task_categories:text-generation size_categories:10K<n<100K language:en license:cc-by-nc-4.0 instruction-finetuning region:us
---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: Tapir-Cleaned
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
# Dataset Card for Tapir-Cleaned

This is a revised version of the DAISLab dataset of IFTTT rules, which has been thoroughly cleaned, scored, and adjusted for the purpose of instruction-tuning.

## Tapir Dataset Summary

Tapir is a subset of the larger DAISLab dataset, which comprises 242,480 recipes extracted from the IFTTT platform. 

After a thorough cleaning process that involved the removal of redundant and inconsistent recipes, the refined dataset was condensed to include 67,697 high-quality recipes. 

This curated set of instruction data is particularly useful for conducting instruction-tuning exercises for language models, 
allowing them to more accurately follow instructions and achieve superior performance.

The last version of Tapir includes a correlation score that helps to identify the most appropriate description-rule pairs for instruction tuning. 
Description-rule pairs with a score greater than 0.75 are deemed good enough and are prioritized for further analysis and tuning.

### Supported Tasks and Leaderboards

The Tapir dataset designed for instruction training pretrained language models

### Languages

The data in Tapir are mainly in English (BCP-47 en).

# Dataset Structure

### Data Instances

```json
{
    "instruction":"From the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.",
    "input":"If it's raining outside, you'll want some nice warm colors inside!",
    "output":"IF Weather Underground Current condition changes to THEN LIFX Change color of lights",
    "score":"0.788197",
    "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nFrom the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.\n\n### Input:\nIf it's raining outside, you'll want some nice warm colors inside!\n\n### Response:\nIF Weather Underground Current condition changes to THEN LIFX Change color of lights",
}
```

### Data Fields

The data fields are as follows:

* `instruction`: describes the task the model should perform.
* `input`: context or input for the task.  Each of the 67K input is unique.
* `output`: the answer taken from the original Tapir Dataset formatted as an IFTTT recipe.
* `score`: the correlation score obtained via BertForNextSentencePrediction 
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors of Alpaca for fine-tuning their models.

### Data Splits

|               | train |
|---------------|------:|
| tapir         | 67697 |

### Licensing Information

The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).


### Citation Information

```
@misc{tapir,
  author = {Mattia Limone, Gaetano Cimino, Annunziata Elefante},
  title = {TAPIR: Trigger Action Platform for Information Retrieval},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/MattiaLimone/ifttt_recommendation_system}},
}
```