size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
Dataset Card for test_spans_dataset
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_huggingfacemethod in Argilla.Dataset records in a format compatible with HuggingFace
datasets. These records will be loaded automatically when usingFeedbackDataset.from_huggingfaceand can be loaded independently using thedatasetslibrary 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("nataliaElv/test_spans_dataset")
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("nataliaElv/test_spans_dataset")
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, metadata, vectors, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
|---|---|---|---|---|
| prompt | Prompt-(Ents) | text | True | False |
| input | Input-(Ents) | text | True | False |
| input2 | Input-(Info Extraction) | text | True | False |
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 |
|---|---|---|---|---|---|
| prompt-ents | Highlight the entities inside Prompt-(Ents): | span | True | N/A | N/A |
| input-ents | Highlight the entities inside Input-(Ents): | span | True | N/A | N/A |
| info-extraction | Highlight the information inside Input-(Info Extraction) that is relevant to the prompt | span | True | N/A | N/A |
| final-response | Provide a correct response given the prompt and the input: | text | True | Only make the necessary corrections. You can submit the text as it is, if it's correct. | N/A |
The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and 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, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the metadata_properties defined in the dataset configuration file in argilla.yaml.
| Metadata Name | Title | Type | Values | Visible for Annotators |
|---|
The guidelines, are optional as well, and 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:
{
"external_id": null,
"fields": {
"input": "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. 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.",
"input2": "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. 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.",
"prompt": "When did Virgin Australia start operating?"
},
"metadata": {},
"responses": [],
"suggestions": [
{
"agent": null,
"question_name": "prompt-ents",
"score": null,
"type": null,
"value": [
{
"end": 25,
"label": "ORG",
"score": 0.9999854564666748,
"start": 9
}
]
},
{
"agent": null,
"question_name": "input-ents",
"score": null,
"type": null,
"value": [
{
"end": 16,
"label": "ORG",
"score": 0.9998990297317505,
"start": 0
},
{
"end": 71,
"label": "ORG",
"score": 0.9999301433563232,
"start": 38
},
{
"end": 162,
"label": "ORG",
"score": 0.9961417317390442,
"start": 156
},
{
"end": 224,
"label": "ORG",
"score": 0.9999250173568726,
"start": 213
},
{
"end": 319,
"label": "LOC",
"score": 0.9998377561569214,
"start": 310
},
{
"end": 376,
"label": "ORG",
"score": 0.9999576807022095,
"start": 360
},
{
"end": 464,
"label": "LOC",
"score": 0.9998786449432373,
"start": 455
},
{
"end": 487,
"label": "LOC",
"score": 0.9998598098754883,
"start": 479
},
{
"end": 498,
"label": "LOC",
"score": 0.9997498393058777,
"start": 489
},
{
"end": 509,
"label": "LOC",
"score": 0.9998868703842163,
"start": 503
}
]
},
{
"agent": null,
"question_name": "final-response",
"score": null,
"type": null,
"value": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
}
],
"vectors": {}
}
While the same record in HuggingFace datasets looks as follows:
{
"external_id": null,
"final-response": [],
"final-response-suggestion": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.",
"final-response-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"info-extraction": [],
"info-extraction-suggestion": null,
"info-extraction-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"input": "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. 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.",
"input-ents": [],
"input-ents-suggestion": {
"end": [
16,
71,
162,
224,
319,
376,
464,
487,
498,
509
],
"label": [
"ORG",
"ORG",
"ORG",
"ORG",
"LOC",
"ORG",
"LOC",
"LOC",
"LOC",
"LOC"
],
"score": [
0.9998990297317505,
0.9999301433563232,
0.9961417317390442,
0.9999250173568726,
0.9998377561569214,
0.9999576807022095,
0.9998786449432373,
0.9998598098754883,
0.9997498393058777,
0.9998868703842163
],
"start": [
0,
38,
156,
213,
310,
360,
455,
479,
489,
503
],
"text": [
"Virgin Australia",
"Virgin Australia Airlines Pty Ltd",
"Virgin",
"Virgin Blue",
"Australia",
"Ansett Australia",
"Australia",
"Brisbane",
"Melbourne",
"Sydney"
]
},
"input-ents-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"input2": "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. 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.",
"metadata": "{}",
"prompt": "When did Virgin Australia start operating?",
"prompt-ents": [],
"prompt-ents-suggestion": {
"end": [
25
],
"label": [
"ORG"
],
"score": [
0.9999854564666748
],
"start": [
9
],
"text": [
"Virgin Australia"
]
},
"prompt-ents-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
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 supported. These are the ones that will be used to provide responses to the questions.
- prompt is of type
text. - input is of type
text. - input2 is of type
text.
- prompt 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.- prompt-ents is of type
span. - input-ents is of type
span. - info-extraction is of type
span. - final-response is of type
text, and description "Only make the necessary corrections. You can submit the text as it is, if it's correct.".
- prompt-ents is of type
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) prompt-ents-suggestion is of type
span. - (optional) input-ents-suggestion is of type
span. - (optional) info-extraction-suggestion is of type
span. - (optional) final-response-suggestion is of type
text.
- (optional) prompt-ents-suggestion is of type
Additionally, we also have two more fields that are optional and are the following:
- metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the
metadata_propertiesdefined in the dataset configuration file inargilla.yaml. - 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
This is a subset of the Dolly dataset with prompts classified as being Closed QA or Information Extractions tasks.
In the record, you will find the prompt and the input of the task. In the first two fields, you will need to highlight and classify all entities found in the prompt and the input. These are marked as (Ents) for easier recognition.
The input field is then repeated as "Input-(Info Extraction)". Using the "Relevant Info" tag, highlight all pieces of information in the input that are relevant to answer the prompt.
Finally, you will be asked to provide a correct response following the prompt and the given input. You may submit the text as it is, if it's correct, or make any necessary amendments.
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]