| --- |
| 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. |
|
|
| ### 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] |
