---
license: mit
language:
- cs
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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](https://github.com/Katsumata420/generic-pretrained-GEC/tree/master/mBART-GEC/examples/mbart#pre-trained-models))

### 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.](https://aclanthology.org/2020.aacl-main.83/)
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Since this model was trained with fairseq, fairseq must be used during inference as well.  
More details can be found in the [README](https://github.com/Katsumata420/generic-pretrained-GEC/blob/master/mBART-GEC/README.md).

This fine-tuned model must be used with a binary file.  
The binary file can be downloaded [here](https://drive.google.com/drive/folders/1oECT9q06j9r0whKmp8cqgpzvXINFutoX?usp=share_link).

### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### 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

Use the code below to get started with the model.

[More Information Needed]

## Training Details

See this [README](https://github.com/Katsumata420/generic-pretrained-GEC/blob/master/mBART-GEC/HOW_TO_REPRODUCE.md).

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

[More Information Needed]


#### 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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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

- 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]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## 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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bib
@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:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

Satoru Katsumata

## Model Card Contact

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