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Showing 13 open source projects for "generate"
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1
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. -
2
BlogWizard
Generate blog articles from video or audio
BlogWizard is a demo/utility project built on top of Groq’s LLM infrastructure that converts video or audio content into well-structured blog posts, enabling creators to repurpose multimedia content into text — useful for SEO, accessibility, or reaching audiences that prefer reading. The tool uses transcription (e.g. via Whisper) to extract text from audio/video, then runs an LLM-based generation pipeline to transform that content into coherent, readable blog-format posts — with sections,... -
3
TagForge
Cross-platform AI tagging and prompt engineering studio
.... ## Key Capabilities - Multi-model tagging: Specialized generators for Stable Diffusion (tags), FLUX/Midjourney (prose), and custom LLM workflows - Multimodal vision: Extract metadata or generate descriptive captions directly from images - Contextual chat assistant: Persistent, coding-capable AI helper with real-time provider features - Persona system: Create custom identities with dynamic input injection and role-based templates - Chat rules: Modular behavioral controls (Concise, Detailed, etc.) with CRUD interface - Agent orchestrator: Centralized management of API keys, endpoints, and model parameters -
4
Intelligent Java
Integrate with the latest language models, image generation and speech
...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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5
Minimal text diffusion
A minimal implementation of diffusion models for text generation
A minimal implementation of diffusion models of text: learns a diffusion model of a given text corpus, allowing to generate text samples from the learned model. The main idea was to retain just enough code to allow training a simple diffusion model and generating samples, remove image-related terms, and make it easier to use. To train a model, run scripts/train.sh. By default, this will train a model on the simple corpus. However, you can change this to any text file using the --train_data argument. ... -
6
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... -
7
AI Chatbots based on GPT Architecture
Training & Implementation of chatbots leveraging GPT-like architecture
...This repo is a simple attempt to help solve that problem. ai-msgbot covers the practical use case of building a chatbot that sounds like you (or some dataset/persona you choose) by training a text-generation model to generate conversation in a consistent structure. This structure is then leveraged to deploy a chatbot that is a "free-form" model that consistently replies like a human. Some of the trained models can be interacted with through the HuggingFace spaces and model inference APIs on the ETHZ Analytics Organization page on huggingface.co. -
8
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. -
9
gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...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. ... -
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10
Accelerated Text
Accelerated Text is a no-code natural language generation platform
...Accelerated Text is a no-code natural language generation platform. It will help you construct document plans which define how your data is converted to textual descriptions. With Accelerated Text you can use such data to generate text for your business reports, your e-commerce platform or your customer support system. Data descriptions require precision. Accelerated Text follows the principle of this strict adherence to data-bound text generation. Via its user interface, it provides instruments to define how the data should be translated into a descriptive text. -
11
onnxt5
Summarization, translation, sentiment-analysis, text-generation, etc.
...Run any of the T5 trained tasks in a line (translation, summarization, sentiment analysis, completion, generation) Export your own T5 models to ONNX easily. Utility functions to generate what you need quickly. Up to 4X speedup compared to PyTorch execution for smaller contexts. -
12
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... -
13
GPT-2 FR
GPT-2 French demo | Démo française de GPT-2
OpenAI GPT-2 model trained on four different datasets in French. Books in French, French film scripts, reports of parliamentary debates, Tweet by Emmanuel Macron, allowing to generate text. Tensorflow and gpt-2-simple are required in order to fine-tune GPT-2. Create an environment then install the two packages pip install tensorflow==1.14 gpt-2-simple. A script and a notebook are available in the src folder to fine-tune GPT-2 on your own datasets. The output of each workout, i.e. the folder checkpoint/run1, is to be put ingpt2-model/model1 model2 model3 etc. ...
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