mBART-cz-GEC

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

Model Details

Model Description

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• Developed by: Satoru Katsumata
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): Czech
• License: MIT License
• Finetuned from model [optional]: Fairseq multilingual BART-large (mbart.CC25)

Model Sources [optional]

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• Repository: https://github.com/Katsumata420/generic-pretrained-GEC
• Paper [optional]: Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model.
• Demo [optional]: [More Information Needed]

Uses

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Since this model was trained with fairseq, fairseq must be used during inference as well.
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

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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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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• m2scorer
  • https://www.comp.nus.edu.sg/~nlp/conll14st.html
  • metrics
    • Precision
    • Recall
    • F0.5

Results

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

• Precision: 75.75
• Recall: 61.41
• F0.5: 72.37

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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• Hours used: [More Information Needed]
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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

Model Card Contact

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