Keyword density helper – This tool comes with a built-in keyword density helper in some ways similar to the likes of SurferSEO or MarketMuse the difference being, ours is free! This feature shows the user the frequency of single or two word keywords in a document, meaning you can easily compare an article you have written against a competitor to see the major differences in keyword densities. This is especially useful for SEO’s who are looking to optimize their blog content for search engines and improve the blog’s visibility.
File compare – Text comparison between files is a breeze with our tool. Simply select the files you would like to compare, hit “Upload” and our tool will automatically insert the content into the text area, then simply hit “Compare” and let our tool show you where the differences in the text are. By uploading a file, you can still check the keyword density in your content.
Comparing text between URLs is effortless with our tool. Simply paste the URL you would like to get the content from (in our example we use a fantastic blog post by Sherice Jacob found here) hit “Submit URL” and our tool will automatically retrieve the contents of the page and paste it into the text area, then simply click “Compare” and let our tool highlight the difference between the URLs. This feature is especially useful for checking keyword density between pages!
You can also easily compare text by copying and pasting it into each field, as demonstrated below.
Ease of use
Our text compare tool is created with the user in mind, it is designed to be accessible to everyone. Our tool allows users to upload files or enter a URL to extract text, this along with the lightweight design ensures a seamless experience. The interface is simple and straightforward, making it easy for users to compare text and detect the diff.
Multiple text file format support
Our tool provides support for a variety of different text files and microsoft word formats including pdf file, .docx, .odt, .doc, and .txt, giving users the ability to compare text from different sources with ease. This makes it a great solution for students, bloggers, and publishers who are looking for file comparison in different formats.
Protects intellectual property
Our text comparison tool helps you protect your intellectual property and helps prevent plagiarism. This tool provides an accurate comparison of texts, making it easy to ensure that your work is original and not copied from other sources. Our tool is a valuable resource for anyone looking to maintain the originality of their content.
User Data Privacy
Our text compare tool is secure and protects user data privacy. No data is ever saved to the tool, the users’ text is only scanned and pasted into the tool’s text area. This makes certain that users can use our tool with confidence, knowing their data is safe and secure.
Compatibility
Our text comparison tool is designed to work seamlessly across all size devices, ensuring maximum compatibility no matter your screen size. Whether you are using a large desktop monitor, a small laptop, a tablet or a smartphone, this tool adjusts to your screen size. This means that users can compare texts and detect the diff anywhere without the need for specialized hardware or software. This level of accessibility makes it an ideal solution for students or bloggers who value the originality of their work and need to compare text online anywhere at any time.
EleutherAI is an open-source AI research consortium founded in 2020. EleutherAI was established to build open-source large language models, but the consortium has also researched in the fields of BioML, machine learning art, mechanistic interpretability, and model alignment.
EleutherAI uses cloud computing resources from CoreWeave and Tensorflow Research Cloud. This is a cheaper alternative to having privately owned computing resources.
Here are some of the popular Eleuther Models:
GPT-Neo was designed as an open-source alternative to GPT-3. It is a transformer model created using EleutherAI’s replication of the GPT-3 architecture, and it was trained using 125 million parameters.
Unlike some other models, you can train GPT-Neo locally on their GPUs instead of training it on a Cloud server.
You can then generate text using the trained model or download a pre-trained one. Users can also create and train their own tokenizer instead of using the recommended Huggingface’s pretrained GPT2 tokenizer.
GPT-Neo-1.3B is a transformer model that tries to replicate GPT-3 architecture while remaining open-source. It was trained using 1.3 billion parameters, and EleutherAI’s the Pile dataset.
The Pile dataset contains lewd and insulting expressions, so GPT-Neo-1.3B may generate insulting texts. It’s best to have a human moderator filter out generated text before releasing it directly to the public. This is unlike OpenAI’s NLP models that have an automated content moderator.
Also trained on the Pile dataset, GPT-J is an open-source transformer model trained on over 6 billion parameters. GPT-J was developed using a JAX-based python machine learning library.
GPT-J has a 68.29% accuracy for LAMBADA sentence completion and an average of 27.62% for zero-shot accuracy using the Hendrycks Test evaluation.
Announced in February 2022, GPT-NeoX-20B is the latest NLP model to be released by EleutherAI. GPT-NeoX-20B is an NLP model that is trained on 20 billion parameters. At the time of release, It was the largest open-source pretrained general purpose autoregressive language model.
GPT-NeoX-20B has a 72% accuracy on LAMBADA sentence completion. It has an average of 28.98% zero-shot accuracy for STEM, social science and humanities subject groups when measured using the Hendrycks Test Evaluation.
EleutherAI’s text generation web app runs based on the GPT-J-6B model. It generates text based on a human text prompt.
You can adjust the temperature and Top-P parameters of the NLP model.
The “Temperature” of the model determines the level of randomness of the generated text. It goes from 0 to 1.5, where 1.5 will give you the most creative and random text.
The “Top-P” parameter also controls the randomness of the model slightly. It goes from 0 to 1. You can adjust the parameters above randomly to evaluate the model’s generated text.
The AI text generator works quite well. But it takes some lengthy seconds or even a minute to respond, unlike Open AI’s ChatGPT whose response is almost instantaneous. The text generator is based on an NLP model that contains some lewd content. So, you might get a few offensive responses from the AI text generator.
The Pile is an 825 GB English language corpus or dataset designed to train large language models. The Pile was constructed from 22 different high-quality data subsets. Some of the datasets were pre-existing, while some were newly constructed. Several of the subsets were derived from professional and academic background sources.
Preexisting datasets incorporated into the collection include Project Gutenberg, the English Wikipedia, Open-Subtitles, Enron Emails Corpus, and Books3. New datasets were obtained from sources including GitHub, PubMed Central, YouTube, HackerNews, BookCorpus, BookCorpus2, Ubuntu IRC, ArXiv, Stack Exchange and others.
The Pile was developed as an alternative to GPT-2 and GPT-3, which performed poorly on many components of the Pile. The metric that indicated their poor performance was BPB.
The Pile BPB (bits per byte) is a measure of how robust a trained model performs in comprehending the varying domains of philosophy, GitHub repositories, medicine, chat logs, biology, physics, chemistry, mathematics, and computer science.
Pythia is an undergoing project by EleutherAI that seeks to research how knowledge is developed and how it evolves during training in autoregressive transformers. The project will study the grammar learning trajectories as well as the memorization and training order patterns of language models. EleutherAI’s team is using scaling laws and interpretability analysis to conduct this research.
Eight model sizes are trained on 2 different datasets including the Pile and the Pile deduplicated for the project. The models are trained within the GPT-NeoX library.
EleutherAI plans to release a user-friendly version of this project for other researchers to work on.
Language model evaluation harness is a project that supplies a unified framework for testing autoregressive language models such as GPT-J, GPT-Neo, GPT-2, and GPT-3. The language models are tested on various tasks like anagrams, arithmetic, ethics, STEM, social science, medicine, accounting, law, religion etc.
Polyglot is EleutherAI’s current project to train language models in Korean and other languages. The current project includes two large Korean language models with 1.3 billion and 3.8 billion parameters. Plans are made to include Nordic and East Asian languages.
Most large language models are predominantly trained using datasets comprising mostly of the English language. Polyglot aims to include languages like Romanian, Indonesian and Vietnamese that have yet to be largely ignored by other large language models.
A recent tweet by the official EleutherAI handle indicates that the consortium will be pivoting and focusing on AI ethics, alignment and interpretability in 2023. This will include work on plagiarism using AI and AI governance.
EleutherAI is also working on other projects that revolve around memorization, capacity removal and ELK (Eliciting Latent Knowledge).
In November 2022, EleutherAI won a 5.94M V100-hour INCITE grant to use ORNL’s Summit Computer to train foundation models along with other AI researchers. This is the first time the United States government funds open-source AI research with millions of dollars worth of computing power.
The future looks bright for Eleuther AI.
EleutherAI is an open-source consortium of AI and machine learning researchers that came together to provide a decentralized alternative to OpenAI. They work on open-source AI models and products that rival OpenAI’s offerings.
GPT-J performs better than the smaller GPT-3 individual models such as Ada and Babbage. But it does not perform as well as Davinci or GPT-3 as a whole. This is because GPT-3 Davinci was trained with more parameters and on a larger dataset.
That said, GPT-J is a good, open-source, free alternative to the costly GPT-3.
GPT-Neo is a free, open-source alternative to OpenAI’s GPT-3. GPT-Neo is a transformer-based NLP model. Three variants of GPT-Neo were released. They are the original GPT-Neo, GPT-Neo-1.3B and GPT-NeoX-20B.
Yes, GPT-J is open-source. You can join EleutherAI’s Discord channel if you want to contribute to the project.
Yes, GPT-Neo is free and open-source. It is a free alternative to GPT-3.
Yes, GPT-Neo is better than GPT-2.
EleutherAI developed GPT-Neo as an alternative to GPT-2 and GPT-3.
While not necessarily better than GPT-3, other alternatives to GPT-3 are BERT, OPT, AlexaTM, BLOOM, GLaM, PaLM, Gopher and GPT-Neo.
BLOOM is an NLP model trained on 176 billion parameters which is 1 billion parameters more than GPT-3. BLOOM can generate text in 46 natural languages and 13 programming languages.
GLaM by Google is a language model trained using a dataset of over 1.6 trillion tokens. A text quality filter was used to select quality data subsets of webpages combined with books and Wikipedia for the final dataset. GLaM has over 1.2 trillion parameters.
BERT by Google is a transformer-based language model for NLP research. It has two models pretrained using data from BooksCorpus and the English Wikipedia.
No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.
When making the tool we had to make each prompt as general as possible to be able to include every kind of input. Not to worry though ChatGPT is smart and will still understand the prompt.
Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.
Vahan Petrosyan
searchenginejournal.com
I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.
Tom Demers
searchengineland.com
After extensive research and testing, we determined Originality.ai to be the most accurate technology.
Rock Content Team
rockcontent.com
Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.
Felix Rose-Collins
ranktracker.com
ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.ai
Ashley Stahl
forbes.com
Originality.ai Do give them a shot!
Sri Krishna
venturebeat.com
For web publishers, Originality.ai will enable you to scan your content seamlessly, see who has checked it previously, and detect if an AI-powered tool was implored.
Industry Trends
analyticsinsight.net
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.
String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.
Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.
String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
With our tool it’s easy, just enter or upload some text, click on the button “Compare text” and the tool will automatically display the diff between the two texts.
Using text comparison tools is much easier, more efficient, and more reliable than proofreading a piece of text by hand. Eliminate the risk of human error by using a tool to detect and display the text difference within seconds.
We have support for the file extensions .pdf, .docx, .odt, .doc and .txt. You can also enter your text or copy and paste text to compare.
There is never any data saved by the tool, when you hit “Upload” we are just scanning the text and pasting it into our text area so with our text compare tool, no data ever enters our servers.
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This table below shows a heat map of features on other sites compared to ours as you can see we almost have greens across the board!
