re-docred-mistral-7b-autore

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Model Details

Model Description

Finetuned model for extracting entities and relationships from documents.

• Developed by: Subline
• Funded by: Brown Institute
• Model type: [More Information Needed]
• Language(s) (NLP): English
• License: MIT
• Finetuned from model: mistralai/Mistral-7B-v0.3

Model Sources [optional]

• Training Data: Re-DocRED
• Paper: @article{lilong2024autore, title={AutoRE: Document-Level Relation Extraction with Large Language Models}, author={Lilong, Xue and Dan, Zhang and Yuxiao, Dong and Jie, Tang}, journal={arXiv preprint arXiv:2403.14888}, year={2024} }

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

Re-DocRED

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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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]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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Framework versions

• PEFT 0.17.1