This task covers guides on both text-generation and text-to-text generation models. Popular large language models that are used for chats or following instructions are also covered in this task. You can find the list of selected open-source large language models here, ranked by their performance scores.
Use Cases
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 application which 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!
Task Variants
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:
Given an incomplete sentence, complete it.
Continue a story given the first sentences.
Provided a code description, generate the code.
The most popular models for this task are GPT-based models (such as GPT-2). These models are trained on data that has no labels, so you just need plain text to train your own model. You can train GPT models to generate a wide variety of documents, from code to stories.
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 T5, T0 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.
Inference
You can use the Transformers library text-generation pipeline to do inference with Text Generation models. It takes an incomplete text and returns multiple outputs with which the text can be completed.
from transformers import pipeline
generator = pipeline('text-generation', model = 'gpt2')
generator("Hello, I'm a language model", max_length = 30, num_return_sequences=3)
## [{'generated_text': "Hello, I'm a language modeler. So while writing this, when I went out to meet my wife or come home she told me that my"},
## {'generated_text': "Hello, I'm a language modeler. I write and maintain software in Python. I love to code, and that includes coding things that require writing"}, ...
Text-to-Text generation models have a separate pipeline called text2text-generation. This pipeline takes an input containing the sentence including the task and returns the output of the accomplished task.
