NuExtract-large
Maintainer: numind
102
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
NuExtract-large is a version of the Phi-3-small model, fine-tuned by NuMind on a private high-quality synthetic dataset for information extraction. It is a text-to-text model designed for extracting structured information from input text.
Compared to similar models like NuNER-v0.1 and NuNER-multilingual-v0.1, which focus on entity recognition, NuExtract-large is specialized for more general information extraction tasks. It can extract relevant information from input text based on a provided JSON template.
Model inputs and outputs
NuExtract-large is a text-to-text model, taking in input text and a JSON template as input, and generating the extracted information as output.
Inputs
- Input text: The input text can be up to 2000 tokens long. It contains the information that the model will extract from.
- JSON template: A JSON template that describes the information the user wants to extract from the input text.
- Example output: An optional example of the desired output formatting to help the model understand the task.
Outputs
- Extracted information: The model's attempt at extracting the requested information from the input text, formatted according to the provided JSON template.
Capabilities
NuExtract-large is capable of extracting structured information from input text based on a provided template. It can handle a variety of information extraction tasks, from extracting key entities and facts to summarizing longer passages of text.
The model's fine-tuning on a high-quality synthetic dataset gives it strong performance on information extraction, as evidenced by its benchmarked results. It outperforms the base Phi-3-small model on these tasks.
What can I use it for?
NuExtract-large could be useful for a variety of applications that require extracting structured information from text, such as:
- Automating data entry from documents or web pages
- Summarizing long passages of text into key facts and entities
- Powering intelligent search and question-answering systems
- Streamlining business processes by extracting relevant information
Companies could potentially monetize NuExtract-large by building applications and services that leverage its information extraction capabilities, such as NuExtract from the model's maintainer NuMind.
Things to try
One interesting thing to try with NuExtract-large is using it to extract information from longer, more complex input texts. The model's fine-tuning on a high-quality dataset suggests it may be able to handle these types of inputs well, going beyond simple entity extraction to summarize key facts and relationships.
Another idea is to experiment with providing different levels of detail in the JSON template and example output to see how it affects the model's performance. This could help refine the template and instructions to get the most accurate extractions for your specific use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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