Model Card for Model ID

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Fine-tuned multilingual BART model for Czech Grammatical Error Correction.

Model Details

Model Description

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Uses

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More details can be found in the README.

This fine-tuned model must be used with a binary file.
The binary file can be downloaded here.

Direct Use

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Downstream Use [optional]

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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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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

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Training Details

See this README.

Training Data

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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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Metrics

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Results

This model achieved the following results for AKCES-GEC test data.

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).

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

@inproceedings{katsumata2020AACL,
    title = {Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model},
    author = {Satoru Katsumata and Mamoru Komachi},
    booktitle = {Proceedings of AACL-IJCNLP 2020}
    year = {2020},
}

APA:

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Glossary [optional]

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Model Card Authors [optional]

Satoru Katsumata

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