---
license: apache-2.0
datasets:
- thebogko/bulgarian-grammar-mistakes
language:
- bg
pipeline_tag: text2text-generation
---
# mt5-base finetuned bulgarian-grammar-mistakes

<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tune checkpoint of mt5-base, fine-tuned on [bulgarian-grammar-mistakes](https://huggingface.co/thebogko/mt5-finetuned-bulgarian-grammar-mistakes) by only taking into account two of the four error types:
- article_misuse, and
- pronoun_misuse
This is done so the model can focus on these mistakes more clearly, as they are more common with native Bulgarian speakers, as opposed to the latter two types which are more common with Bulgarian learners.

## Model Details

### Model Description

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- **Model type:** Sequence2sequence Generation
- **Language(s) (NLP):** Bulgarian
- **License:** apache2.0
- **Finetuned from model:** [google/mt5-base](https://huggingface.co/google/mt5-base)

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## Uses

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Intended use of the model includes but is not limited to:
  - comparison and development of Bulgarian error correction NLP systems by developers
  - incorporation in Bulgarian language learner applications
  - research in the field of Bulgarian NLP grammar error correction

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
No need to fine-tuning further, unless needing to add more error types.

### Out-of-Scope Use

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The model should not be used to intentionally create hostile or alienating environments for people - especially Bulgarian learners. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model specialises in identifying and correcting Bulgarian grammar errors that have to do with article and pronoun misuse, so it will likely not perform well on other types of errors. Additionally, the dataset used for fine-tuning does not encompass all possible errors of those types, so use should be with caution of grammatical validity of output.

### Recommendations

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Users are strongly advised to double check the validity of the model's outputs and to strive to understand the underlying grammatical rules behind the language, instead of using the model's outputs as given.

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thebogko/mt5-finetuned-bulgarian-grammar-mistakes")
model = AutoModelForSeq2SeqLM.from_pretrained("thebogko/mt5-finetuned-bulgarian-grammar-mistakes")


erroneous_sentence = 'Владетеля умря още млад.'
encoded_source = tokenizer(erroneous_sentence,
                           return_tensors='pt',
                           max_length=100,
                           padding='max_length')
encoded_source.to(device)

correct_sentence = ft_model.generate(**encoded_source, max_length=100)
correct_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in correct_sentence][0]
print(correct_sentence)

"Владетелят умря още млад."
```

## Training Details

### 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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### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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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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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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