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Text generation is the process of automatically producing coherent and meaningful text, which can be in the form of sentences, paragraphs, or even entire documents. It involves various techniques, which can be found under the field such as natural language processing (NLP), machine learning, and deep learning algorithms, to analyze input data and generate human-like text.
- 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 ...
Mar 16, 2023 · How to Choose an AI Text Generator. When choosing an AI text generator, there are several factors to consider. Here are some tips for choosing the right AI text generator for your needs: 1. Determine the type of text you want to generate. Some AI text generators are specialized for generating specific types of text, such as news articles or poetry.
Feb 5, 2023 · The num_return_sequences argument is set to 3, meaning the code will generate 3 separate sequences of text. The max_length argument is set to 30, meaning the generated text will be limited to a ...
Mar 1, 2020 · We will give a tour of the currently most prominent decoding methods, mainly Greedy search, Beam search, and Sampling. Let's quickly install transformers and load the model. We will use GPT2 in PyTorch for demonstration, but the API is 1-to-1 the same for TensorFlow and JAX. !pip install -q transformers.
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Customize text generation. You can override any generation_config by passing the parameters and their values directly to the generate method: >>> my_model.generate(**inputs, num_beams= 4, do_sample= True) Even if the default decoding strategy mostly works for your task, you can still tweak a few things. Some of the commonly adjusted parameters ...
