Open Source AI Text Generators
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AI Text Generators
View 434 business solutionsBrowse free open source AI Text Generators and projects below. Use the toggles on the left to filter open source AI Text Generators by OS, license, language, programming language, and project status.
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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Text Generation Web UI
Oobabooga - The definitive Web UI for local AI, with powerful features
A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. Dropdown menu for switching between models. Notebook mode that resembles OpenAI's playground. Chat mode for conversation and role playing. Instruct mode compatible with Alpaca and Open Assistant formats. Nice HTML output for GPT-4chan. Markdown output for GALACTICA, including LaTeX rendering. Custom chat characters. Advanced chat features (send images, get audio responses with TTS). Very efficient text streaming. Parameter presets, 8-bit mode. Layers splitting across GPU(s), CPU, and disk. CPU mode, FlexGen, DeepSpeed ZeRO-3, API with streaming and without streaming. LLaMA model, including 4-bit GPTQ. RWKV model, LoRA (loading and training), Softprompts, and extensions. -
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TextBox
A text generation library with pre-trained language models github.com
TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation. From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models. -
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amrlib
A python library that makes AMR parsing, generation and visualization
A python library that makes AMR parsing, generation and visualization simple. amrlib is a python module designed to make processing for Abstract Meaning Representation (AMR) simple by providing the following functions. Sentence to Graph (StoG) parsing to create AMR graphs from English sentences. Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences. A QT-based GUI to facilitate the conversion of sentences to graphs and back to sentences. Methods to plot AMR graphs in both the GUI and as library functions. Training and test code for both the StoG and GtoS models. A SpaCy extension that allows direct conversion of SpaCy Docs and Spans to AMR graphs. Sentence to Graph alignment routines FAA_Aligner (Fast_Align Algorithm), based on the ISI aligner code detailed in this paper. RBW_Aligner (Rule Based Word) for a simple, single token to single node alignment. -
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Dickinson
Text generation language
Dickinson is a text-generation language. You can try out the language on the web without installing anything. Binaries for some platforms are available on the releases page. There is an install script that will try to download the right release for your computer. -
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gpt-j-api
API for the GPT-J language mode. Including a FastAPI backend
An API to interact with the GPT-J language model and variants! You can use and test the model in two different ways. These are the endpoints of the public API and require no authentication. Just SSH into a TPU VM. This code was tested on both the v2-8 and v3-8 variants. -
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Travesty
Parody text generator
A parody text generator. This is taken from the article published in BYTE Magazine in 1984. Literary critic Hugh Kenner and computer scientist Joseph O'Rourke introduced their text scrambler "Travesty" in an issue of BYTE magazine 1984. See the Wikipedia page for more information. The code has been mostly preserved, I've just added a GUI to make it easier to play around with the options and included a copy of Alice in Wonderland. A Windows binary is available on the releases page. Parody generators are computer programs which generate text that is syntactically correct, but usually meaningless, often in the style of a technical paper or a particular writer. They are also called travesty generators and random text generators. Their purpose is often satirical, intending to show that there is little difference between the generated text and real examples. -
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Node.js Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models
This is the Node.js client (with Typescript types) for the NLP Cloud API. NLP Cloud serves high-performance pre-trained or custom models for NER, sentiment analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, text generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, and served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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gpt2-client
Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.
GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. It is the successor to the GPT (Generative Pre-trained Transformer) model trained on 40GB of text from the internet. It features a Transformer model that was brought to light by the Attention Is All You Need paper in 2017. The model has 4 versions - 124M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters they contain. Finally, gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more accessiblity, comprehensibility, and utilty. You can play around with all four GPT-2 models in less than five lines of code. Install client via pip. The generation options are highly flexible. You can mix and match based on what kind of text you need generated, be it multiple chunks or one at a time with prompts. -
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hfapigo
Unofficial (Golang) Go bindings for the Hugging Face Inference API
(Golang) Go bindings for the Hugging Face Inference API. Directly call any model available in the Model Hub. An API key is required for authorized access. To get one, create a Hugging Face profile. -
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Ad Generator
Professional text randomizer and ad generator by Airat Khalitov
Professional text randomizer and ad generator by Airat Khalitov / Professional text randomizer and ad generator. Author: Airat Halitov. Visit 'Plugins, Add New', click 'Upload Plugin', upload the file 'ad-generator.zip', and activate Ad Generator from your Plugins page. Add [ad_generator] shortcode to WordPress Page. Create a new WordPress Page, add [ad_generator] shortcode and save. Go to the page and use the ad generator. This is a program for industrial creation of pseudo-unique content. Used, for example, when registering a site in multiple directories. So that in each directory the site is described by text that is unique from the point of view of search engines. Unlike similar tools (synonymizers, dorgens), it allows you to maximize the readability of the resulting texts. -
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CPT
CPT: A Pre-Trained Unbalanced Transformer
A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation. We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. Position Embeddings We extend the max_position_embeddings from 512 to 1024. We initialize the new version of models with the old version of checkpoints with vocabulary alignment. Token embeddings found in the old checkpoints are copied. And other newly added parameters are randomly initialized. We further train the new CPT & Chinese BART 50K steps with batch size 2048, max-seq-length 1024, peak learning rate 2e-5, and warmup ratio 0.1. Aiming to unify both NLU and NLG tasks, We propose a novel Chinese Pre-trained Un-balanced Transformer (CPT). -
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GPT2 for Multiple Languages
GPT2 for Multiple Languages, including pretrained models
With just 2 clicks (not including Colab auth process), the 1.5B pretrained Chinese model demo is ready to go. The contents in this repository are for academic research purpose, and we do not provide any conclusive remarks. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC) Simplifed GPT2 train scripts（based on Grover, supporting TPUs). Ported bert tokenizer, multilingual corpus compatible. 1.5B GPT2 pretrained Chinese model (~15G corpus, 10w steps). Batteries-included Colab demo. 1.5B GPT2 pretrained Chinese model (~30G corpus, 22w steps). -
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Intelligent Java
Integrate with the latest language models, image generation and speech
Intelligent java (IntelliJava) is the ultimate tool to integrate with the latest language models and deep learning frameworks using java. The library provides an intuitive functions for sending input to models like ChatGPT and DALL·E, and receiving generated text, speech or images. With just a few lines of code, you can easily access the power of cutting-edge AI models to enhance your projects. Access ChatGPT, GPT3 to generate text and DALL·E to generate images. OpenAI is preferred for quality results without tuning. Generate text; Cohere allows you to generate a language model to suit your specific needs. Generate audio from text; Access DeepMind’s speech models. The only dependencies is GSON. Required to add manually when using IntelliJava jar. However, if you imported this repo through Maven, it will handle the dependencies. -
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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with synonyms, random word insertion, deletion, replacement, etc. method, generating new text and implementing text augmentation This project realizes the back translation function based on Baidu translation API, first translate Chinese sentences into English, and then translate English into new Chinese. This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
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abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
Enter your abstract into the little doohicky here, and quicker'n you can blink your eyes1, a shiny new paper'll come right out for ya! What are you waiting for? Click the "doohicky" link above to get started, and then click the link to open the demo notebook in Google Colaboratory. To run the demo as a Jupyter notebook (e.g., locally), use this version instead. Note: to compile a PDF of your auto-generated paper (when you run the demo locally), you'll need to have a working LaTeX installation on your machine (e.g., so that pdflatex is a recognized system command). The notebook will also automatically install the transformers library if it's not already available in your local environment. In its unmodified state, the demo notebooks use the abstract from the GPT-3 paper as the "seed" for a new paper. Each time you run the notebook you'll get a new result. -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions). Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time. -
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node-red-contrib-custom-chatgpt
Node-RED ChatGPT
A Node-RED node that interacts with OpenAI machine learning models like "ChatGPT". Install with the built-in Node-RED Palette manager. When editing the properties of the node, to get your OPENAI_API_KEY log in to ChatGPT. Create a new secret key" then copy and paste the "API key" into the node API_KEY property value. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. Its success generally depends on the complexity of the task and quality of your prompt. A good rule of thumb is to think about how you would write a word problem for a middle schooler to solve. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. -
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text-generator
Golang text generator for generate SEO texts
Golang text generator for generate SEO texts. Fast text generator on a mask. Written in Golang. I do not use regular expressions and the fastest. I covered tests and simple! Supporting recursive text generation rules. -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). Input audio and images for generation instead of just text. Simplify tool setup process on colab, and enable ‘one-click’ sharing of the generated link to other users. Experiment with the possibilities for multi-user access to the same link. -
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Aida Lib
Aida is a language agnostic library for text generation
Aida is a language-agnostic library for text generation. When using Aida, first you compose a tree of operations on your text that includes conditions via branches and other control flow. Later, you fill the tree with data and render the text. A building block is a variable class: Var. Use it to represent a value that you want to control later. A variable can hold numbers (e.g. float, int) or strings. You can create branches and complex logic with Branch. The context, represented by the class Ctx, is useful to create rules that depends on what has been written before. Each object or literal that is passed to Aida is remembered by the context. Creating a reference expression is a common use-case, so we have a helper function called create_ref. You can compose operations on your text with some handy operators. -
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PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. Pass the model you want to use and the NLP Cloud token to the client during initialization. If you are making asynchronous requests, you will always receive a quick response containing a URL. -
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Regex
Generate matching and non matching strings based on regex patterns
Generate matching and non-matching strings. This is a java library that, given a regex pattern, allows to generation of matching strings. Iterate through unique matching strings. Generate not matching strings. Follow the link to Online IDE with created project: JDoodle. Enter your pattern and see the results. By design a+, a* and a{n,} patterns in regex imply an infinite number of characters should be matched. When generating data, that would mean values of infinite length might be generated. It is highly doubtful anyone would require a string of infinite length, thus I've artificially limited repetitions in such patterns to 100 symbols when generating random values. Use a{n,m} if you require some specific number of repetitions. It is suggested to avoid using such infinite patterns to generate data based on regex. -
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Text Gen
Almost state of art text generation library
Almost state of art text generation library. Text gen is a python library that allow you build a custom text generation model with ease. Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use.
Open Source AI Text Generators Guide
Open source AI text generators are a type of artificial intelligence (AI) technology that provides users with the ability to generate natural language text. These tools are typically used for such tasks as content creation and writing, as well as generating automated responses to queries. Open source AI text generators rely on machine learning algorithms to process input data in order to create output text.
One of the most widely-used open source AI text generation solutions is Google’s TensorFlow. This tool leverages deep learning and natural language processing technologies in order to generate high-quality output text from input data. The software can be used to generate articles, blog posts, stories and more with minimal effort on the part of the user. Other popular open source options include GPT-2 and OpenAI's GPT-3, which employ advanced neural network models for automatic generation of sentences and paragraphs from given context or prompts.
When creating texts using an open source AI generator, users provide seed words or phrases which then form the basis for the rest of the generated sentence or paragraph structure. Additionally, some text generation solutions come equipped with “training libraries” – collections of past works written by famous authors - which allow users to emulate particular styles or genres when producing their own pieces of work. In addition to these features, some systems also come with features like sentiment analysis and categorization in order determine whether generated texts contain positive or negative sentiments towards a certain topic.
Overall, open source AI text generators offer a convenient solution for content production while reducing costs associated with hiring professional writers. While these programs are constantly evolving through updates and improvements in their underlying machine learning algorithms, it is important for users to remain aware of potential errors associated with the use of such tools so that they can take appropriate measures when necessary.
Features of Open Source AI Text Generators
- Text Generation: Open source AI text generators are able to generate text from a given input, such as a picture or phrase. This text can be used for natural language processing tasks and machine learning applications.
- Natural Language Processing (NLP): NLP is the process of analyzing written or spoken language and understanding its meaning in order to take action or produce an appropriate response.AI-based open source text generators provide tools such as named entity recognition, part of speech tagging, sentiment analysis, and more that help identify key structures in documents.
- Machine Learning Applications: Open source AI text generators can also be used to train machine learning models with large datasets of textual data. Text generation tools can help create feature vectors used for supervised and unsupervised learning techniques, including classification, clustering, and optimization problems.
- Contextualization: Text generation tools utilize context in order to produce better quality results when generating text from a given input. For example, if the language being processed is English then the tool will pick up on different tenses being used throughout the generated output.
- Output Customization: Some open source AI text generators also offer customization options for their outputs based on user-defined parameters like vocabulary size or words per sentence length limit. This allows users to tailor-make generated texts according to specific linguistic patterns or preferences needed for certain projects.
Types of Open Source AI Text Generators
- Natural Language Generators (NLG): This type of open source AI text generator uses natural language processing and machine learning algorithms to produce text from data. NLG systems are capable of generating reports, answers to questions, descriptions of objects, summaries and much more.
- Autocomplete Systems: Autocomplete is a type of open source AI text generator that provides suggested words or phrases based on partial input from the user. It can be used to speed up typing in applications such as word processors and web browsers.
- Text Summarizers: Text summarizers use natural language processing algorithms to generate concise summaries from longer documents by extracting key information and facts. They can be used to quickly summarize articles or other lengthy texts with no human involvement required.
- Chatbot Generators: This type of open source AI text generator generates conversational scripts for chatbots based on user input. The system is able to understand the conversation context and generate appropriate responses in natural language format within seconds.
- Speech Synthesizers: Speech synthesizers use advanced deep learning algorithms to convert text into spoken audio output which sounds nearly indistinguishable from that produced by a human voice actor. These systems are particularly useful for providing audio versions of written documents for accessibility purposes and facilitating speech recognition applications such as Amazon's Alexa personal assistant system and Google Assistant platform.
Open Source AI Text Generators Advantages
- Customization: Open source AI text generators provide users with the ability to customize the output to meet their specific needs. This may include content type or other parameters like length and style.
- Cost savings: Open source AI text generators are free for anyone to use, giving users access to powerful tools without any cost associated.
- Freedom from copyright restrictions: Using open source AI text generators eliminates the need for license fees or permission from the creator of an existing work, since all generated content is property of the user.
- Reduced development time: Open source tools often simplify complex tasks like writing scripts or building language models into a few lines of code. This reduces development time significantly compared to manual coding processes.
- Quality control: The open-source nature of these tools also guarantees quality standards by allowing experienced professionals and developers to review and suggest improvements when necessary.
Who Uses Open Source AI Text Generators?
- Scientists: Use open source AI text generators to create experimental datasets and test out new theories.
- Businesses: Utilize these tools to generate content for marketing materials, training documents, and customer-facing content.
- Journalists: Generate stories for multiple media outlets with the help of AI text generators.
- Educators: Create lesson plans and teaching guides with the assistance of open source AI text generators.
- Hobbyists/Enthusiasts: Generate creative writing pieces, such as short stories or poems, with the use of these tools.
- Game Developers: Utilize AI text generators to create highly dynamic dialogue in video games or other interactive experiences.
- Healthcare Professionals: Develop medical records and patient histories using automated solutions powered by AI technology.
- Authors/Writers: Open source AI text generators can be used as a brainstorming tool, offering suggestions on plot lines or characters while generating ideas quickly and efficiently.
How Much Do Open Source AI Text Generators Cost?
Open source AI text generators can be used for free, so cost is not an issue. However, if you are looking to create more advanced text generators then it may be necessary to purchase additional software or services that can help you modernize your system. For instance, if you want a more complex generator that is capable of processing natural language data and understanding context better then you might need to buy extra modules or services like AI-as-a-service (AIaaS). These services usually offer custom solutions tailored to specific needs, however they will typically cost between $1,000 - $20,000 depending on complexity and functionality. If the project requires advanced machine learning capabilities such as deep learning models then the cost could go much higher than this range. Additionally, many open-source tools are available online which allow users to build their own text generator without needing outside programming skills or expertise. In most cases these projects may require minimal setup costs for cloud hosting and related services but are ultimately quite affordable compared other AI solutions.
What Software Can Integrate With Open Source AI Text Generators?
There are many different types of software that can integrate with open source AI text generators. These range from content management systems, such as WordPress and Drupal, to website design tools like Squarespace and Wix. Additionally, various eCommerce solutions like Shopify also offer support for open source AI text generation capabilities. Similarly, chatbot building platforms such as Chatfuel and ManyChat can be used to incorporate these AI-based functionalities into conversational interfaces for applications. Finally, online collaboration services like Slack and Google Docs may be configured to leverage the use of open source AI text generators in order to enhance their organizational workflows without having to write complicated code. In sum, the potential for integration between an array of software solutions and open source AI text generators is extensive.
Trends Related to Open Source AI Text Generators
- Natural Language Processing (NLP): Open source AI text generators use NLP to create natural-sounding text that is syntactically correct. This allows them to generate more realistic sentences and paragraphs than traditional text generators.
- Machine Learning: Open source AI text generators use machine learning algorithms to generate text based on user input. This allows for more creative and flexible output than traditional text generators.
- Neural Networks: Open source AI text generators use neural networks to better understand the context of the input and generate more accurate and relevant output.
- Deep Learning: Open source AI text generators use deep learning algorithms to better understand the context of the input and generate more accurate and relevant output.
- Natural Language Generation (NLG): Open source AI text generators use NLG to create coherent, meaningful sentences from structured data. This allows them to generate more natural-sounding output than traditional text generators.
- Natural Language Understanding (NLU): Open source AI text generators use NLU to better understand user input and generate more accurate and relevant output.
- Automated Content Generation: Open source AI text generators can automatically generate content by analyzing structured data sources such as databases, web APIs, and social media feeds. This allows them to generate large amounts of content quickly and accurately.
How To Get Started With Open Source AI Text Generators
Getting started with using open source AI text generators is easy. Begin by researching the different options available and selecting a generator that meets your needs. Many of these programs come with tutorials on how to use them, so take the time to go through them if you are unfamiliar with the program. Be sure to pay attention to any technical jargon or tips that might be helpful for you in getting started.
Once you have chosen and installed your text-generation software, it's time to begin putting it into action. Start by entering some basic input data (e.g., a few sentences) into the program so that it can learn from this data set and start generating more advanced sentences on its own over time. Additionally, make sure to check out any settings or configuration tools included with the software as customizing things such as grammar rules may help improve the quality of output generated by an AI generator over time.
Finally, experiment with different types of inputs and outputs in order to see what kind of results an AI text generator produces best under various conditions; this may help you refine your approach or discover something new about natural language processing or other related techniques. As long as you keep working at it, soon enough you will have created texts or stories that are entirely generated by an AI system - something truly amazing.
