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LawGPT is a large language model (LLM) fine-tuned for the legal sector. This implies it has been trained on a wide range of legal literature, allowing it to understand and generate legal language.

What is LaWGPT?

LaWGPT is a large language model (LLM) fine-tuned for the legal area. It is the work of Stability AI, a company that creates and deploys huge language models for a wide range of applications.

LaWGPT has been trained on a wide range of legal text, allowing it to understand and generate legal language. It is used in the legal profession for a range of activities, including:

LawGPT can be used for a variety of tasks in the legal profession, including:

Legal research: LawGPT can help you obtain essential legal information quickly and conveniently.

Legal writing: LawGPT can generate legal documents including contracts and petitions.

Legal analysis: LawGPT may be used to examine legal arguments and find flaws.

Legal education: LawGPT can be used to assist law students in learning the law.

Legal practice: Lawyers may utilize LawGPT to give better service to their clients.

LawGPT is still in the works, but it has the potential to transform the legal profession. LawGPT can free up attorneys to focus on more complicated and strategic work by automating processes that are now performed by humans. Furthermore, LawGPT can assist attorneys in delivering better service to their clients by giving them access to more information and assisting them in more efficiently analyzing legal arguments.

Law GPT is a powerful new instrument with the potential to significantly alter the legal profession. Law GPT is going to have a more major part in the way law is practiced as it develops.

Installation

To get started quickly with the LaW GPT project, follow these steps to prepare the code and create the environment:

1. Download the code:

git clone [email protected]:pengxiao-song/LaWGPT.git cd LaWGPT

2. Create the environment:

conda create -n lawgpt python=3.10 -y conda activate lawgpt pip install -r requirements.txt

3. Launch the Web UI (optional, for easy parameter adjustment):

Execute the service startup script:

bash scripts/webui.sh

4. Command line inference (optional, batch testing supported)

First, construct the test sample set with reference to the file content;resources/example_infer_data.json

Second, execute the inference script: . where parameter is the test sample set path, and if it is empty or the path is wrong, it is run in interactive mode.bash scripts/infer.sh--infer_data_path

LaWGPT project directory structure LaWGPT ├── assets # Static resources ├── resources # Project resources ├── models # Base models and Lora weights │ ├── base_models │ └── lora_weights ├── outputs # Fine-tuned instruction outputs ├── data # Experimental data ├── scripts # Script directory │ ├── chúng tôi # Instruction fine-tuning script │ └── chúng tôi # Service startup script ├── templates # Prompt templates ├── tools # Toolkits ├── utils ├── train_clm.py # Secondary training ├── chúng tôi # Instruction fine-tuning ├── chúng tôi # Service startup ├── README.md └── requirements.txt

Here’s a brief description of the main directories and files:

assets: This directory includes the project’s static resources.

resources: It contains project-specific resources.

models: This directory contains the base models and Lora weights.

outputs: It stores the output weights from fine-tuning instructions.

data: Experimental data is stored in this directory.

scripts: It contains various scripts, including finetune.sh for instruction fine-tuning and webui.sh for service startup.

templates: Prompt templates are stored here.

tools: Toolkits required for the project are located in this directory.

utils: Utility functions or modules can be found here.

train_clm.py: This script is used for secondary training.

finetune.py: It is used for fine-tuning instructions.

webui.py: This script is used to start the service.

README.md: A markdown file containing information about the project.

requirements.txt: A file listing the required Python packages for the project.

Data construction

This project is based on datasets such as legal document data and judicial examination data released by the Chinese Judgment Document Network; for more information, please see the Chinese legal data summary.

Primary data generation: Using Stanford_alpaca and self-instruct techniques, generate conversational Q&A data.

Knowledge-led data generation: Using a knowledge-based self-instruct technique, generate data based on Chinese legal structured knowledge.

Introduce ChatGPT to clean data and assist in the creation of high-quality datasets.

Model training

The training process of Law GPT series models is divided into two phases:

Phase 1: Expand the legal vocabulary and prepare Chinese-LLaMA for large-scale legal instruments and codex data.

The second stage: Create a legal conversation question and response dataset, then fine-tune the instructions based on the pre-trained model.

Secondary training process

Refer to Construct a secondary training datasetresources/example_instruction_train.json

run scripts/train_clm.sh

Instructions fine-tune the steps.

Refer to Constructing a Directive Fine-tuning Data Setresources/example_instruction_tune.json

run scripts/finetune.sh

Limitations

Due to the limitations of computing resources, data scale, and other factors, Law GPT has many limitations at this stage:

Model memory and language skills are limited due to limited data resources and model capacity. As a result, when presented with factual knowledge tests, inaccurate outcomes may be obtained.

The models in the series only have a basic alignment with human purpose. As a result, potentially dangerous information and content that does not adhere to human preferences and values may be generated.

There are issues with self-awareness, and Chinese understanding might be improved.

Also read: Flowise: A Drag-and-Drop UI for Building LLM Flows

You're reading Lawgpt: A New Ai Tool For The Legal Profession

A Tool For Investor – The Art Of Web Scraping

This article was published as a part of the Data Science Blogathon

if you want to know this, then you are in the right place….

I the industries, then one has to research about a particular industry, then google about the different companies after that, using NSE or BSE website analyze the stock by going to different tabs & links.

Imagine having the power to speed up this process by analyzing BSE/NSE website in a few seconds. I am sure now you surely have thought of it, so let me help you with it.

online source.

WHAT IS WEB SCRAPING?

Web scratching is an important method since it licenses quickly and is capable of extracting online data. Such data would then have the option to be taken care of to assemble bits of knowledge as required. In this manner, it furthermore makes it possible to screen the brand and reputation of an association.

How To Perform Web Scraping?

 After understanding web-scraping, the most common question is – How do I learn web scraping?

The process of web-scraping is really simple. To extract data using web scraping with python, you need to follow these basic steps:

1. Find the URL that you want to scrape.

2. Inspecting the Page.

3. Find the data you want to extract.

4. Write the code.

5. Run the code and extract the data.

6. Store the data in the desired format

All the steps mentioned above as shown below by performing actual web-scraping that will help in investing.

Let’s begin with the Art of Web Scraping

With the help of web scraping one can understand – when people are scared and in which stock one can invest and earn more even in the bearish market.

For performing the above-mentioned process of extracting data from the web i.e., web scraping, first we need to install some necessary libraries like:

· Pandas

· Bs4

· BeautifulSoup

· Webdriver_manager.chrome

· ChromeDriveManager

The code for importing the same is:

import pandas as pd from bs4 import BeautifulSoup from selenium import webdriver from webdriver_manager.chrome import ChromeDriverManager driver = webdriver.Chrome(ChromeDriverManager().install()) html=driver.page_source soup = BeautifulSoup(html,'html.parser')

Now, let’s check whether we are on the correct website or not…..

For checking, we will be using Beautiful Soup Library

The code for the same is:

print("Title of the website is : ") for title in soup.find_all('title'): print(title.get_text()) OUTPUT: Title of the website is :

Now, we have to open the NSE site on the other tab, let’s look at it for a second and try to observe different tags. To look for the tag names that are used in the actual website one needs to open inspect element.

What is Inspect Element?

Inspect element is one of the designer devices consolidated into Google Chrome, Firefox, Safari, and Internet Explorer internet browsers. By getting this instrument, one can really see — and even alter — the HTML and CSS source code behind the web content.

Inspect Element is a source that helps in viewing the source code of the website. There are two ways to open inspect element:

2. Use shortcut key – Ctrl + Shift + I

Source – It is a screenshot from my Laptop

After opening Inspect Element, search for the market/Index for which you want to extract data. Generally, all these types of information are known as a class and all classes are at the ‘P’ tag. Hence to extract information that is on the ‘P’ tag we will use the code:

para=soup.select('p') para

OUTPUT:

Now, it can be observed that we got all the information about different markets with dates + timings but this is not very readable/understandable. To make it easy to understand we will use code:

para = soup.findAll('p') for p in para: print(p.get_text())

OUTPUT:

 

Finally, we can now read it and understand it.

Now, let’s deep-dive into the same and now let us search for Index – I will choose NIFTY index, you can choose according to your own desire.

To get the NIFTY Index information we will use the code:

Nifty = soup.findAll('p', {'class':'tb_name'}) for name in Nifty: print(name.get_text())

OUTPUT:

NIFTY 50 NIFTY NEXT 50 NIFTY MIDCAP 50 NIFTY BANK NIFTY FINANCIAL SERVICES

Now let’s find out the value of each NIFTY Index for the same, we’ll use code:

Nifty = soup.findAll('p', {'class':'tb_name'}) value = soup.findAll('p', {'class':'tb_val'}) for Nifty_name in Nifty: print(Nifty_name.get_text()) for Nifty_value in value: print(Nifty_value.get_text())

OUTPUT:

NIFTY 50 NIFTY NEXT 50 NIFTY MIDCAP 50 NIFTY BANK NIFTY FINANCIAL SERVICES 17,802.00 42,443.10 8,606.30 39,400.55 18,829.70

 

Therefore, we got all the information we need to understand today’s Index for options trading.

In this article, we extracted a few pieces of information, but you can use the same technique to extract more data.

Another example for web scraping can be:

Let’s use the “DIV” tag now,

For this let’s use the code:

div=soup.find_all("div") div

OUTPUT:

(The output for this is also not readable and understandable)

 

Let’s make it easy to understand

For this we’ll use the code:

t = soup.body for T in t.find_all('div'): print(T.text)

OUTPUT:

Now, It can be observed that everything is readable and easy to understand…..

ABOUT THE AUTHOR

A 3rd-year (5th Semester) Student at CHRIST University, Lavasa, Pune Campus. Currently Pursuing BBA (BUSINESS ANALYTICS).

Website – chúng tôi (CHECK THIS OUT)

Contacts:

If you want to keep updated with my latest articles and projects, .

Connect with me via:

LinkedIn

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Related

Kde 4: A New Dawn For The Linux Desktop?

With a new visual interface including new themes sounds and effects as well as a semantic file manager, speed and multimedia handling improvements, KDE 4 is a major release for the Linux desktop.

While many of KDE 4’s new features will be visible to end users, a lot of work has been done under the hood that will benefit developers too with new frameworks including Phonon for multimedia, Plasma for desktop and panel interface and Strigi for search. The sum total of all KDE 4’s improvements could well serve to help bolster its adoption against its Linux desktop rival GNOME as well as Microsoft’s Windows.

“KDE has in my opinion has done something very good here, building foundations that are both very strong and very flexible,” Lars Knoll VP of Engineering at Trolltech told chúng tôi “They have tried to build KDE4 from the ground up and I am convinced that we will only see the full effect of having these foundations in a year or two from now.”

One of the defining elements of KDE is Trolltech’s Qt cross-platform application development framework. In KDE 4, KDE has move from the older Qt 3.x series to the new Qt 4.x which make a difference in a lot of different areas.

Graphics support is also improved by way of Qt4 as is accessibility support and internationalization. Knoll noted that internationalization is extremely important for KDE as it is translated into more than 40 languages from all corners of the world.

“In total I’d say that Qt4 is a much stronger foundation for a desktop environment than Qt3 could ever be,” Knoll said.

Holger Dyroff, Vice President, Product Management SUSE LINUX Products for Novell, views the enhanced usability in many KDE applications such as the file manager and PDF reader as benefits for its users. As well Dyroff, is keen on the smaller resource footprint of KDE 4 which can help users in resource constrained environments.

(SLED) use the rival GNOME Linux desktop as the default.

“KDE is an option on SLED releases, but there is no default selection on openSUSE, where we offer GNOME, KDE, and other desktop environments like XFCE,” Novell’s Dyroff noted.

Similarly Red Hat’s Fedora has KDE as an option as does Ubuntu (though the KDE version of Ubuntu is called Kubuntu).

“KDE 4 is a major feature on the Linux landscape in 2008, and while KDE 4.0 is not yet ready for production enterprise use, Novell anticipates that the innovations driven by KDE 4 will improve future versions of SLED,” Dyroff said. “We will include the latest stable version of all open source projects that compose the relevant desktop landscape for our broad customer community. However, GNOME will stay as default for our SUSE Linux Enterprise products, due to adoption and support in the industry with partners, ISVs and customers.”

df) shows that 71 percent of their users are using KDE versus 21 percent GNOME. As well the he noted that Mandriva Linux is mainly KDE based.

“So in total I do not feel that KDE lags in term of user adoption,” Knoll said. “It might lack in terms of mindshare, especially in the US.”

But the battle for the Linux desktop is not about GNOME versus KDE in Knoll’s view. He argued that having both desktops has helped the Linux desktop environment overall and provided choice. Knoll added that can also run KDE applications on GNOME and vice versa, so they are not excluding each other.

Knoll argued. “Windows and Mac OS X have over 98 percent market share together, why should KDE and GNOME fight for the small piece of the cake they have instead of trying to get part of the huge pieces that Microsoft and Apple currently have.”

This article was first published on chúng tôi

How To Search The Web With Bing’s New Ai

Get Microsoft Edge

As much as you may like Chrome, Firefox, or whatever web browser you use, Bing is a Microsoft product and it works best with the company’s homegrown browser, Edge. Without it, you won’t get full access to Microsoft’s AI-powered search engine (which they’ve branded “The New Bing”). If you open Bing in any other browser, you’ll still get to search the web, but you won’t get to try the platform’s chat mode. 

And that’s not the only restriction. To use Bing with ChatGPT, you’ll also need to log into a Microsoft account. If you don’t, you’ll only get five responses, after which the platform will prompt you to sign in to continue your conversation. If you do, Bing will give you access to 25 more responses, save the conversations you’ve had with it, and let you view them across Microsoft’s apps and services.   

[Related: 6 ways ChatGPT is actually useful right now]

Finally, if you have more intimate questions you’d prefer Bing didn’t archive under your name, we’re sorry to inform you that Edge’s InPrivate mode (the browser’s equivalent to Chrome’s Incognito mode and Firefox’s Private window) doesn’t support ChatGPT. This means there’s no way to have delicate conversations with the platform, so if you share a computer with someone else, make sure you log out after you’ve made your queries or search in a more private setting. 

Get familiar with the platform Choose a conversation style

The big difference between classic Bing and its AI-powered version is that it lets you search for information in a conversational style. This means the engine understands your questions in a specific context, which makes it easier to refine your search. For example, if you’re asking Bing how to get from the airport to your hotel on your next trip to Paris, the platform will understand what you mean when you follow up by asking which method of transportation is the cheapest or fastest. But there are actually several types of conversations you can have with Bing, depending on what you want it to do. 

Before you enter your prompt, use the buttons in the middle of the interface to define your query’s conversation style. Bing suggests Creative to “generate more imaginative and original responses, such as poems, stories, jokes, images, etc.,” but if you want “more informative and factual responses,” like search results and definitions, you should go with Balanced. Finally, if you’re looking for something even more specific, like calculations, conversions, or straightforward recipes, Precise mode is what you need. 

Do dogs dream? Bing generated three types of answers with similar information. Sandra Gutierrez for Popular Science

In our experience, no matter what conversational type you use, the information will be pretty much the same. Answers will mostly vary in length and tone, with Precise being the shortest and most straightforward. Keep in mind that you can’t change the conversational style midway through a conversation, so choose wisely—or you’ll have to start over. 

If you’ve been chatting with Bing for a while and can’t remember what conversational style you’re using, pay attention to the color of the interface. When using the Creative type, it’ll be magenta, you’ll see blue with Balanced, and green with Precise. 

Check for accuracy

Bing won’t discuss how it chooses the sources of it uses to generate answers to your questions. Sandra Gutierrez for Popular Science

Start again

The last function you’ll need to get familiar with is the New Topic button, which you’ll find in the bottom left corner of the interface. Sometimes you’ll only see its icon: a broom with some sparkles.

[Related: 3 ways to prevent ChatGPT from using you as training data]

This button will automatically archive your conversation and start a new one. By default, it’ll keep the conversation style you had for your previous conversation, but you can change it if you need to. And if you ever need to go back to an earlier chat and ask a follow-up question, you can find them listed in chronological order to the right of the interface and start exactly where you left off. 

Editpad Paraphrasing Tool: How To Create Undetectable Ai Content?

AI is one of the leading contributors to content creation today. Experts suggest that around 33% of marketers use AI to create content in one way or another. Meaning they might be using it to create outlines, product descriptions, or downright entire content pieces.

This indicates one thing; AI is the future of content creation. Now, it might be looked down upon to use AI to create content. But there are counter AI tools that help marketers avoid AI content detection. In a way, these tools help make the content original.

One such tool that we are discussing today is EditPad’s paraphrasing tool. How does it help create undetectable AI content? That is what we are here to find out, so let us get started and understand a few important things first.

AI content detection is not exactly rocket science. While there are AI content generators, there are AI content detection tools that sense that the text was not written by a human hand. Human-like content has soul and is a bit more friendly, casual, and sometimes even conversational.

Whereas GPT/AI-generated content would sound too perfect. In a way, this content would be robotic, will have no blemishes, and feature near-perfect punctuation. Therefore, the counter-AI tools can detect whether the text was written by a human or AI.

However, that does not mean it is beyond fixing. A lot of ethical ways allow users to employ AI and create content without letting AI do all the work. Because there are implications when writers are caught using AI in certain settings.

Academic writers might face suspension, whereas professional writers might face various penalties too. That is why it is important to understand the removal of AI content properly.

Now how do you go about removing AI content with EditPad’s paraphrasing tool? To do that, we first must understand how AI is used in content creation.

Then, we will analyze how content is captured/caught by AI using detection techniques. And finally, we will talk about using EditPad’s paraphrasing abilities to remove said AI content. So, let us begin.

The three main uses of AI for any content creator today are for research, creating outlines, and the content itself. While it is not really approved in many places around the world to use AI for content creation, using AI for research and creating outlines is quite all right. Here are three examples of it:

Research:

Here you can see that CPU generation can be a difficult concept to grasp, but AI simplifies it and explains it in layman’s terms. For someone looking to write about it, this can prove vital as research instead of having to understand it through Google.

Outlines:

Creating outlines is another way where AI is used, as you can see. It simplifies the process and helps the writer focus their research on specific areas by creating an outline, as seen above.

Content:

And in this prompt, you can see that the content written by AI is quite comprehensive. However, it is unacceptable as the ethical moral of doing this still hangs in the balance.

Now, as quick as AI is in writing content, some tools can catch it just as quickly. One such example would be EditPad’s own AI Content Detector. This tool uses the same techniques that we talked about earlier and can detect AI-written text like this:

As you can see, even if the writer has made some changes, it is still detecting it as AI-written content. So, let us keep going to find out how you can get rid of this detection.

This is when using EditPad’s paraphrasing tool can be handy to help you remove AI content. We have AI-written content, and now all we need to do is use EditPad’s paraphrasing to remove AI content from our text. Here is how:

You can see that EditPad has rephrased the text quite a bit. So, does it remove AI content detection? Let us evaluate it:

You can see that the content’s human-written percentage has gone from 53% to 92%. This tells you that EditPad does indeed help you remove AI-content detection. Now, there are plenty of content modes offered by this tool, such as:

Smooth

Reworder

Formal

Smart

Improver

That means users can try more than one type of content mode to match the rest of their text. This also helps in matching the content with the rest of their text.

Manual paraphrasing can be thorough but also lengthy. Moreover, manual paraphrasing takes a lot of time and might even cause additional stress. All of the things that we did in this article basically took only a few seconds.

From generating text with GPT to rephrasing it with EditPad’s paraphrasing tool, it all took less than 5 minutes. Now, consider all the hassle manual paraphrasing can cause, such as:

Reading and understanding.

Writing and proofreading.

And finalizing by checking for AI content again.

Even then, chances are AI content might still be there. Whereas using a paraphraser removes any doubt that the content will not be the same as it was before—we saw the demonstration above. For instance, EditPad’s paraphrasing was:

Quicker.

Profuse.

And easier.

It did not just save us from the hassle, but it also saved us from the implications of AI content detection. Therefore, EditPad paraphrasing is not only better than manual paraphrasing; it is also quicker and easier to do.

These are some of the key essentials of removing AI content with the help of EditPad’s paraphrasing tool. Using AI responsibly is the best way to ensure that no unethical outcome of the technology happens.

That is why writers should try a few different things, like using EditPad’s paraphrasing tool. Not only did it rid the content of AI-generated text quickly, but it also generated better text. Therefore, it is essential for those who use AI to assist with content writing.

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Related

Legal Code: Definition And Meaning

In a country with a civil law system, a code of law often fully covers the entire legal framework, including criminal and civil law. In contrast, amend the current common law in a common law nation with legislative techniques rooted in the English tradition only to the extent of its stated or implied provision, otherwise leaving the common law untouched. In a certain area, a code completely substitutes the common law, making the common law ineffective until the code is abolished.

What is the Legal Code?

Legal code refers to a set of laws or statutes that have been formally adopted by a government or other legislative body. It is the written body of laws that govern a particular jurisdiction, such as a state or country. These laws may cover a wide range of topics, including criminal and civil matters, property rights, and regulations for businesses and other organizations. Legal codes are usually created by a legislative body, such as a parliament or congress, and are enforced by the judicial branch of government.

Besides, a code of law is a standing body of statute law on a specific topic that is added to, subtracted from, or otherwise modified by individual legislative enactments, according to a third usage that is slightly different. This usage is common in the civil law system countries as well as common-law countries that have adopted similar legislative practices. However, different common law and civil law systems use codification in different ways, despite the fact that the methods and reasons for codification are comparable.

The Legal Code’s History

The legal code was a common element of the ancient Middle Eastern legal systems. The Sumerian Code of Ur-Nammu (c. 2100–2050 BC), the Law Code of Eshnunna (roughly 100 years before Lipit-Ishtar), the Law Code of Lipit-Ishtar (1934–1924 BC), and the Babylonian Code of Hammurabi (c. 1760 BC) are some of the earliest and best preserved legal codes, all coming from Sumer, Mesopotamia. The Uruk-Agina Law Code (2380-2, now Iraq).

Numerous codifications were created during the reign of the Roman Empire, including the Justinian Code and the Twelve Tables of Roman Law (first compiled in 450 BC) (429–534 AD). The Roman legal system was not fully described by these law codes, nonetheless. The Twelve Tables had a narrow reach, and most legal doctrines were created by pontiffs who “interpreted” the tables to address circumstances that were not covered by them. The Justinian Code compiled all of the then-current legal literature.

Continental legal systems have left their mark on the Americas in two different ways. Legal codes of the Continental style are common in civil law states. However, there has been a noticeable trend toward codification in common-law states. The end product of such codification is not usually a civil law jurisdiction’s equivalent of a legal code. For instance, the California Civil Code differs significantly from all other civil codes in both structure and content and substantially codifies common law doctrine.

The Seven Legal Code Principles

The Constitution is based on seven fundamental ideas. They are as follows-

Federalism

Republicanism

Checks and balances

Limited government

Popular sovereignty

Separation of powers

Individual rights

An Example of a Legal Code

A code is a grouping of laws, rules, or regulations that are organised in a specific way. The term “code” refers to both a compilation of already-existing statutes and the unwritten law on any topic made up of contents that can be found in all sources. Examples of codes are the Uniform Commercial Code and the United States Code.

Number of Legal Codes in India

According to the online repository maintained by the Legislative Department of the Ministry of Law and Justice of India as of July 2023, there are around 839 Central laws. Additionally, the same source also contains a number of State legislation for each state.

Division of Legal Code Civil Code

Civil law systems are often built around a civil code. The entirety of the private law system is often covered in depth by the legal code.

There are always common civil codes, these civil codes, however, sometimes consist of compilations of common law principles and several ad hoc acts; as a result, they do not aim for total logical coherence.

Criminal Code

In many legal systems, there is a criminal code or penal code. The criminal code can be codified to make it easier to access and to more democratically create and alter.

Conclusion

A legal code is a body of laws that a state or country has established. The Indian Penal Code, for instance, codifies and consolidates the general and permanent criminal laws of India as per subject matter. The Indian Parliament is responsible for creating and publishing the Indian Penal Code. In the meantime, common-law regimes also started to codify more frequently. For instance, a criminal code is present in several common law countries and it is still being discussed in many countries.

FAQs

Q1. What was the original code of law?

Ans. The Code of Hammurabi is the earliest known written system of laws. He ruled over Babylon from 1792 BC to 1758 BC. These laws are credited with having been given to Hammurabi by Shamash, the God of Justice.

Q2. Who created a code of laws?

Ans. Hammurabi, a king of Babylon.

The Babylonian monarch Hammurabi, who ruled from 1792 to 1750 B.C., established the Code of Hammurabi, one of the first and most comprehensive written law systems. Along the Euphrates River, Hammurabi developed the city-state of Babylon to encompass all of southern Mesopotamia.

Q3. Is a code a written law?

Ans. As soon as you run a company or organisation covered by the relevant law or regulation, the mandatory code will be binding. Only after you voluntarily sign up for a code will it become enforceable.

Q4. How do laws become codified?

Ans. Codification refers to the systematic coding of laws, rules, or regulations. It is possible to codify judicial rulings or legislative actions through the codification process. This procedure merely organises current law into a code, typically by subject, without necessarily producing new legislation.

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