NuExtract-StructuredIE-v1.2.2

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A model fine-tuned for structured information extraction (IE) specifically for political elites.

Model Details

Model Description

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This is an early fine-tuned version of NuExtract 2.0-8B, which was originally a fine-tuned version of Qwen2.5-VL-7B-Instruct for structured information extraction (IE). Particularly, the target task involve joint named entity recognition (NER) and relation extraction (RE) to identify & extract information about politicla elites, their educational and professional associations, events and timeframes, and family members. The extracted information is generated in a structured JSON output. The fine-tuning process is adopted from NuMind team's procedure (numind/NuExtract-2.0-8B). Data for the fine-tuning comes from 2 sources: (1) mannual collection and (2) synthetic data generated by GPT-4.

• Developed by: Tu Eric Ngo

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• Language(s) (NLP): English

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• Finetuned from model [optional]: numind/NuExtract-2.0-8B

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Uses

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The model is fine-tuned to structured information extraction from political elite biographies in a very specific way. It follows a particular template that is very specific to the author's research project. The actual JSON schema and prompt for this fine-tuned task will be published in the future

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Out-of-Scope Use

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While the fine-tuned model may be able to perform similar structured IE tasks (especially for the simpler tasks with simpler JSON schema), the model is only trained with a specific task in mind. However, in the future, the author intends to expand the range of structured IE tasks that the model can be used for.

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Training Details

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Training Procedure

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Preprocessing [optional]

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Evaluation

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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