
# Dataset Card for Multilingual Grammar Error Correction

## Dataset Description

- **Homepage:** https://juancavallotti.com
- **Paper:** https://blog.juancavallotti.com/2023/01/06/training-a-multi-language-grammar-error-correction-system/
- **Point of Contact:** Juan Alberto López Cavallotti

### Dataset Summary

This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German. 
This dataset was developed as a component for the [Squidigies](https://squidgies.app/) platform.

### Supported Tasks and Leaderboards

* **Grammar Error Correction:** By appending the prefix *fix grammar:* to the prrompt.
* **Language Detection:** By appending the prefix: *language:* to the prompt.

### Languages

* English
* Spanish
* French
* German

## Dataset Structure

### Data Instances

The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences.

### Data Fields

* `lang`: The language of the sentence
* `sentence`: The original sentence. 
* `modified`: The corrupted sentence.
* `transformation`: The primary transformation used by the synthetic data generator.
* `sec_transformation`: The secondary transformation (if any) used by the synthetic data generator.

### Data Splits

* `train`: There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric.

## Dataset Creation

### Curation Rationale

This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet.

### Source Data

#### Initial Data Collection and Normalization

The source grammatical sentences come from various open-source datasets, such as Tatoeba.

#### Who are the source language producers?

* Juan Alberto López Cavallotti

### Annotations

#### Annotation process

The annotation is automatic and produced by the generation script.

#### Who are the annotators?

* Data generation script by Juan Alberto López Cavallotti

### Other Known Limitations

The dataset doesn't cover all the possible grammar errors but serves as a starting point that generates fair results.

## Additional Information

### Dataset Curators

* Juan Alberto López Cavallotti

### Licensing Information

This dataset is distributed under the [Apache 2 License](https://www.apache.org/licenses/LICENSE-2.0)

### Citation Information

Please mention this original dataset and the author **Juan Alberto López Cavallotti**

### Contributions

* Juan Alberto López Cavallotti
task_categories:translation size_categories:100K<n<1M language:en language:es language:fr language:de license:apache-2.0 grammar gec multi language language detection region:us
{"language": ["en", "es", "fr", "de"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["translation"], "pretty_name": "Multi Lingual Grammar Error Correction Dataset", "author": "Juan Alberto L\u00f3pez Cavallotti", "date": "Jan 6, 2023", "tags": ["grammar", "gec", "multi language", "language detection"]}
TAGS
#task_categories-translation #size_categories-100K<n<1M #language-English #language-Spanish #language-French #language-German #license-apache-2.0 #grammar #gec #multi language #language detection #region-us 


# Dataset Card for Multilingual Grammar Error Correction

## Dataset Description

- Homepage: URL
- Paper: URL
- Point of Contact: Juan Alberto López Cavallotti

### Dataset Summary

This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German. 
This dataset was developed as a component for the Squidigies platform.

### Supported Tasks and Leaderboards

* Grammar Error Correction: By appending the prefix *fix grammar:* to the prrompt.
* Language Detection: By appending the prefix: *language:* to the prompt.

### Languages

* English
* Spanish
* French
* German

## Dataset Structure

### Data Instances

The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences.

### Data Fields

* 'lang': The language of the sentence
* 'sentence': The original sentence. 
* 'modified': The corrupted sentence.
* 'transformation': The primary transformation used by the synthetic data generator.
* 'sec_transformation': The secondary transformation (if any) used by the synthetic data generator.

### Data Splits

* 'train': There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric.

## Dataset Creation

### Curation Rationale

This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet.

### Source Data

#### Initial Data Collection and Normalization

The source grammatical sentences come from various open-source datasets, such as Tatoeba.

#### Who are the source language producers?

* Juan Alberto López Cavallotti

### Annotations

#### Annotation process

The annotation is automatic and produced by the generation script.

#### Who are the annotators?

* Data generation script by Juan Alberto López Cavallotti

### Other Known Limitations

The dataset doesn't cover all the possible grammar errors but serves as a starting point that generates fair results.

## Additional Information

### Dataset Curators

* Juan Alberto López Cavallotti

### Licensing Information

This dataset is distributed under the Apache 2 License



Please mention this original dataset and the author Juan Alberto López Cavallotti

### Contributions

* Juan Alberto López Cavallotti
# Dataset Card for Multilingual Grammar Error Correction ## Dataset Description

- Homepage: URL
- Paper: URL
- Point of Contact: Juan Alberto López Cavallotti ### Dataset Summary

This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German. 
This dataset was developed as a component for the Squidigies platform. ### Supported Tasks and Leaderboards

* Grammar Error Correction: By appending the prefix *fix grammar:* to the prrompt.
* Language Detection: By appending the prefix: *language:* to the prompt. ### Languages

* English
* Spanish
* French
* German ## Dataset Structure ### Data Instances

The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences. ### Data Fields

* 'lang': The language of the sentence
* 'sentence': The original sentence. 
* 'modified': The corrupted sentence.
* 'transformation': The primary transformation used by the synthetic data generator.
* 'sec_transformation': The secondary transformation (if any) used by the synthetic data generator. ### Data Splits

* 'train': There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric. ## Dataset Creation ### Curation Rationale

This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet. ### Source Data #### Initial Data Collection and Normalization

The source grammatical sentences come from various open-source datasets, such as Tatoeba. #### Who are the source language producers?

* Juan Alberto López Cavallotti ### Annotations #### Annotation process

The annotation is automatic and produced by the generation script. #### Who are the annotators?

* Data generation script by Juan Alberto López Cavallotti ### Other Known Limitations

The dataset doesn't cover all the possible grammar errors but serves as a starting point that generates fair results. ## Additional Information ### Dataset Curators

* Juan Alberto López Cavallotti ### Licensing Information

This dataset is distributed under the Apache 2 License



Please mention this original dataset and the author Juan Alberto López Cavallotti ### Contributions

* Juan Alberto López Cavallotti
TAGS
#task_categories-translation #size_categories-100K<n<1M #language-English #language-Spanish #language-French #language-German #license-apache-2.0 #grammar #gec #multi language #language detection #region-us 
 # Dataset Card for Multilingual Grammar Error Correction ## Dataset Description

- Homepage: URL
- Paper: URL
- Point of Contact: Juan Alberto López Cavallotti ### Dataset Summary

This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German. 
This dataset was developed as a component for the Squidigies platform. ### Supported Tasks and Leaderboards

* Grammar Error Correction: By appending the prefix *fix grammar:* to the prrompt.
* Language Detection: By appending the prefix: *language:* to the prompt. ### Languages

* English
* Spanish
* French
* German ## Dataset Structure ### Data Instances

The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences. ### Data Fields

* 'lang': The language of the sentence
* 'sentence': The original sentence. 
* 'modified': The corrupted sentence.
* 'transformation': The primary transformation used by the synthetic data generator.
* 'sec_transformation': The secondary transformation (if any) used by the synthetic data generator. ### Data Splits

* 'train': There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric. ## Dataset Creation ### Curation Rationale

This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet. ### Source Data #### Initial Data Collection and Normalization

The source grammatical sentences come from various open-source datasets, such as Tatoeba. #### Who are the source language producers?

* Juan Alberto López Cavallotti ### Annotations #### Annotation process

The annotation is automatic and produced by the generation script. #### Who are the annotators?

* Data generation script by Juan Alberto López Cavallotti ### Other Known Limitations

The dataset doesn't cover all the possible grammar errors but serves as a starting point that generates fair results. ## Additional Information ### Dataset Curators

* Juan Alberto López Cavallotti ### Licensing Information

This dataset is distributed under the Apache 2 License



Please mention this original dataset and the author Juan Alberto López Cavallotti ### Contributions

* Juan Alberto López Cavallotti
passage: TAGS
#task_categories-translation #size_categories-100K<n<1M #language-English #language-Spanish #language-French #language-German #license-apache-2.0 #grammar #gec #multi language #language detection #region-us 
# Dataset Card for Multilingual Grammar Error Correction## Dataset Description

- Homepage: URL
- Paper: URL
- Point of Contact: Juan Alberto López Cavallotti### Dataset Summary

This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German. 
This dataset was developed as a component for the Squidigies platform.### Supported Tasks and Leaderboards

* Grammar Error Correction: By appending the prefix *fix grammar:* to the prrompt.
* Language Detection: By appending the prefix: *language:* to the prompt.### Languages

* English
* Spanish
* French
* German## Dataset Structure### Data Instances

The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences.### Data Fields

* 'lang': The language of the sentence
* 'sentence': The original sentence. 
* 'modified': The corrupted sentence.
* 'transformation': The primary transformation used by the synthetic data generator.
* 'sec_transformation': The secondary transformation (if any) used by the synthetic data generator.### Data Splits

* 'train': There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric.## Dataset Creation### Curation Rationale

This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet.### Source Data#### Initial Data Collection and Normalization

The source grammatical sentences come from various open-source datasets, such as Tatoeba.#### Who are the source language producers?

* Juan Alberto López Cavallotti