Datasets:
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
dataset_info:
features:
- name: text
dtype: string
id: field
- name: label
list:
- name: user_id
dtype: string
id: question
- name: value
dtype: string
id: suggestion
- name: status
dtype: string
id: question
- name: label-suggestion
dtype: string
id: suggestion
- name: label-suggestion-metadata
struct:
- name: type
dtype: string
id: suggestion-metadata
- name: score
dtype: float32
id: suggestion-metadata
- name: agent
dtype: string
id: suggestion-metadata
- name: external_id
dtype: string
id: external_id
- name: metadata
dtype: string
id: metadata
splits:
- name: train
num_bytes: 3396401
num_examples: 20000
download_size: 1323853
dataset_size: 3396401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Card for emotion
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets
library in 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 theFeedbackDataset.from_huggingface
method in Argilla.Dataset records in a format compatible with HuggingFace
datasets
. These records will be loaded automatically when usingFeedbackDataset.from_huggingface
and can be loaded independently using thedatasets
library viaload_dataset
.The 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:
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/emotion")
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:
from datasets import load_dataset
ds = load_dataset("argilla/emotion")
Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
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 |
---|---|---|---|---|
text | Text | 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 |
---|---|---|---|---|---|
label | Label | LabelQuestion | True | N/A | ['0', '1', '2', '3', '4', '5'] |
✨ 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 section.
Data Instances
An example of a dataset instance in Argilla looks as follows:
{
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {
"split": "train"
},
"responses": [
{
"status": "submitted",
"values": {
"label": {
"value": 0
}
}
}
],
"suggestions": []
}
While the same record in HuggingFace datasets
looks as follows:
{
"external_id": null,
"label": [
{
"status": "submitted",
"user_id": null,
"value": "0"
}
],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{\"split\": \"train\"}",
"text": "i didnt feel humiliated"
}
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.
- text is of type
TextField
.
- text is of type
Questions: These are the questions that will be asked to the annotators. They can be of different types, such as
RatingQuestion
,TextQuestion
,LabelQuestion
,MultiLabelQuestion
, andRankingQuestion
.- label is of type
LabelQuestion
with the following allowed values ['0', '1', '2', '3', '4', '5'].
- label is of type
✨ 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) label-suggestion is of type
label_selection
with the following allowed values ['0', '1', '2', '3', '4', '5'].
- (optional) label-suggestion is of type
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
Argilla port of dair-ai/emotion.
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]