---
language:
- ca
multilinguality:
- monolingual
pretty_name: synthetic_meteocat
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- token-classification
- question-answering
task_ids:
- named-entity-recognition
---

# Dataset Card for Meteocat

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Point of Contact:** [langtech@bsc.es](langtech@bsc.es)

### Dataset Summary

This is a synthetic dataset that contains examples, each of them, with the following fields:
- Instructions like "El dissabte a la nit, quin temps farà a Mont-real?"
- Context like "Day: dissabte | Location: Mont-real | mati: el cel estarà molt ennuvolat | tarda: plourà escadusserament | nit: el cel tendirà a estar cobert de núvols | temp: Lleugera pujada de les temperatures"
- Response like "A la nit el cel estarà ennuvolat"

Added instructions for answering "yes" or "no" questions.

### Supported Tasks and Leaderboards

This dataset is mainly intended to train models for text-generation and named-entity-recognition.

### Languages

The dataset is in Catalan (`ca-CA`).

## Dataset Structure

The dataset consists of examples in a jsonl format with 3 fields each: instruction, context and response.

### Data Instances
Changed origina context for a more linguistically natural one: "tarda del divendres a Montesquiu al mati s'esperen més nuvolades, a la tarda guspirejarà amb insistència, a la nit podria guspirejar, i Temperatures sense canvis"
{
  "instruction": "Quin temps farà a la nit a Camarasa dijous?", 
  xxx "context": "Day: dijous | Location: Camarasa | mati: el cel anirà encapotant-se cada cop més | tarda: el sol anirà guanyant terreny als núvols | nit: cel clar | temp: Temperatures sense canvis", 
  "response": "A la nit, cel ben clar"
}

### Data Fields
- instruction: Weather-related question.
xxx - context: Information in the format "Day: [DAY] | Location: [LOCATION] | mati: [WEATHER FORECAST] | tarda: [WEATHER FORECAST] | nit: [WEATHER FORECAST]".
 
- response: Whether forecast answering the question.

### Data Splits

* dev.json: 6873 examples
* test.json: 1279 examples
* train.json: 61776 examples


## Additional Information

### Dataset Curators

Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)


This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).

### Licensing Information

???? [Creative Commons Attribution Non-commercial No-Derivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/).

### Contributions

[N/A]