README.md

---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for matlab-dataset







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("lecheuklun/matlab-dataset")
```

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("lecheuklun/matlab-dataset")
```

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 |
| ---------- | ----- | ---- | -------- | -------- |
| code | Code for autocompletion | text | True | True |


### 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| span_label | Select lines of text to be removed | span | True | N/A | N/A |


<!-- check length of metadata properties -->





### Data Instances

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

```json
{
    "_server_id": "bf8e3b61-a7c2-4ac9-bb2e-18f886ac2acd",
    "fields": {
        "code": "function [paragraphs, wordCounts, maxWordCount, maxParagraph] = processTextDocument(str)\n    % Counts words in each paragraph and identifies the paragraph with the maximum number of words.\n\n    % Split the document into paragraphs\n    paragraphs = splitParagraphs(str);\n    \n    % Initialize an array to store word counts\n    wordCounts = zeros(1, length(paragraphs));\n    \n    % Loop through each paragraph to count the number of words\n    for i = 1:length(paragraphs)\n        words = strsplit(paragraphs{i});\n        wordCounts(i) = length(words);\n    end\n    \n    [maxWordCount, idx] = max(wordCounts);\n    \n    % Identify the paragraph with the maximum word count\n    if isempty(idx)\n        maxParagraph = \"\";\n        wordCounts = 0;\n        maxWordCount = 0;\n    else\n        maxParagraph = paragraphs(idx);\n    end\nend"
    },
    "id": "599f6f88-38e3-4275-b2ca-a75827a1b8ae",
    "metadata": {},
    "responses": {
        "span_label": [
            {
                "user_id": "344fe7e0-81e4-443c-a7de-a35222018436",
                "value": [
                    {
                        "end": 46,
                        "label": "REMOVE",
                        "start": 9
                    },
                    {
                        "end": 825,
                        "label": "REMOVE",
                        "start": 723
                    }
                ]
            }
        ]
    },
    "status": "completed",
    "suggestions": {},
    "vectors": {}
}
```

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

```json
{
    "_server_id": "bf8e3b61-a7c2-4ac9-bb2e-18f886ac2acd",
    "code": "function [paragraphs, wordCounts, maxWordCount, maxParagraph] = processTextDocument(str)\n    % Counts words in each paragraph and identifies the paragraph with the maximum number of words.\n\n    % Split the document into paragraphs\n    paragraphs = splitParagraphs(str);\n    \n    % Initialize an array to store word counts\n    wordCounts = zeros(1, length(paragraphs));\n    \n    % Loop through each paragraph to count the number of words\n    for i = 1:length(paragraphs)\n        words = strsplit(paragraphs{i});\n        wordCounts(i) = length(words);\n    end\n    \n    [maxWordCount, idx] = max(wordCounts);\n    \n    % Identify the paragraph with the maximum word count\n    if isempty(idx)\n        maxParagraph = \"\";\n        wordCounts = 0;\n        maxWordCount = 0;\n    else\n        maxParagraph = paragraphs(idx);\n    end\nend",
    "id": "599f6f88-38e3-4275-b2ca-a75827a1b8ae",
    "span_label.responses": [
        [
            {
                "end": 46,
                "label": "REMOVE",
                "start": 9
            },
            {
                "end": 825,
                "label": "REMOVE",
                "start": 723
            }
        ]
    ],
    "span_label.responses.status": [
        "submitted"
    ],
    "span_label.responses.users": [
        "344fe7e0-81e4-443c-a7de-a35222018436"
    ],
    "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

Highlight the lines of code you wish to remove.

#### 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]