size_categories:10K<n<100K rlfh argilla human-feedback region:us
---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for argilla_experiment_dolly_15k

This dataset has been created with [Argilla](https://docs.argilla.io).

As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Dataset Description

- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset contains:

* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.

* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.

### Load with Argilla

To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.FeedbackDataset.from_huggingface("HakanK/argilla_experiment_dolly_15k")
```

### Load with `datasets`

To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:

```python
from datasets import load_dataset

ds = load_dataset("HakanK/argilla_experiment_dolly_15k")
```

### Supported Tasks and Leaderboards

This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).

There are no leaderboards associated with this dataset.

### Languages

[More Information Needed]

## Dataset Structure

### Data in Argilla

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.

The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| category | Task category | TextField | True | False |
| instruction | Instruction | TextField | True | False |
| context | Input | TextField | False | False |
| response | Response | TextField | True | False |


The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| new-instruction | Final instruction: | TextQuestion | True | Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here. | N/A |
| new-input | Final input: | TextQuestion | False | Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty. | N/A |
| new-response | Final response: | TextQuestion | True | Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here. | N/A |


**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.

Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.

### Data Instances

An example of a dataset instance in Argilla looks as follows:

```json
{
    "external_id": "0",
    "fields": {
        "category": "closed_qa",
        "context": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.[3] It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.[4]",
        "instruction": "When did Virgin Australia start operating?",
        "response": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
    },
    "metadata": {},
    "responses": [
        {
            "status": "submitted",
            "user_id": "6c5cfab8-6136-4e83-9deb-3f5a52215706",
            "values": {
                "new-input": {
                    "value": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.[3] It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.[4]"
                },
                "new-instruction": {
                    "value": "When did Virgin Australia start operating?"
                },
                "new-response": {
                    "value": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. Blah blah"
                }
            }
        }
    ],
    "suggestions": []
}
```

While the same record in HuggingFace `datasets` looks as follows:

```json
{
    "category": "information_extraction",
    "context": "John Moses Browning (January 23, 1855 \u2013 November 26, 1926) was an American firearm designer who developed many varieties of military and civilian firearms, cartridges, and gun mechanisms \u2013 many of which are still in use around the world. He made his first firearm at age 13 in his father\u0027s gun shop and was awarded the first of his 128 firearm patents on October 7, 1879, at the age of 24. He is regarded as one of the most successful firearms designers of the 19th and 20th centuries and pioneered the development of modern repeating, semi-automatic, and automatic firearms.\n\nBrowning influenced nearly all categories of firearms design, especially the autoloading of ammunition. He invented, or made significant improvements to, single-shot, lever-action, and pump-action rifles and shotguns. He developed the first reliable and compact autoloading pistols by inventing the telescoping bolt, then integrating the bolt and barrel shroud into what is known as the pistol slide. Browning\u0027s telescoping bolt design is now found on nearly every modern semi-automatic pistol, as well as several modern fully automatic weapons. He also developed the first gas-operated firearm, the Colt\u2013Browning Model 1895 machine gun \u2013 a system that surpassed mechanical recoil operation to become the standard for most high-power self-loading firearm designs worldwide. He also made significant contributions to automatic cannon development.\n\nBrowning\u0027s most successful designs include the M1911 pistol, the water-cooled M1917, the air-cooled M1919, and heavy M2 machine guns, the M1918 Browning Automatic Rifle, and the Browning Auto-5 \u2013 the first semi-automatic shotgun. Some of these arms are still manufactured, often with only minor changes in detail and cosmetics to those assembled by Browning or his licensees. The Browning-designed M1911 and Hi-Power are some of the most copied firearms in the world.",
    "external_id": "9",
    "instruction": "Who was John Moses Browning?",
    "metadata": "{}",
    "new-input": [],
    "new-input-suggestion": null,
    "new-input-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "new-instruction": [],
    "new-instruction-suggestion": null,
    "new-instruction-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "new-response": [],
    "new-response-suggestion": null,
    "new-response-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "response": "John Moses Browning is one of the most well-known designer of modern firearms.  He started building firearms in his father\u0027s shop at the age of 13, and was awarded his first patent when he was 24.\n\nHe  designed the first reliable automatic pistol, and the first gas-operated firearm, as well inventing or improving single-shot, lever-action, and pump-action rifles and shotguns.\n\nToday, he is most well-known for the M1911 pistol, the Browning Automatic Rifle, and the Auto-5 shotgun, all of which are in still in current production in either their original design, or with minor changes.  His M1911 and Hi-Power pistols designs are some of the most reproduced firearms in the world today."
}
```

### Data Fields

Among the dataset fields, we differentiate between the following:

* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
    
    * **category** is of type `TextField`.
    * **instruction** is of type `TextField`.
    * (optional) **context** is of type `TextField`.
    * **response** is of type `TextField`.

* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
    
    * **new-instruction** is of type `TextQuestion`, and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
    * (optional) **new-input** is of type `TextQuestion`, and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
    * **new-response** is of type `TextQuestion`, and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".

* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
    
    * (optional) **new-instruction-suggestion** is of type `text`.
    * (optional) **new-input-suggestion** is of type `text`.
    * (optional) **new-response-suggestion** is of type `text`.

Additionally, we also have one more field which is optional and is the following:

* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.

### Data Splits

The dataset contains a single split, which is `train`.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation guidelines

In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.

To curate the dataset, you will need to provide an answer to the following text fields:

1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.

2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.

3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.

You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]