Jsonformer simplifies and improves JSON generation by focusing on content tokens and filling in fixed tokens
- Generating structured JSON from language models is a difficult task, often resulting in syntactically incorrect output.
- Jsonformer is a new approach that fills in fixed tokens during the generation process, delegating only content token generation to the language model.
- The method is more efficient and bulletproof than existing approaches, ensuring the generated JSON is always syntactically correct and conforms to the specified schema.
- Jsonformer supports a subset of JSON Schema types and is built on top of the HuggingFace transformers library, making it compatible with any model supporting the HuggingFace interface.
Generating structured JSON from language models presents a significant challenge, as the generated JSON must be syntactically correct and adhere to a specific schema. Existing methods often rely on prompt engineering, fine-tuning, and post-processing but are still prone to errors and inconsistencies.
Jsonformer offers a robust solution to this issue by focusing on generating content tokens and filling in fixed tokens. This new approach involves a wrapper around HuggingFace models that fills in the fixed tokens during the generation process, leaving the language model to generate content tokens. As a result, Jsonformer is more efficient and reliable than current alternatives.
Jsonformer supports a subset of JSON Schema types, including number, boolean, string, array, and object. The bulletproof JSON generation ensures that the output is always syntactically correct and conforms to the specified schema. This approach is not only more efficient but also flexible and extendable. Jsonformer is built on top of the HuggingFace transformers library, allowing compatibility with any model that supports the HuggingFace interface.
To install Jsonformer, simply run ‘pip install jsonformer.’ The software is released under the MIT License, granting users the freedom to use, modify, and distribute the software for any purpose, commercial or non-commercial, as long as the original copyright and license notice are included. This innovative approach to generating structured JSON from language models has the potential to greatly improve the efficiency and reliability of data generation in various applications.