI in AGRICULTURE AI in SCIENCE, AI in TECHNOLOGY, AI in TRAVEL, AI in TRANSPORTATION, AI in CAR, AI in CHARITY AI in PUBLIC SERVICE, AI in HOUSING, AI in LAW, AI in COMMUNICATION, AI in FOOD

AI Store of Image Generator

Best AI Tools in Image Generator User Ratings Reviews and Showcase

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DeepNLP AI store is a platform and community for user to write genuine user reviews and ratings of AI apps and services. User can upload pictures as showcase of AI Image Generator, such as Midjourney, Canva, Stable Diffusion, etc. People can write reviews about some common user prompts (questions or intents), such as "Generate Cartoon Characters", "Draw Fantacy and Humanoids", "Plot Architecture" and more.

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AI Image Generator ShowCases

AI Store in Law

Best AI in Law and Legal User Ratings Reviews and Showcase

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Unlike most Large Language Models(LLM) benchmark and arena's win-rate metric, which is quite difficult for customers to understand (1 vs 1 metric), users' reviews on DeepNLP AI store is about AI tool's performance in a detailed subfield of the industry, such as "Ask ChatGPT Employment Law Related Questions" and "Ask Gemini Contracts and Agreements Related Questions". Users can give rating from 1 to 5 stars on different aspects of the answeres generated by AI systems, including "Overall", "Correctness", "Helpfulness", "Interesting". They can also give rating to any customized aspect of the service, such as "whether the information is realtime", "generation speed", and many others. Here, we are going to cover different sub areas of AI in Law, includine Employment Law, Contracts and Agreements, Business and Corporate Law, Real Estate, Family Law, Personal Injury, Criminal Law, Immigration Law, and Civil Rights Law, etc.

Prompts (Questions) exmaples

You are a lawyer specialized in employment Law. I will consult you a few questions. And my questions include "Can my employer fire me without cause and how much compensation can I get if I live in California?".
What are my rights regarding workplace discrimination?
Act as an employment law expert and please answer this question "Can I fight for unjustified evaluation if I am placed on performance improvement plan?".

Employment Law

Best AI in Employment Law
ask-chatgpt-employment-law-related-questions
ask perplexity ai employment law related questions
ask gemini employment law related questions
ask claude employment law related questions

Contracts and Agreements

Best AI in Contracts and Agreements
Ask perplexity ai contracts and agreements related questions
Ask chatgpt contracts and agreements related questions
Ask gemini contracts and agreements related questions
Ask claude contracts and agreements related questions

Business and Corporate Law

Best AI in Business and Corporate Law ask gemini business and corporate law related questions
ask chatgpt business and corporate law related questions
ask perplexity ai business and corporate law related questions
ask claude business and corporate law related questions

Real Estate

Best AI in Real Estate Law ask-chatgpt-real-estate-related-questions
ask-perplexity-ai-real-estate-related-questions
ask-gemini-real-estate-related-questions
ask claude real estate related questions

Civil Rights Law

Best AI in Civil Rights Law
ask perplexity ai civil rights law related questions
ask gemini civil rights law related questions
ask chatgpt civil rights law related questions
ask claude civil rights law related questions

Family Law

Best AI in Family Law Ask chatgpt family law and related question
Ask claude family law related questions
Ask perplexity ai family law related questions
Ask gemini family law related questions

Personal Injury

Best AI in Personal Injury
Ask perplexity ai personal injury law and accidents related question
Ask chatgpt personal injury law and accidents related question
Ask claude personal injury law and accidents related question
Ask gemini personal injury law and accidents related question

Criminal Law

Best AI in Criminal Law Ask gemini criminal law related questions
Ask perplexity ai criminal law related questions
Ask chatgpt criminal law related questions
Ask claude criminal law related questions

Immigration Law

Best AI in immigration law Ask perplexity ai immigration law related questions
Ask gemini immigration law related questions
Ask chatgpt immigration law related questions
Ask gemini criminal law related questions

AI in Finance: User Ratings, Reviews and Showcase

Best AI in Finance User Ratings Reviews and Showcase

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Prompts (Questions)

You are an expert in stock investment, and I will consult you a few questions. The questions include "Is Tesla a buy stock? Should I sell Nvidia stock after financial report season?"
Please act as a trader. Please answer this question "Summarize Nvidia's GAAP Financial Report Q4 FY24 statistics and compare P/E ratio with other tech company such as google and apple"
Act as a financial analyst and "Please draw a bar chart comparing the market capital of Google, Apple, Tesla, Nvidia and Microsoft."

AI in Investment Finance

Best AI in Investment Finance
Ask chatgpt facts about investment and seek advice
Ask Gemini Facts about Investment and Seek Advice
Ask Claude INVESTMENT Related Questions
Ask Perplexity AI INVESTMENT Related Questions

AI in INSURANCE Finance

Best AI in Finance INSURANCE User Ratings Reviews and Showcase
Ask Gemini INSURANCE Related Questions
Ask Perplexity AI INSURANCE Related Questions
Ask ChatGPT INSURANCE Related Questions
Ask Claude INSURANCE Related Questions

AI in Mortgage and Loan Finance

Best AI in Mortgage Loan Finance
Ask ChatGPT MORTGAGE & LOAN Related Questions
Ask Gemini MORTGAGE & LOAN Related Questions
Ask Claude MORTGAGE & LOAN Related Questions
Ask Perplexity AI MORTGAGE & LOAN Related Questions

AI in BANKING Finance

Best AI in BANKING Finance
Ask Perplexity AI BANKING Related Questions
Ask Gemini BANKING Related Questions
Ask ChatGPT BANKING Related Questions
Ask Claude BANKING Related Questions

AI in DEBT Finance

Best AI in Debt Finance
Ask Claude DEBT Related Questions
Ask ChatGPT DEBT Related Questions
Ask Gemini DEBT Related Questions
Ask Perplexity AI DEBT Related Questions

AI in Healthcare

Best AI in Healthcare User Ratings Reviews and Showcase

HOSPITAL APPOINTMENT

Best AI in HOSPITAL APPOINTMENT
Ask Gemini HOSPITAL APPOINTMENT Related Questions
Ask ChatGPT HOSPITAL APPOINTMENT Related Questions
Ask Perplexity AI HOSPITAL APPOINTMENT Related Questions
Ask Claude HOSPITAL APPOINTMENT Related Questions

HOSPITAL

Best AI in Hospital Healthcare

AI in ILLNESS

Best AI in ILLNESS Healthcare
Seeing Doctors about Illness
Ask Gemini Food to Avoid About Illness
Ask Perplexity AI ILLNESS Related Questions
Ask ChatGPT ILLNESS Related Questions
Ask Claude ILLNESS Related Questions

MEDICINE

Best AI in Medicine Healthcare
Ask ChatGPT MEDICINE Related Questions
Ask Perplexity AI MEDICINE Related Questions
Ask Gemini MEDICINE Related Questions
Ask Claude MEDICINE Related Questions

NURSING

Best AI in NURSING Healthcare
Ask Gemini NURSING Related Questions
Ask Claude NURSING Related Questions
Ask Perplexity AI NURSING Related Questions
Ask ChatGPT NURSING Related Questions

BEAUTY

Best AI in BEAUTY Healthcare
Ask Gemini NURSING Related Questions
Ask Claude NURSING Related Questions
Ask Perplexity AI NURSING Related Questions
Ask ChatGPT NURSING Related Questions

ELDERLY CARE

Best AI in ELDERLY CARE
Ask Perplexity AI ELDERLY CARE Related Questions
Ask Gemini ELDERLY CARE Related Questions
Ask ChatGPT ELDERLY CARE Related Questions
Ask Claude ELDERLY CARE Related Questions

MEDICAL INSTRUMENT

Best AI in MEDICAL INSTRUMENT
Ask ChatGPT MEDICAL INSTRUMENT Related Questions
Ask Claude MEDICAL INSTRUMENT Related Questions
Ask Perplexity AI MEDICAL INSTRUMENT Related Questions
Ask Gemini MEDICAL INSTRUMENT Related Questions

AI for Elderly: User Ratings, Reviews and Showcase

Best AI for Elderly User Ratings, Reviews and Showcase

Prompts (Questions) exmaples What are the side effects of [medication]? How can I manage [illness]?

e.g. What are the side effects of Tylenol? How can I manage arthritis pain?

ShowCases

Elderly People Ask Gemini Personal Hobbies Related Questions
Elderly People Ask Gemini Legal Questions
Elderly People Ask Gemini Health and Medicine Related Questions
Elderly People Ask ChatGPT Health and Medicine Related Questions
Elderly People Ask Perplexity AI Health and Medicine Related Questions
Elderly People Ask Claude Health and Medicine Related Questions

AI for Kids: User Ratings, Reviews and Showcase

Best AI for Kids User Ratings, Reviews and Showcase

Prompts (Questions) exmaples

STORY TELLING for KIDS

Please tell me a bedtime story about unicorns for my 4 years old daughter.
Help me write a story about Paw Patrol Dogs Fighting Bad Guys

DRAWING|AIGC

Help me Draw a picture about Paw Patrol Dogs Chase Driving Police Car
Help me Draw a picture Peppa Pig playing in the swimming pool

ShowCases Ask Gemini to Tell a Bedtime Story Ask Gemini to Draw Cartoon Characters Ask Gemini to Tell a Bedtime Story Ask ChatGPT to Draw Cartoon Character

AI in LifeStyle

Best AI in LifeStyle User Ratings, Reviews and Showcase

Prompts (Questions) exmaples

Act as ${role}

I want you to respond and answer like ${role} using the tone, manner that ${role} would use. Do not write any explanations. My first sentence is ${your_question}.

ShowCases

Ask ChatGPT to Act As Lovers in a Relationship
Ask Gemini to Act As Lovers in a Relationship
Ask Character AI to Act As Lovers in a Relationship
Ask Doubao to Act As Lovers in a Relationship
Ask Qwen AI from Alibaba to Act As Lovers in a Relationship
Ask Zhipu AI to Act As Lovers in a Relationship

AI in Productivity Tool

Best AI in Productivity Tool User Ratings, Reviews and Showcase

image text

Writing Tool

Best AI in Writing Tool

Research Analysis

Best AI in Research & Analysis

Programming

Best AI in Programming

Prompts: Show me the ${programming language} code of ${algorithm}, no explanation. Write the ${programming language} to implement ${function}, no explanation. Generate the ${programming language} code of a ${description} website, no explanation. Find the ${statistic} from ${data_source} and display in ${format}.

e.g.

Show me the latex code of KL Divergence
Write the python code for QuickSort Implementation
Generate the html code of a login page of a community website, no explanation.
Find the 2023 divorce rate of all states in US and display in the table and line chart.

ShowCase

Programming

Gemini for Programming ChatGPT for Programming

Plot Chat

Use ChatGPT to Draw Plot of Math Function

Data Analysis

Use Gemini to Conduct Data Analysis

======================================================================================================================================================================================================

By End of 2020, deepnlp lib is archived and only support TF to 1.13

Deep Learning NLP Pipeline implemented on Tensorflow. Following the 'simplicity' rule, this project aims to use the deep learning library of Tensorflow to implement new NLP pipeline. You can extend the project to train models with your own corpus/languages. Pretrained models of Chinese corpus are distributed. Free RESTful NLP API are also provided. Visit http://www.deepnlp.org/api/v1.0/pipeline for details.

Brief Introduction

Modules
Installation
Tutorial
- Segmentation
- POS
- NER
- Parsing
- Pipeline
- Textsum
- Textrank
- Textcnn
- Train your model
- Web API Service
中文简介
安装说明
Reference

Modules

NLP Pipeline Modules:
- Word Segmentation/Tokenization
- Part-of-speech (POS)
- Named-entity-recognition(NER)
- Dependency Parsing (Parse)
- textsum: automatic summarization Seq2Seq-Attention models
- textrank: extract the most important sentences
- textcnn: document classification
- Web API: Free Tensorflow empowered web API
- Planed: Automatic Summarization
Algorithm(Closely following the state-of-Art)
- Word Segmentation: Linear Chain CRF(conditional-random-field), based on python CRF++ module
- POS: LSTM/BI-LSTM/LSTM-CRF network, based on Tensorflow
- NER: LSTM/BI-LSTM/LSTM-CRF network, based on Tensorflow
- Parse: Arc-Standard System with Feed Forward Neural Network
- Textsum: Seq2Seq with attention mechanism
- Texncnn: CNN
Pre-trained Model
- Chinese: Segmentation, POS, NER, Parse (1998 china daily corpus)
- Domain Specific NER Models are also provided: general, entertainment, o2o, etc... Contribution are welcome
- English: POS (brown corpus)
- For your Specific Language, you can easily use the script to train model with the corpus of your language choice.

Installation

Requirements
- CRF++ (>=0.54)
- Tensorflow(1.4)
- Python (python2.7 and python3.6 are tested) This project is up to date with the latest tensorflow release. For tensorflow (<=0.12.0), use deepnlp <=0.1.5 version. tensorflow (1.0-1.3), use deepnlp = 0.1.6 version tensorflow (1.4), use deepnlp = 0.1.7 version See RELEASE.md for more details
Pip
```
# linux, run the script:
pip install deepnlp
```
Due to pkg size restriction, english pos model, ner domain specific model files are not distributed on pypi You can download the pre-trained model files from github and put in your installation directory .../site-packages/.../deepnlp/... model files: ../pos/ckpt/en/pos.ckpt ; ../ner/ckpt/zh/ner.ckpt
Source Distribution, e.g. deepnlp-0.1.7.tar.gz: https://pypi.python.org/pypi/deepnlp

    # linux, run the script:
    tar zxvf deepnlp-0.1.7.tar.gz
    cd deepnlp-0.1.7
    python setup.py install

Initial setup


# install crf++0.58 package using the script
sh ./deepnlp/segment/install_crfpp.sh
# Download all the pre-trained models
python ./test/test_install.py

# Or Download pre-trained models from below command lines
import deepnlp
deepnlp.download('segment')
deepnlp.download('pos')
deepnlp.download('ner')
deepnlp.download('parse')


* Running Examples
```python
    # ./deepnlp/test folder
    cd test
    python test_segment.py    # segmentation
    python test_pos_en.py       # POS tag
    python test_ner_zh.py       # NER Zh
    python test_ner_domain.py   # NER domain-specific models
    python test_ner_dict_udf.py # NER load user dict and UDF for disambiguation
    python test_nn_parser.py    # dependency parsing
    python test_api_v1_module.py
    python test_api_v1_pipeline.py

Tutorial

Set Coding

设置编码 For python2, the default coding is ascii not unicode, use future module to make it compatible with python3

#coding=utf-8
from __future__ import unicode_literals # compatible with python3 unicode

Download pretrained models

下载预训练模型 If you install deepnlp via pip, the pre-trained models are not distributed due to size restriction. You can download full models for 'Segment', 'POS' en and zh, 'NER' zh, zh_entertainment, zh_o2o, 'Textsum' by calling the download function.

import deepnlp
# Download all the modules
deepnlp.download()

# Download specific module
deepnlp.download('segment')
deepnlp.download('pos')
deepnlp.download('ner')
deepnlp.download('parse')

# Download module and domain-specific model
deepnlp.download(module = 'pos', name = 'en') 
deepnlp.download(module = 'ner', name = 'zh_entertainment')

Segmentation

分词模块

#coding=utf-8
from __future__ import unicode_literals
from deepnlp import segmenter

tokenizer = segmenter.load_model(name = 'zh_entertainment')
text = "我刚刚在浙江卫视看了电视剧老九门，觉得陈伟霆很帅"
segList = tokenizer.seg(text)
text_seg = " ".join(segList)

#Results
# 我 刚刚 在 浙江卫视 看 了 电视剧 老九门 ， 觉得 陈伟霆 很 帅

POS

词性标注

#coding:utf-8
from __future__ import unicode_literals

import deepnlp
deepnlp.download('pos')

## English Model
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(name = 'en')  # Loading English model, lang code 'en', English Model Brown Corpus

text = "I want to see a funny movie"
words = text.split(" ")     # unicode
print (" ".join(words))

tagging = tagger.predict(words)
for (w,t) in tagging:
    pair = w + "/" + t
    print (pair)

#Results
#I/nn want/vb to/to see/vb a/at funny/jj movie/nn

## Chinese Model
from deepnlp import segmenter
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(name = 'zh') # Loading Chinese model, lang code 'zh', China Daily Corpus

text = "我爱吃北京烤鸭"
words = segmenter.seg(text) # words in unicode coding
print (" ".join(words))

tagging = tagger.predict(words)  # input: unicode coding
for (w,t) in tagging:
    pair = w + "/" + t
    print (pair)

#Results
#我/r 爱/v 吃/v 北京/ns 烤鸭/n

NER

命名实体识别


from __future__ import unicode_literals   # compatible with python3 unicode

import deepnlp
deepnlp.download('ner')  # download the NER pretrained models from github if installed from pip

from deepnlp import ner_tagger

# Example: Entertainment Model
tagger = ner_tagger.load_model(name = 'zh_entertainment')   # Base LSTM Based Model
#Load Entertainment Dict
tagger.load_dict("zh_entertainment")
text = "你 最近 在 看 胡歌 演的 猎场 吗 ?"
words = text.split(" ")
tagset_entertainment = ['actor', 'role_name', 'teleplay', 'teleplay_tag']
tagging = tagger.predict(words, tagset = tagset_entertainment)
for (w,t) in tagging:
    pair = w + "/" + t
    print (pair)

#Result
#你/nt
#最近/nt
#在/nt
#看/nt
#胡歌/actor
#演的/nt
#猎场/teleplay
#吗/nt
#?/nt

Parsing

依存句法分析


from __future__ import unicode_literals # compatible with python3 unicode coding

from deepnlp import nn_parser
parser = nn_parser.load_model(name = 'zh')

#Example 1, Input Words and Tags Both
words = ['它', '熟悉', '一个', '民族', '的', '历史']
tags = ['r', 'v', 'm', 'n', 'u', 'n']

#Parsing
dep_tree = parser.predict(words, tags)

#Fetch result from Transition Namedtuple
num_token = dep_tree.count()
print ("id\tword\tpos\thead\tlabel")
for i in range(num_token):
    cur_id = int(dep_tree.tree[i+1].id)
    cur_form = str(dep_tree.tree[i+1].form)
    cur_pos = str(dep_tree.tree[i+1].pos)
    cur_head = str(dep_tree.tree[i+1].head)
    cur_label = str(dep_tree.tree[i+1].deprel)
    print ("%d\t%s\t%s\t%s\t%s" % (cur_id, cur_form, cur_pos, cur_head, cur_label))

# Result
id  word    pos head    label
1   它   r   2   SBV
2   熟悉  v   0   HED
3   一个  m   4   QUN
4   民族  n   5   DE
5   的   u   6   ATT
6   历史  n   2   VOB

Pipeline

#coding:utf-8
from __future__ import unicode_literals

from deepnlp import pipeline
p = pipeline.load_model('zh')

#Segmentation
text = "我爱吃北京烤鸭"
res = p.analyze(text)

print (res[0].encode('utf-8'))
print (res[1].encode('utf-8'))
print (res[2].encode('utf-8'))

words = p.segment(text)
pos_tagging = p.tag_pos(words)
ner_tagging = p.tag_ner(words)

print (pos_tagging.encode('utf-8'))
print (ner_tagging.encode('utf-8'))

Textsum

自动文摘

See details: README

Textrank

重要句子抽取

See details: README

TextCNN (WIP)

文档分类

Train your model

自己训练模型

Segment model

See instructions: README

POS model

See instructions: README

NER model

See instructions: README

Parsing model

See instructions: README

Textsum model

See instructions: README

Web API Service

www.deepnlp.org provides free web API service for common NLP modules of sentences and paragraphs. The APIs are RESTful and based on pre-trained tensorflow models. Chinese language is now supported.

RESTful API

Testing API from Browser, Need to log in first

Calling API from python

See ./deepnlp/test/test_api_v1_module.py for more details.

#coding:utf-8
from __future__ import unicode_literals

import json, requests, sys, os
if (sys.version_info>(3,0)): from urllib.parse import quote 
else : from urllib import quote

from deepnlp import api_service
login = api_service.init()          # registration, if failed, load default empty login {} with limited access
conn = api_service.connect(login)   # save the connection with login cookies

# Sample URL
# http://www.deepnlp.org/api/v1.0/pipeline/?lang=zh&annotators=segment,pos,ner&text=我爱吃上海小笼包

# Define text and language
text = ("我爱吃上海小笼包").encode("utf-8")  # convert text from unicode to utf-8 bytes

# Set up URL for POS tagging
url_pos = 'http://www.deepnlp.org/api/v1.0/pos/?"+ "lang=" + quote('zh') + "&text=" + quote(text)
web = requests.get(url_pos, cookies = conn)
tuples = json.loads(web.text)
print (tuples['pos_str'].encode('utf-8'))    # POS json {'pos_str', 'w1/t1 w2/t2'} return string

中文简介

deepnlp项目是基于Tensorflow平台的一个python版本的NLP套装, 目的在于将Tensorflow深度学习平台上的模块，结合最新的一些算法，提供NLP基础模块的支持，并支持其他更加复杂的任务的拓展，如生成式文摘等等。

NLP 套装模块
- 分词 Word Segmentation/Tokenization
- 词性标注 Part-of-speech (POS)
- 命名实体识别 Named-entity-recognition(NER)
- 依存句法分析 Dependency Parsing (Parse)
- 自动生成式文摘 Textsum (Seq2Seq-Attention)
- 关键句子抽取 Textrank
- 文本分类 Textcnn (WIP)
- 可调用 Web Restful API
- 计划中: 句法分析 Parsing
算法实现
- 分词: 线性链条件随机场 Linear Chain CRF, 基于CRF++包来实现
- 词性标注: 单向LSTM/ 双向BI-LSTM, 基于Tensorflow实现
- 命名实体识别: 单向LSTM/ 双向BI-LSTM/ LSTM-CRF 结合网络, 基于Tensorflow实现
- 依存句法分析: 基于arc-standard system的神经网络的parser
预训练模型
- 中文: 基于人民日报语料和微博混合语料: 分词, 词性标注, 实体识别

API 服务

http://www.deepnlp.org 出于技术交流的目的, 提供免费API接口供文本和篇章进行深度学习NLP的分析, 简单注册后就可以使用。 API符合RESTful风格, 内部是基于tensorflow预先训练好的深度学习模型。具体使用方法请参考博客: http://www.deepnlp.org/blog/tutorial-deepnlp-api/

API目前提供以下模块支持：

分词: http://www.deepnlp.org/api/v1.0/segment/?lang=zh&text=我爱吃北京烤鸭
词性标注: http://www.deepnlp.org/api/v1.0/pos/?lang=zh&text=我爱吃北京烤鸭
命名实体识别: http://www.deepnlp.org/api/v1.0/ner/?lang=zh&text=我爱吃北京烤鸭
Pipeline: http://www.deepnlp.org/api/v1.0/pipeline/?lang=zh&annotators=segment,pos,ner&text=我爱吃北京烤鸭

安装说明

需要
- CRF++ (>=0.54) 可以从 https://taku910.github.io/crfpp/ 下载安装
- Tensorflow(1.0) 这个项目的Tensorflow函数会根据最新Release更新，目前支持Tensorflow 1.0版本，对于老版本的Tensorflow(<=0.12.0), 请使用 deepnlp <=0.1.5版本, 更多信息请查看 RELEASE.md
Pip 安装
```
pip install deepnlp
```

从源码安装, 下载deepnlp-0.1.7.tar.gz文件: https://pypi.python.org/pypi/deepnlp

# linux, run the script:
tar zxvf deepnlp-0.1.7.tar.gz
cd deepnlp-0.1.7
python setup.py install

初始设置

# 运行脚本安装 crf++0.58 包
sh ./deepnlp/segment/install_crfpp.sh
# 运行脚本下载预训练模型测试
python ./test/test_install.py

Reference

CRF++ package: https://taku910.github.io/crfpp/#download
Tensorflow: https://www.tensorflow.org/
Word Segmentation Using CRF++ Blog: http://www.52nlp.cn/%E4%B8%AD%E6%96%87%E5%88%86%E8%AF%8D%E5%85%A5%E9%97%A8%E4%B9%8B%E5%AD%97%E6%A0%87%E6%B3%A8%E6%B3%954
Blogs http://www.deepnlp.org/blog/

rockingdingo / deepnlp

readme

DeepNLP AI APP Store

Detailed AI Service Use Case

Multi-Aspect Rating

All Categories and People Groups

Reviews and Rating of AI App Store By Categories

AI Store of Image Generator

AI Image Generator ShowCases

AI Image Generator of FOOD

AI Image Generator of ANIMAL

AI Image Generator of CELEBRITY

AI Image Generator of SELFIES

AI Image Generator of SPORTS

AI Image Generator of TECHNOLOGY

AI Image Generator of TRAVEL

AI Image Generator of NATURE

AI Image Generator of DAILY LIFE

ShowCases

AI Store in Law

Employment Law

Contracts and Agreements

Business and Corporate Law

Real Estate

Civil Rights Law

Family Law

Personal Injury

Criminal Law

Immigration Law

AI in Finance: User Ratings, Reviews and Showcase

AI in Investment Finance

AI in INSURANCE Finance

AI in Mortgage and Loan Finance

AI in BANKING Finance

AI in DEBT Finance

AI in Healthcare

HOSPITAL APPOINTMENT

HOSPITAL

AI in ILLNESS

MEDICINE

NURSING

BEAUTY

ELDERLY CARE

MEDICAL INSTRUMENT

AI for Elderly: User Ratings, Reviews and Showcase

ShowCases

AI for Kids: User Ratings, Reviews and Showcase

STORY TELLING for KIDS

DRAWING|AIGC

AI in LifeStyle

Act as ${role}

ShowCases

AI in Productivity Tool

Writing Tool

Research Analysis

Programming

ShowCase

Programming

Plot Chat

Data Analysis

Related Blog

By End of 2020, deepnlp lib is archived and only support TF to 1.13

Brief Introduction

Modules

Installation

Tutorial

Set Coding

Download pretrained models

Segmentation

POS

NER

Parsing

Pipeline

Textsum

Textrank

TextCNN (WIP)

Train your model

Segment model

POS model

NER model