---
YAML tags: null
language:
- ca
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: IntruCAT
---

# Dataset Card for Instrucat

## Dataset Description
- **Homepage** [Projecte AINA](https://projecteaina.cat/tech/)
- **Repository** [HuggingFace](https://huggingface.co/projecte-aina)
- **Point of Contact:** langtech@bsc.es

### Dataset Summary

InstruCat is a dataset consisting of 216,826 instructions in Catalan.

It contains data converted to instructions format from the following datasets:

- caBreu : The instructions were created in form of summarization tasks. There are 2 types of summarization categories in the dataset: extreme and abstractive. The extreme one summarizes text into one sentence and the abstractive into shorter texts around 3-5 sentences.
- CatalanQA : The instructions correspond to questions in CatalanQA.
- CaWikiTC : The instructions were created in 2 different ways of text classification tasks with the distribution 70% - 30%. The first way is to define a category of a given text. The second way is to answer where a given text belongs to a certain category in a form of alternative question.
- ceil : The instructions were created in 2 different ways of Named Entity Recognition tasks with the distribution 70% - 30%. The first way is to list all the found Named Entities. The second way is to list only Named Entities of a particular category.  
- CoqCat : The instructions correspond to the first questions of CoqCat conversations. 
- GuiaCat : The instructions were created in form of sentiment analysis tasks.
- IntoxiCat : The instructions were created in form of binary classification tasks. The task is to define wether a given text is toxic or no.
- NLUCat : The instructions were created in form of phrase generation tasks to express a given intent.
- Parafraseja : The instructions were created in form of text generation tasks. The task is to generate a text equivalent by meaning to a given text.
- PAWS-ca : The instructions were created in form of text generation tasks. The task is to generate a text equivalent by meaning to a given text.
- sts-ca : The instructions were created in form of text generation tasks. The task is to generate a text equivalent by meaning to a given text.
- teca : The instructions were created in 2 different ways with the distribution 70% - 30%. The first way is in form of entailment generation tasks. The second way is to define whether one given text is an entailment of another given text. 
- WikiCat : The instructions were created in 2 different ways of text classification tasks with the distribution 70% - 30%. The first way is to define a category of a given text. The second way is to answer where a given text belongs to a certain category in a form of alternative question.


### Supported Tasks and Leaderboards

Training LLMs

### Languages

This dataset is in Catalan (ca-ES). 

## Dataset Structure

### Data Instances

Three JSONL files, one for each split.

An example of 'test' looks as follows:

```
{
   "ID": "Parafraseja_8977",
   "instruction": "Reescriu aquesta frase sense alterar-ne el significat:",
   "context": "Es tracta d'un tipus que ens falla ja que a ell li falla aquesta falta d'interès per tal d'exercir el domini sobre l'ambient.",
   "response": "Es tracta d'un tipus que ens falla perquè a ell li falla aquesta falta d'interès per exercir el domini sobre l'ambient.",
   "category": "paraphrasis"
}
```

### Category Distibution
| Category | Number of instructions |% |
|----------------|----------|------ |
| ner | 59410 | 27.39% |
| paraphrasis | 34695 | 16.00% |
| text_classification | 33393 | 15.40% |
| toxicity | 29809 | 13.74% |
| qa | 27427 | 12.64% |
| phrase_generation | 11873 | 5.47% |
| entailment_generation | 6354 | 2.93% |
| sentiment_analysis | 5750 | 2.65% |
| abstractive_summarization | 2999 | 1.38% |
| extreme_summarization | 2999 | 1.38% |
| entailment | 2117 | 0.97% |


### Data Splits

- train.jsonl: 165100 instructions
- validation.jsonl: 25351 instructions
- test.jsonl: 26375 instructions

## Additional Information

### Dataset Curators

Language Technologies Unit (langtech@bsc.es) at the Barcelona Supercomputing Center (BSC). 

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334 

### Licensing Information

InstruCAT contains data converted to instructions format from datasets with various licenses. 
The whole work is licensed under the most restrictive license in the corpus, which is [Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es) license.

### Citation Information

[N/A]

### Contributions

[N/A]