size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:argilla region:us rlfh argilla human-feedback
---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for argilla_test2







This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).


## Using this dataset 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.Dataset.from_hub("pavan01729/argilla_test2")
```

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

## Using this dataset with `datasets`

To load the records of 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("pavan01729/argilla_test2")
```

This will only load the records of the dataset, but not the Argilla settings.

## Dataset Structure

This dataset repo contains:

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` 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.
* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.

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

### Fields

The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| instruction | Instruction | text | True | False |
| input | Input Text | text | True | False |


### Questions

The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| output | Provide the output based on the instruction and input | text | True | N/A | N/A |


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### Data Instances

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

```json
{
    "_server_id": "ee86825d-20e7-4c63-8ab7-38c0685d0468",
    "fields": {
        "input": "Quantum computers are being studied for their ability to solve problems that is impossible for classical computers.",
        "instruction": "Correct the grammar in the following sentence related to quantum computing."
    },
    "id": "6ee484c7-6b31-48a0-aeb7-5f7bd3ae8854",
    "metadata": {},
    "responses": {
        "output": [
            {
                "user_id": "480b12e0-edab-4a79-bd99-ff813b022e6a",
                "value": "quantum computing"
            }
        ]
    },
    "status": "completed",
    "suggestions": {},
    "vectors": {}
}
```

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

```json
{
    "_server_id": "ee86825d-20e7-4c63-8ab7-38c0685d0468",
    "id": "6ee484c7-6b31-48a0-aeb7-5f7bd3ae8854",
    "input": "Quantum computers are being studied for their ability to solve problems that is impossible for classical computers.",
    "instruction": "Correct the grammar in the following sentence related to quantum computing.",
    "output.responses": [
        "quantum computing"
    ],
    "output.responses.status": [
        "submitted"
    ],
    "output.responses.users": [
        "480b12e0-edab-4a79-bd99-ff813b022e6a"
    ],
    "status": "completed"
}
```


### 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

Annotate the outputs for the given instructions and inputs.

#### 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]