francisco-the-man/re-docred-mistral-7b-autore

Model Card for Model ID

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

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

Re-DocRED

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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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Hardware

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Software

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Citation [optional]

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Framework versions

• PEFT 0.17.1