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Text Generation

Generating text is the task of producing new text. These models can, for example, fill in incomplete text or paraphrase.


Once upon a time,

Text Generation Model

Once upon a time, we knew that our ancestors were on the verge of extinction. The great explorers and poets of the Old World, from Alexander the Great to Chaucer, are dead and gone. A good many of our ancient explorers and poets have

About Text Generation

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.


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.

from transformers import pipeline

text2text_generator = pipeline("text2text-generation")
text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")
[{'generated_text': 'the answer to life, the universe and everything'}]

text2text_generator("translate from English to French: I'm very happy")
[{'generated_text': 'Je suis très heureux'}]

The T0 model is even more robust and flexible on task prompts.

text2text_generator = pipeline("text2text-generation", model = "bigscience/T0")

text2text_generator("Is the word 'table' used in the same meaning in the two previous sentences? Sentence A: you can leave the books on the table over there. Sentence B: the tables in this book are very hard to read." )
## [{"generated_text": "No"}]

text2text_generator("A is the son's of B's brother. What is the family relationship between A and B?")
## [{"generated_text": "brother"}]

text2text_generator("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy")
## [{"generated_text": "positive"}]

text2text_generator("Reorder the words in this sentence: justin and name bieber years is my am I 27 old.")
##  [{"generated_text": "Justin Bieber is my name and I am 27 years old"}]

Useful Resources

Would you like to learn more about the topic? Awesome! Here you can find some curated resources that you may find helpful!


Scripts for training


Compatible libraries

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Models for Text Generation
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Datasets for Text Generation
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Spaces using Text Generation

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Metrics for Text Generation
Cross Entropy
Cross Entropy is a metric that calculates the difference between two probability distributions. Each probability distribution is the distribution of predicted words
The Perplexity metric is the exponential of the cross-entropy loss. It evaluates the probabilities assigned to the next word by the model. Lower perplexity indicates better performance