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Creation of human-like text
- Text generation is a technique that involves the creation of human-like text using artificial intelligence and machine learning algorithms. It enables computers to generate coherent and contextually relevant text based on patterns and structures learned from existing textual data.
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Natural Language Generation (NLG) Is the ability of a machine to produce human-like text or speech that is clear, concise, and engaging. It involves tasks like text summarization, storytelling, dialogue systems, and speech synthesis. NLG helps machines generate meaningful and coherent responses in a way that is easily understood by humans.
- Use Cases
- Task Variants
- Language Model Variants
- Text Generation from Image and Text
- Inference
- Text Generation Inference
- Chatui Spaces
- Useful Resources
Instruction Models
A model trained for text generation can be later adapted to follow instructions. One of the most used open-source models for instruction is OpenAssistant, which you can try at Hugging Chat.
Code Generation
A Text Generation model, also known as a causal language model, can be trained on code from scratch to help the programmers in their repetitive coding tasks. One of the most popular open-source models for code generation is StarCoder, which can generate code in 80+ languages. You can try it here.
Stories Generation
A story generation model can receive an input like "Once upon a time" and proceed to create a story-like text based on those first words. You can try this applicationwhich contains a model trained on story generation, by MosaicML. If your generative model training data is different than your use case, you can train a causal language model from scratch. Learn how to do it in the free transformers course!
Completion Generation Models
A popular variant of Text Generation models predicts the next word given a bunch of words. Word by word a longer text is formed that results in for example: 1. Given an incomplete sentence, complete it. 2. Continue a story given the first sentences. 3. Provided a code description, generate the code. The most popular models for this task are GPT-based models, Mistral or Llama series. These models are trained on data that has no labels, so you just need plain text to train your own model. You c...
Text-to-Text Generation Models
These models are trained to learn the mapping between a pair of texts (e.g. translation from one language to another). The most popular variants of these models are NLLB, FLAN-T5, and BART. Text-to-Text models are trained with multi-tasking capabilities, they can accomplish a wide range of tasks, including summarization, translation, and text classification.
When it comes to text generation, the underlying language model can come in several types: 1. Base models: refers to plain language models like Mistral 7B and Llama-2-70b. These models are good for fine-tuning and few-shot prompting. 2. Instruction-trained models: these models are trained in a multi-task manner to follow a broad range of instructio...
There are language models that can input both text and image and output text, called vision language models. LLaVA and BLIP-2 are good examples. Although they work just like other language models by means of input parameters for generation, since they also take input images, you can use them with image-to-text pipeline. You can find information abo...
You can use the 🤗 Transformers library text-generationpipeline to do inference with Text Generation models. It takes an incomplete text and returns multiple outputs with which the text can be completed. Text-to-Text generation models have a separate pipeline called text2text-generation. This pipeline takes an input containing the sentence includin...
Text Generation Inference (TGI) is an open-source toolkit for serving LLMs tackling challenges such as response time. TGI powers inference solutions like Inference Endpoints and Hugging Chat, as well as multiple community projects. You can use it to deploy any supported open-source large language model of your choice.
Hugging Face Spaces includes templates to easily deploy your own instance of a specific application. ChatUI is an open-source interface that enables serving conversational interface for large language models and can be deployed with few clicks at Spaces. TGI powers these Spaces under the hood for faster inference. Thanks to the template, you can de...
Would you like to learn more about the topic? Awesome! Here you can find some curated resources that you may find helpful!
Text generation is a process where an AI system produces written content, imitating human language patterns and styles. The process involves generating coherent and meaningful text that resembles natural human communication.
Mar 23, 2024 · Tutorials. Text generation with an RNN. On this page. Setup. Import TensorFlow and other libraries. Download the Shakespeare dataset. Read the data. Process the text. Run in Google Colab. View source on GitHub. Download notebook. This tutorial demonstrates how to generate text using a character-based RNN.
Jan 11, 2023 · Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications like text generators that compose coherent essays, chatbots that fool people into thinking they’re sentient, and text-to-image programs that produce photorealistic images of anything you can describe.
Text generation is a field that has been developing since the 1970s and is regarded as a subsection of NLP (Natural Language Processing). 2 Developing deep learning models for text generation is an ongoing process in the field of NLP. 3 As an example, the researchers are training Generative adversarial networks (GANs), which are generative model...
Text generation is a technique that involves the creation of human-like text using artificial intelligence and machine learning algorithms. It enables computers to generate coherent and contextually relevant text based on patterns and structures learned from existing textual data. How Text Generation Works.
