Search results
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. You can try some of the most powerful instruction-tuned open-access models like Mixtral 8x7B, Cohere Command R+, and Meta Llama3 70B 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 Meta Llama-3-70b. 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 instr...
There are language models that can input both text and image and output text, called vision language models. IDEFICS 2 and MiniCPM Llama3 V are good examples. They accept the same generation parameters as other language models. However, since they also take images as input, you have to use them with the image-to-text pipeline. You can find more inf...
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!
May 24, 2023 · 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. Text generation has gained significant importance in various fields, including natural language processing, content ...
Collaborate on models, datasets and Spaces. Faster examples with accelerated inference. Switch between documentation themes. Sign Up. to get started. 500. Not Found. ← Video-text-to-text Best Practices for Generation with Cache →. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Feb 5, 2023 · Text generation using deep learning models has several applications, including: Content creation: Text generation models can be used to generate articles, summaries, headlines, and other types of ...
Mar 1, 2020 · In open-ended generation, a couple of reasons have been brought forward why beam search might not be the best possible option: Beam search can work very well in tasks where the length of the desired generation is more or less predictable as in machine translation or summarization - see Murray et al. (2018) and Yang et al. (2018). But this is ...
People also ask
How does text generation work?
What is a text generation model?
How do text-generation models work?
What is text generation in AI?
How do I train a text generation model?
What is generating text?
Jul 19, 2023 · Creator: OpenAI. First published: March 2023. GPT-4 is OpenAI’s flagship large language model. It can generate text from both images and text inputs. GPT-4 was designed to replace GPT-3 and GPT-3.5, one of the models used to fine-tune ChatGPT. It powers ChatGPT Plus, OpenAI’s $20-a-month premium subscription service.
