Text-to-Structured Data Model
A Text-to-Structured Data Model is a text-to-* model that transforms text data into a structured data format.
- Context:
- It can be applied in fields such as Data Extraction, Information Retrieval, and Machine Learning to convert textual data into a more usable format.
- It can range from generating simple key-value pairs to complex JSON objects, tables, or XML documents.
- ...
- Example(s):
- a Text-to-Code Model, ...
- a Text-to-JSON Model, ...
- Extracting patient information from clinical notes to populate a medical database.
- Converting product descriptions from e-commerce sites into structured product attributes.
- Analyzing customer reviews to generate sentiment scores and thematic categorizations.
- ...
- Counter-Example(s):
- General Language Models, which may not inherently produce structured data output.
- Text-to-Text Models, focusing on generating or translating text rather than structuring data.
- Image-to-Text Models, which convert visual inputs into textual descriptions.
- Code Generation Models, designed to produce source code rather than data structures.
- See: Data Serialization, JSON, XML, Natural Language Understanding.